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Journal articles on the topic 'Metaheuristic'
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Kusuma, Purba Daru, and Ashri Dinimaharawati. "TREBLE SEARCH OPTIMIZER: A STOCHASTIC OPTIMIZATION TO OVERCOME BOTH UNIMODAL AND MULTIMODAL PROBLEMS." IIUM Engineering Journal 24, no. 2 (July 4, 2023): 86–99. http://dx.doi.org/10.31436/iiumej.v24i2.2700.
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Today, many metaheuristics have used metaphors as their inspiration and baseline for novelty. It makes the novel strategy of these metaheuristics difficult to investigate. Moreover, many metaheuristics use high iteration or swarm size in their first introduction. Based on this consideration, this work proposes a new metaheuristic free from metaphor. This metaheuristic is called treble search optimizer (TSO), representing its main concept in performing three searches performed by each member in each iteration. These three searches consist of two directed searches and one random search. Several seeds are generated from each search. Then, these searches are compared with each other to find the best seed that might substitute the current corresponding member. TSO is also designed to overcome the optimization problem in the low iteration or swarm size circumstance. In this paper, TSO is challenged to overcome the 23 classic optimization functions. In this experiment, TSO is compared with five shortcoming metaheuristics: slime mould algorithm (SMA), hybrid pelican komodo algorithm (HPKA), mixed leader-based optimizer (MLBO), golden search optimizer (GSO), and total interaction algorithm (TIA). The result shows that TSO performs effectively and outperforms these five metaheuristics by making better fitness scores than SMA, HPKA, MLBO, GSO, and TIA in overcoming 21, 21, 23, 23, and 17 functions, consecutively. The result also indicates that TSO performs effectively in overcoming unimodal and multimodal problems in the low iteration and swarm size. ABSTRAK: Dewasa ini, terdapat ramai metaheuristik menggunakan metafora sebagai inspirasi dan garis dasar pembaharuan. Ini menyebabkan strategi baharu metaheuristik ini susah untuk dikaji. Tambahan, ramai metaheuristik menggunakan ulangan berulang atau saiz kerumunan dalam pengenalan mereka. Berdasarkan penilaian ini, kajian ini mencadangkan metaheuristk baharu bebas metafora. Metaheuristik ini dipanggil pengoptimum pencarian ganda tiga (TSO), mewakilkan konsep utama dalam pemilihan tiga pencarian yang dilakukan oleh setiap ahli dalam setiap ulangan. Ketiga-tiga carian ini terdiri daripada dua pencarian terarah dan satu pencarian rawak. Beberapa benih dihasilkan dalam setiap carian. Kemudian, carian ini dibandingkan antara satu sama lain bagi mencari benih terbaik yang mungkin berpotensi menggantikan ahli yang sedang digunakan. TSO juga direka bagi mengatasi masalah pengoptimuman dalam ulangan rendah atau lingkungan saiz kerumunan. Kajian ini TSO dicabar bagi mengatasi 23 fungsi pengoptimuman klasik. Eksperimen ini TSO dibandingkan dengan lima kekurangan metaheuristik: algoritma acuan lendir (SMA), algorithma hibrid komodo burung undan (HPKA), Pengoptimum Campuran berdasarkan-Ketua (MLBO), Pengoptimuman Carian Emas (GSO), dan algoritma jumlah interaksi (TIA). Dapatan kajian menunjukkan TSO berkesan menghasilkan dan lebih baik daripada kelima-lima metaheuristik dengan menghasilkan pemarkahan padanan terbaik berbanding SMA, HPKA, MLBO, GSO, dan TIA dalam mengatasi fungsi 21, 21, 23, 23, dan 17, secara berurutan. Dapatan kajian juga menunjukkan TSO turut berperanan efektif dalam mengatasi masalah modal tunggal dan modal ganda dalam iterasi rendah dan saiz kerumunan.
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LEE, YOUNG CHOON, JAVID TAHERI, and ALBERT Y. ZOMAYA. "A PARALLEL METAHEURISTIC FRAMEWORK BASED ON HARMONY SEARCH FOR SCHEDULING IN DISTRIBUTED COMPUTING SYSTEMS." International Journal of Foundations of Computer Science 23, no. 02 (February 2012): 445–64. http://dx.doi.org/10.1142/s0129054112400229.
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A large number of optimization problems have been identified as computationally challenging and/or intractable to solve within a reasonable amount of time. Due to the NP-hard nature of these problems, in practice, heuristics account for the majority of existing algorithms. Metaheuristics are one very popular type of heuristics used for many of these optimization problems. In this paper, we present a novel parallel-metaheuristic framework, which effectively enables to devise parallel metaheuristics, particularly with heterogeneous metaheuristics. The core component of the proposed framework is its harmony-search-based coordinator. Harmony search is a recent breed of metaheuristic that mimics the improvisation process of musicians. The coordinator facilitates heterogeneous metaheuristics (forming a parallel metaheuristic) to escape local optima. Specifically, best solutions generated by these worker metaheuristics are maintained in the harmony memory of the coordinator, and they are used to form new-possibly better-harmonies (solutions) before actual solution sharing between workers occurs; hence, their solutions are harmonized with each other. For the applicability validation and the performance evaluation, we have implemented a parallel hybrid metaheuristic using the framework for the task scheduling problem on multiprocessor computing systems (e.g., computer clusters). Experimental results verify that the proposed framework is a compelling approach to parallelize heterogeneous metaheuristics.
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Feitosa Neto, Antonino, Anne Canuto, and João Xavier-Junior. "Hybrid Metaheuristics to the Automatic Selection of Features and Members of Classifier Ensembles." Information 9, no. 11 (October 26, 2018): 268. http://dx.doi.org/10.3390/info9110268.
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Metaheuristic algorithms have been applied to a wide range of global optimization problems. Basically, these techniques can be applied to problems in which a good solution must be found, providing imperfect or incomplete knowledge about the optimal solution. However, the concept of combining metaheuristics in an efficient way has emerged recently, in a field called hybridization of metaheuristics or, simply, hybrid metaheuristics. As a result of this, hybrid metaheuristics can be successfully applied in different optimization problems. In this paper, two hybrid metaheuristics, MAMH (Multiagent Metaheuristic Hybridization) and MAGMA (Multiagent Metaheuristic Architecture), are adapted to be applied in the automatic design of ensemble systems, in both mono- and multi-objective versions. To validate the feasibility of these hybrid techniques, we conducted an empirical investigation, performing a comparative analysis between them and traditional metaheuristics as well as existing existing ensemble generation methods. Our findings demonstrate a competitive performance of both techniques, in which a hybrid technique provided the lowest error rate for most of the analyzed objective functions.
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Chicco, Gianfranco, and Andrea Mazza. "Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the ‘Rush to Heuristics’." Energies 13, no. 19 (September 30, 2020): 5097. http://dx.doi.org/10.3390/en13195097.
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In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems.
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Bouhmala, N. "A Variable Depth Search Algorithm for Binary Constraint Satisfaction Problems." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/637809.
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The constraint satisfaction problem (CSP) is a popular used paradigm to model a wide spectrum of optimization problems in artificial intelligence. This paper presents a fast metaheuristic for solving binary constraint satisfaction problems. The method can be classified as a variable depth search metaheuristic combining a greedy local search using a self-adaptive weighting strategy on the constraint weights. Several metaheuristics have been developed in the past using various penalty weight mechanisms on the constraints. What distinguishes the proposed metaheuristic from those developed in the past is the update ofkvariables during each iteration when moving from one assignment of values to another. The benchmark is based on hard random constraint satisfaction problems enjoying several features that make them of a great theoretical and practical interest. The results show that the proposed metaheuristic is capable of solving hard unsolved problems that still remain a challenge for both complete and incomplete methods. In addition, the proposed metaheuristic is remarkably faster than all existing solvers when tested on previously solved instances. Finally, its distinctive feature contrary to other metaheuristics is the absence of parameter tuning making it highly suitable in practical scenarios.
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Zhang, Le, and Jinnan Wu. "A PSO-Based Hybrid Metaheuristic for Permutation Flowshop Scheduling Problems." Scientific World Journal 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/902950.
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This paper investigates the permutation flowshop scheduling problem (PFSP) with the objectives of minimizing the makespan and the total flowtime and proposes a hybrid metaheuristic based on the particle swarm optimization (PSO). To enhance the exploration ability of the hybrid metaheuristic, a simulated annealing hybrid with a stochastic variable neighborhood search is incorporated. To improve the search diversification of the hybrid metaheuristic, a solution replacement strategy based on the pathrelinking is presented to replace the particles that have been trapped in local optimum. Computational results on benchmark instances show that the proposed PSO-based hybrid metaheuristic is competitive with other powerful metaheuristics in the literature.
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Crawford, Broderick, Ricardo Soto, José Lemus-Romani, Marcelo Becerra-Rozas, José M. Lanza-Gutiérrez, Nuria Caballé, Mauricio Castillo, et al. "Q-Learnheuristics: Towards Data-Driven Balanced Metaheuristics." Mathematics 9, no. 16 (August 4, 2021): 1839. http://dx.doi.org/10.3390/math9161839.
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One of the central issues that must be resolved for a metaheuristic optimization process to work well is the dilemma of the balance between exploration and exploitation. The metaheuristics (MH) that achieved this balance can be called balanced MH, where a Q-Learning (QL) integration framework was proposed for the selection of metaheuristic operators conducive to this balance, particularly the selection of binarization schemes when a continuous metaheuristic solves binary combinatorial problems. In this work the use of this framework is extended to other recent metaheuristics, demonstrating that the integration of QL in the selection of operators improves the exploration-exploitation balance. Specifically, the Whale Optimization Algorithm and the Sine-Cosine Algorithm are tested by solving the Set Covering Problem, showing statistical improvements in this balance and in the quality of the solutions.
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Omran, Mahamed G., and Andries Engelbrecht. "Time Complexity of Population-Based Metaheuristics." MENDEL 29, no. 2 (December 20, 2023): 255–60. http://dx.doi.org/10.13164/mendel.2023.2.255.
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This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then used to empirically analyze the computational cost of the three aforementioned metaheuristics. The pros and cons of the two approaches, i.e. mathematical and empirical analysis, are discussed.
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Rahman, Md Ashikur, Rajalingam Sokkalingam, Mahmod Othman, Kallol Biswas, Lazim Abdullah, and Evizal Abdul Kadir. "Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances." Mathematics 9, no. 20 (October 19, 2021): 2633. http://dx.doi.org/10.3390/math9202633.
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Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of metaheuristics in combinatorial optimization. From the authors’ point of view, there is still a lack of comprehensive surveys on current research directions. Therefore, a substantial part of this paper is devoted to analyzing and discussing the modern age metaheuristic algorithms that gained popular use in mostly cited combinatorial optimization problems such as vehicle routing problems, traveling salesman problems, and supply chain network design problems. A survey of seven different metaheuristic algorithms (which are proposed after 2000) for combinatorial optimization problems is carried out in this study, apart from conventional metaheuristics like simulated annealing, particle swarm optimization, and tabu search. These metaheuristics have been filtered through some key factors like easy parameter handling, the scope of hybridization as well as performance efficiency. In this study, a concise description of the framework of the selected algorithm is included. Finally, a technical analysis of the recent trends of implementation is discussed, along with the impacts of algorithm modification on performance, constraint handling strategy, the handling of multi-objective situations using hybridization, and future research opportunities.
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Misevičius, Alfonsas, Vytautas Bukšnaitis, and Jonas Blonskis. "Kombinatorinis optmizavimas ir metaeuristiniai metodai: teoriniai aspektai." Informacijos mokslai 42, no. 43 (January 1, 2008): 213–19. http://dx.doi.org/10.15388/im.2008.0.3417.
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Straipsnyje aptariami kombinatorinio optimizavimo ir intelektualių optimizavimo priemonių, t. y. metaeuristinių metodų (metaeuristikų), teoriniai aspektai. Apibūdinami kombinatorinio optimizavimo uždaviniai, jų savybės, specifika. Pagrindinis dėmesys skiriamas metaeuristinių optimizavimo metodų charakterizavimui būtent kombinatorinio optimizavimo kontekste. Trumpai formuluojami metaeuristinių metodų tikslai, bendrosios nuostatos, taip pat akcentuojamas šių metodų savitumas, modernumas.Išsamiau apžvelgiami skiriamieji metaeuristikų bruožai, aprašomos svarbesnės teorinės metaeuristinių metodų aiškinimo kryptys. Pabaigoje pateikiamos apibendrinamosios pastabos.Combinatorial optimization and metaheuristic methods: theoretical aspectsAlfonsas Misevičius, Vytautas Bukšnaitis, Jonas Blonskis SummaryIn this paper, theoretical aspects of combinatorial optimization (CO) and intelligent optimization techniques, i. e. metaheuristic methods (metaheuristics) are discussed. The combinatorial optimization problems and their basic properties are shortly introduced. Much of our attention is paid to the characterization of the metaheuristic methods, in particular for solving CO problems. We formulate the main goals of the metaheuristic methods, also focusing on the special theoretical issues and features of these methods. The most important interpretations of the metaheuristic methods are described in more details. The paper is completed with the concluding remarks.
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Zambrano-Gutierrez, Daniel F., Jorge M. Cruz-Duarte, Herman Castañeda, and Juan Gabriel Avina-Cervantes. "Optimization of Adaptive Sliding Mode Controllers Using Customized Metaheuristics in DC-DC Buck-Boost Converters." Mathematics 12, no. 23 (November 26, 2024): 3709. http://dx.doi.org/10.3390/math12233709.
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Metaheuristics have become popular tools for solving complex optimization problems; however, the overwhelming number of tools and the fact that many are based on metaphors rather than mathematical foundations make it difficult to choose and apply them to real engineering problems. This paper addresses this challenge by automatically designing optimization algorithms using hyper-heuristics as a master tool. Hyper-heuristics produce customized metaheuristics by combining simple heuristics, guiding a population of initially random individuals to a solution that satisfies the design criteria. As a case study, the obtained metaheuristic tunes an Adaptive Sliding Mode Controller to improve the dynamic response of a DC-DC Buck–Boost converter under various operating conditions (such as overshoot and settling time), including nonlinear disturbances. Specifically, our hyper-heuristic obtained a tailored metaheuristic composed of Genetic Crossover- and Swarm Dynamics-type operators. The goal is to build the metaheuristic solver that best fits the problem and thus find the control parameters that satisfy a predefined performance. The numerical results reveal the reliability and potential of the proposed methodology in finding suitable solutions for power converter control design. The system overshoot was reduced from 87.78% to 0.98%, and the settling time was reduced from 31.90 ms to 0.4508 ms. Furthermore, statistical analyses support our conclusions by comparing the custom metaheuristic with recognized methods such as MadDE, L-SHADE, and emerging metaheuristics. The results highlight the generated optimizer’s competitiveness, evidencing the potential of Automated Algorithm Design to develop high-performance solutions without manual intervention.
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Bourennani, Farid, Shahryar Rahnamayan, and Greg F. Naterer. "OGDE3: Opposition-Based Third Generalized Differential Evolution." Journal of Advanced Computational Intelligence and Intelligent Informatics 16, no. 3 (May 20, 2012): 469–80. http://dx.doi.org/10.20965/jaciii.2012.p0469.
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Multi-Objective Optimization (MOO) metaheuristics are commonly used for solving complex MOO problems characterized by non-convexity, multimodality, mixed-types variables, non-linearity, and other complexities. However, often metaheuristics suffer from slow convergence. Opposition-Based Learning (OBL) has been successfully used in the past for acceleration of single-objective metaheuristics. The most successful example in this regard is Opposition-based Differential Evolution (ODE). However, OBL was not fully explored for MOO metaheuristics. Therefore, in this paper, to the best of our knowledge, for the first time OBL is successfully adapted for a MOO metaheuristic by using a single population (no coevolution). The proposed MOO metaheuristic is based on the GDE3 method and it is called Opposition-based GDE3 (OGDE3). OGDE3 utilizes OBL for opposition-based population initialization and self-adaptive oppositionbased generating jumping. Furthermore, the new algorithm is compared with seven state-of-the-artMOO metaheuristics using the ZDT test suite. OGDE3 outperformed the other algorithms; the results are explained and discussed in detail.
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García González, Enol, JOSÉ RAMÓN VILLAR FLECHA, JAVIER SEDANO FRANCO, and CAMELIA CHIRA. "BENCHMARKING ANALYSIS FOR BIOLOGICAL-BASED METAHEURISTICS." DYNA 99, no. 3 (May 1, 2024): 296–302. http://dx.doi.org/10.6036/11070.
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This article reviews the metaheuristics published in the literature, emphasizing their usefulness in solving complex optimization problems. The review highlights inspiration's relevance in the metaheuristics design, being the main classification in multiple taxonomies existing in the literature. After reviewing the state of the art, six of the most relevant metaheuristics used to solve problems of various types (engineering, logistics, economics, data science, ...) were selected. This selection of metaheuristics will be subjected to an analysis of their performance using a set of problems selected from different authors. The problems selected for this analysis include problems with a single minimum or multiple minima, different sizes in terms of dimensions, and different types of mathematical functions such as polynomial, trigonometric, or exponential. The analysis offers a discussion of which scenarios are the best for each metaheuristic, analyzing aspects such as the ability of metaheuristics to explore and escape local minima. The article concludes by summarizing which metaheuristic is best for each type of problem. Keywords: Metaheuristics, benchmark, optimization problems, biological-based metaheuristics
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Cruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Iván Amaya, Yong Shi, Hugo Terashima-Marín, and Nelishia Pillay. "Towards a Generalised Metaheuristic Model for Continuous Optimisation Problems." Mathematics 8, no. 11 (November 17, 2020): 2046. http://dx.doi.org/10.3390/math8112046.
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Metaheuristics have become a widely used approach for solving a variety of practical problems. The literature is full of diverse metaheuristics based on outstanding ideas and with proven excellent capabilities. Nonetheless, oftentimes metaheuristics claim novelty when they are just recombining elements from other methods. Hence, the need for a standard metaheuristic model is vital to stop the current frenetic tendency of proposing methods chiefly based on their inspirational source. This work introduces a first step to a generalised and mathematically formal metaheuristic model, which can be used for studying and improving them. This model is based on a scheme of simple heuristics, which perform as building blocks that can be modified depending on the application. For this purpose, we define and detail all components and concepts of a metaheuristic (i.e., its search operators), such as heuristics. Furthermore, we also provide some ideas to take into account for exploring other search operator configurations in the future. To illustrate the proposed model, we analyse search operators from four well-known metaheuristics employed in continuous optimisation problems as a proof-of-concept. From them, we derive 20 different approaches and use them for solving some benchmark functions with different landscapes. Data show the remarkable capability of our methodology for building metaheuristics and detecting which operator to choose depending on the problem to solve. Moreover, we outline and discuss several future extensions of this model to various problem and solver domains.
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Purnomo, Hindriyanto, Tad Gonsalves, Evangs Mailoa, Fian Julio Santoso, and Muhammad Rizky Pribadi. "Metaheuristics Approach for Hyperparameter Tuning of Convolutional Neural Network." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 8, no. 3 (June 1, 2024): 340–45. http://dx.doi.org/10.29207/resti.v8i3.5730.
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Deep learning is an artificial intelligence technique that has been used for various tasks. Deep learning performance is determined by its hyperparameter, architecture, and training (connection weight and bias). Finding the right combination of these aspects is very challenging. Convolution neural networks (CNN) is a deep learning method that is commonly used for image classification. It has many hyperparameters; therefore, tuning its hyperparameter is difficult. In this research, a metaheuristic approach is proposed to optimize the hyperparameter of convolution neural networks. Three metaheuristic methods are used in this research: ant colony optimization (ACO), genetic algorithm (GA), and Harmony Search (HS). The metaheuristics methods are used to find the best combination of 8 hyperparameters with 8 options each which creates 1.6. 107 of solution space. The solution space is too large to explore using manual tuning. The Metaheuristics method will bring benefits in terms of finding solutions in the search space more effectively and efficiently. The performance of the metaheuristic methods is evaluated using MNIST datasets. The experiment results show that the accuracy of ACO, GA and HS are 99,7%, 97.7% and 89,9% respectively. The computational times for the ACO, GA and HS algorithms are 27.9 s, 22.3 s, and 56.4 s, respectively. It shows that ACO performs the best among the three algorithms in terms of accuracy, however, its computational time is slightly longer than GA. The results of the experiment reveal that the metaheuristic approach is promising for the hyperparameter tuning of CNN. Future research can be directed toward solving larger problems or improving the metaheuristics operator to improve its performance.
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Bajenaru, Victor, Steven Lavoie, Brett Benyo, Christopher Riker, Mitchell Colby, and James Vaccaro. "Recommender System Metaheuristic for Optimizing Decision-Making Computation." Electronics 12, no. 12 (June 14, 2023): 2661. http://dx.doi.org/10.3390/electronics12122661.
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We implement a novel recommender system (RS) metaheuristic framework within a nonlinear NP-hard decision-making problem, for reducing the solution search space before high-burden computational steps are performed. Our RS-based metaheuristic supports consideration of comprehensive evaluation criteria, including estimations of the potential solution set’s optimality, diversity, and feedback/preference of the end-user, while also being fully compatible with additional established RS evaluation metrics. Compared to prior Operations Research metaheuristics, our RS-based metaheuristic allows for (1) achieving near-optimal solution scores through comprehensive deep learning training, (2) fast metaheuristic parameter inference during solution instantiation trials, and (3) the ability to reuse this trained RS module for traditional RS ranking of final solution options for the end-user. When implementing this RS metaheuristic within an experimental high-dimensionality simulation environment, we see an average 91.7% reduction in computation time against a baseline approach, and solution scores within 9.1% of theoretical optimal scores. A simplified RS metaheuristic technique was also developed in a more realistic decision-making environment dealing with multidomain command and control scenarios, where a significant computation time reduction of 87.5% is also achieved compared with a baseline approach, while maintaining solution scores within 9.5% of theoretical optimal scores.
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Almufti, Saman M. "Exploring the Impact of Big Bang-Big Crunch Algorithm Parameters on Welded Beam Design Problem Resolution." Academic Journal of Nawroz University 12, no. 4 (September 8, 2023): 1–16. http://dx.doi.org/10.25007/ajnu.v12n4a1903.
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A Metaheuristic Optimization is a group of algorithms that are widely studied and employed in the scientific literature. Typically, metaheuristics algorithms utilize stochastic operators that make each iteration unique, and they frequently contain controlling parameters that have an impact on the convergence process since their impacts are mostly neglected in most optimization literature, making it difficult to draw conclusions. This paper introduced the Big Bang-Big Crunch (BB-BC) metaheuristic algorithm to evaluate the performance of a metaheuristic algorithm in relation to its control parameter. It also demonstrates the effects of varying the values of BB-BC in solving. The "Welded Beam Design problem" is a well-known engineering optimization problem that is classified as a Single-Objective Constrained Optimization issue. Multiple starting parameter values for the BB-BC are evaluated as part of the experimental findings. This is done in an attempt to find the algorithm's optimal starting settings. The lowest, maximum, and mean values of the penalized objective functions are then computed. Finally, the BB-BC results are compared with various metaheuristics algorithms.
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Becerra-Rozas, Marcelo, José Lemus-Romani, Felipe Cisternas-Caneo, Broderick Crawford, Ricardo Soto, Gino Astorga, Carlos Castro, and José García. "Continuous Metaheuristics for Binary Optimization Problems: An Updated Systematic Literature Review." Mathematics 11, no. 1 (December 27, 2022): 129. http://dx.doi.org/10.3390/math11010129.
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For years, extensive research has been in the binarization of continuous metaheuristics for solving binary-domain combinatorial problems. This paper is a continuation of a previous review and seeks to draw a comprehensive picture of the various ways to binarize this type of metaheuristics; the study uses a standard systematic review consisting of the analysis of 512 publications from 2017 to January 2022 (5 years). The work will provide a theoretical foundation for novice researchers tackling combinatorial optimization using metaheuristic algorithms and for expert researchers analyzing the binarization mechanism’s impact on the metaheuristic algorithms’ performance. Structuring this information allows for improving the results of metaheuristics and broadening the spectrum of binary problems to be solved. We can conclude from this study that there is no single general technique capable of efficient binarization; instead, there are multiple forms with different performances.
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Nassef, Ahmed M., Mohammad Ali Abdelkareem, Hussein M. Maghrabie, and Ahmad Baroutaji. "Review of Metaheuristic Optimization Algorithms for Power Systems Problems." Sustainability 15, no. 12 (June 12, 2023): 9434. http://dx.doi.org/10.3390/su15129434.
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Metaheuristic optimization algorithms are tools based on mathematical concepts that are used to solve complicated optimization issues. These algorithms are intended to locate or develop a sufficiently good solution to an optimization issue, particularly when information is sparse or inaccurate or computer capability is restricted. Power systems play a crucial role in promoting environmental sustainability by reducing greenhouse gas emissions and supporting renewable energy sources. Using metaheuristics to optimize the performance of modern power systems is an attractive topic. This research paper investigates the applicability of several metaheuristic optimization algorithms to power system challenges. Firstly, this paper reviews the fundamental concepts of metaheuristic optimization algorithms. Then, six problems regarding the power systems are presented and discussed. These problems are optimizing the power flow in transmission and distribution networks, optimizing the reactive power dispatching, optimizing the combined economic and emission dispatching, optimal Volt/Var controlling in the distribution power systems, and optimizing the size and placement of DGs. A list of several used metaheuristic optimization algorithms is presented and discussed. The relevant results approved the ability of the metaheuristic optimization algorithm to solve the power system problems effectively. This, in particular, explains their wide deployment in this field.
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Roeva, Olympia, Dafina Zoteva, and Peter Vassilev. "Generalized Net Model of Coyote Optimization Algorithm." International Journal Bioautomation 26, no. 4 (December 2022): 353–60. http://dx.doi.org/10.7546/ijba.2022.26.4.000787.
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In the presented paper, the functioning of the coyote optimization algorithm (COA) is described using the apparatus of generalized nets (GNs). The COA is a population-based metaheuristic for optimization inspired by the Canis latrans species. Based on a Universal GN-model of population-based metaheuristics, а GN-model of COA is constructed by setting different characteristic functions of the GN-tokens. The presented GN-model successfully describes the considered metaheuristic algorithm, conducting basic steps and performing an optimal search.
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Cruz-Duarte, Jorge M., José C. Ortiz-Bayliss, Ivan Amaya, and Nelishia Pillay. "Global Optimisation through Hyper-Heuristics: Unfolding Population-Based Metaheuristics." Applied Sciences 11, no. 12 (June 18, 2021): 5620. http://dx.doi.org/10.3390/app11125620.
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Optimisation has been with us since before the first humans opened their eyes to natural phenomena that inspire technological progress. Nowadays, it is quite hard to find a solver from the overpopulation of metaheuristics that properly deals with a given problem. This is even considered an additional problem. In this work, we propose a heuristic-based solver model for continuous optimisation problems by extending the existing concepts present in the literature. We name such solvers ‘unfolded’ metaheuristics (uMHs) since they comprise a heterogeneous sequence of simple heuristics obtained from delegating the control operator in the standard metaheuristic scheme to a high-level strategy. Therefore, we tackle the Metaheuristic Composition Optimisation Problem by tailoring a particular uMH that deals with a specific application. We prove the feasibility of this model via a two-fold experiment employing several continuous optimisation problems and a collection of diverse population-based operators with fixed dimensions from ten well-known metaheuristics in the literature. As a high-level strategy, we utilised a hyper-heuristic based on Simulated Annealing. Results demonstrate that our proposed approach represents a very reliable alternative with a low computational cost for tackling continuous optimisation problems with a tailored metaheuristic using a set of agents. We also study the implication of several parameters involved in the uMH model and their influence over the solver performance.
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Cruz-Rosales, Martín H., Marco Antonio Cruz-Chávez, Federico Alonso-Pecina, Jesus del C. Peralta-Abarca, Erika Yesenia Ávila-Melgar, Beatriz Martínez-Bahena, and Juana Enríquez-Urbano. "Metaheuristic with Cooperative Processes for the University Course Timetabling Problem." Applied Sciences 12, no. 2 (January 6, 2022): 542. http://dx.doi.org/10.3390/app12020542.
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This work presents a metaheuristic with distributed processing that finds solutions for an optimization model of the university course timetabling problem, where collective communication and point-to-point communication are applied, which are used to generate cooperation between processes. The metaheuristic performs the optimization process with simulated annealing within each solution that each process works. The highlight of this work is presented in the algorithmic design for optimizing the problem by applying cooperative processes. In each iteration of the proposed heuristics, collective communication allows the master process to identify the process with the best solution and point-to-point communication allows the best solution to be sent to the master process so that it can be distributed to all the processes in progress in order to direct the search toward a space of solutions which is close to the best solution found at the time. This search is performed by applying simulated annealing. On the other hand, the mathematical representation of an optimization model present in the literature of the university course timing problem is performed. The results obtained in this work show that the proposed metaheuristics improves the results of other metaheuristics for all test instances. Statistical analysis shows that the proposed metaheuristic presents a different behavior from the other metaheuristics with which it is compared.
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Mehmood, Khizer, Naveed Ishtiaq Chaudhary, Zeshan Aslam Khan, Khalid Mehmood Cheema, Muhammad Asif Zahoor Raja, Ahmad H. Milyani, and Abdullah Ahmed Azhari. "Dwarf Mongoose Optimization Metaheuristics for Autoregressive Exogenous Model Identification." Mathematics 10, no. 20 (October 16, 2022): 3821. http://dx.doi.org/10.3390/math10203821.
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Nature-inspired metaheuristic algorithms have gained great attention over the last decade due to their potential for finding optimal solutions to different optimization problems. In this study, a metaheuristic based on the dwarf mongoose optimization algorithm (DMOA) is presented for the parameter estimation of an autoregressive exogenous (ARX) model. In the DMOA, the set of candidate solutions were stochastically created and improved using only one tuning parameter. The performance of the DMOA for ARX identification was deeply investigated in terms of its convergence speed, estimation accuracy, robustness and reliability. Furthermore, comparative analyses with other recent state-of-the-art metaheuristics based on Aquila Optimizer, the Sine Cosine Algorithm, the Arithmetic Optimization Algorithm and the Reptile Search algorithm—using a nonparametric Kruskal–Wallis test—endorsed the consistent, accurate performance of the proposed metaheuristic for ARX identification.
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Miyamoto, Sadaaki, Yasunori Endo, Koki Hanzawa, and Yukihiro Hamasuna. "Metaheuristic Algorithms for Container Loading Problems: Framework and Knowledge Utilization." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 1 (January 20, 2007): 51–60. http://dx.doi.org/10.20965/jaciii.2007.p0051.
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A family of automatic container loading problems is studied and algorithms are proposed. The algorithms are constructed with metaheuristics and include flat and/or vertical loading schemes, loading efficiency, stability of loaded objects, and computational requirement. Commonsense and expert knowledge incorporation is considered to combine with metaheuristics. Handling groups of objects in a metaheuristic scheme is moreover considered. Numerical examples are given.
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Naka, Edjola, Eris Zeqo, and Alsa Kaziu. "Performance Analysis of Metaheuristic Algorithms on Benchmark Functions." Interdisciplinary Journal of Research and Development 11, no. 2 (July 29, 2024): 10. http://dx.doi.org/10.56345/ijrdv11n202.
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The discipline of optimization can be used to maximize or minimize several problems. The use of metaheuristic algorithms is a strategy that often works well for global optimization. They are a type of stochastic algorithm that, via trial and error, finds workable solutions to difficult optimization problems in a reasonable amount of time, but they do not provide assurance that the answers are optimal. This paper aims to offer a comparative analysis of several metaheuristics in searching for the optimal solution. The selected metaheuristics are Artificial Bee Colony, Ant Lion Optimizer, Bat, Black Hole, Cuckoo Search, Cat Swarm Optimization, Dragonfly, Differential Evolution, Firefly, Genetic, Gravitational-Based Search, Grasshopper Optimization, Grey Wolf Optimizer, Harmony Search, Krill-Herd, Moth-Flame Optimizer, Particle Swarm Optimization, Sine Cosine, Shuffled Frog-Leaping, and Whale Optimization algorithms. For this evaluation, 18 benchmark test functions, categorized as unimodal, multimodal, and fixed-dimension multimodal are used to examine various properties, such as accuracy, escape from the local optimum, and convergence. As an indicator of how effectively these metaheuristics work, metrics like minimum, maximum, average, and standard deviation of fitness are provided. There are no optimization algorithms that are adequate for all problems, as the No Free Lunch theorem suggests, but the metaheuristics that are more effective than the others will be demonstrated. This study could be helpful for young researchers to identify the most prominent metaheuristics for achieving a better global optimum. Received: 8 June 2024 / Accepted: 25 July 2024 / Published: 29 July 2024Optimization, Metaheuristic algorithm, Benchmark function, Performance metric
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Correia, Sérgio D., Marko Beko, Luis A. Da Silva Cruz, and Slavisa Tomic. "Elephant Herding Optimization for Energy-Based Localization." Sensors 18, no. 9 (August 29, 2018): 2849. http://dx.doi.org/10.3390/s18092849.
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This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations, we approach it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics are applied to this type of problem. More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as a performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.
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VAN LEIJEN, A. VINCENT, LEON ROTHKRANTZ, and FRANS GROEN. "METAHEURISTIC OPTIMIZATION OF ACOUSTIC INVERSE PROBLEMS." Journal of Computational Acoustics 19, no. 04 (December 2011): 407–31. http://dx.doi.org/10.1142/s0218396x1100447x.
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Swift solving of geoacoustic inverse problems strongly depends on the application of a global optimization scheme. Given a particular inverse problem, this work aims to answer the questions how to select an appropriate metaheuristic search strategy, and how to configure it for optimal performance. Four state-of-the-art metaheuristics have been selected for this study; these are simulated annealing, genetic algorithms, ant colony optimization, and differential evolution. To make a careful comparison, each of these metaheuristic optimizers has been configured for two real-world geoacoustic inverse problems. The influence and sensitivity of specific performance parameters have been studied by analysis of repeated problem-solving. It is concluded that a proper configuration and tuning is just as important as selection of the best optimization scheme. The application in this work is geoacoustic inversion, but the argumentation on selecting and configuring an appropriate metaheuristic has potential for any indirect inverse problem.
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Sutikno, T., A. Pamungkas, G. Pau, A. Yudhana, and M. Facta. "A review of recent advances in metaheuristic maximum power point tracking algorithms for solar photovoltaic systems under the partial-shading conditions." Indonesian Journal of Science and Technology 7, no. 1 (December 16, 2021): 131–58. http://dx.doi.org/10.17509/ijost.v7i1.45612.
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Several maximum power point (MPP) tracking algorithms for solar power or photovoltaic (PV) systems concerning partial-shading conditions have been studied and reviewed using conventional or advanced methods. The standard MPPT algorithms for partial-shading conditions are: (i) conventional; (ii) mathematics-based; (iii) artificial intelligence; (iv) metaheuristic. The main problems of the conventional methods are poor power harvesting and low efficiency due to many local maximum appearances and difficulty in determining the global maximum tracking. This paper presents MPPT algorithms for partial-shading conditions, mainly metaheuristics algorithms. Firstly, the four classification algorithms will be reviewed. Secondly, an in-depth review of the metaheuristic algorithms is presented. Remarkably, 40 metaheuristic algorithms are classified into four classes for a more detailed discussion; physics-based, biology-based, sociology-based, and human behavior-based are presented and evaluated comprehensively. Furthermore, the performance comparison of the 40 metaheuristic algorithms in terms of complexity level, converter type, sensor requirement, steady-state oscillation, tracking capability, cost, and grid connection are synthesized. Generally, readers can choose the most appropriate algorithms according to application necessities and system conditions. This study can be considered a valuable reference for in-depth works on current related issues.
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Escalona-Vargas, Diana Irazú, Ivan Lopez-Arevalo, and David Gutiérrez. "Multicompare Tests of the Performance of Different Metaheuristics in EEG Dipole Source Localization." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/524367.
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We study the use of nonparametric multicompare statistical tests on the performance of simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE), when used for electroencephalographic (EEG) source localization. Such task can be posed as an optimization problem for which the referred metaheuristic methods are well suited. Hence, we evaluate the localization’s performance in terms of metaheuristics’ operational parameters and for a fixed number of evaluations of the objective function. In this way, we are able to link the efficiency of the metaheuristics with a common measure of computational cost. Our results did not show significant differences in the metaheuristics’ performance for the case of single source localization. In case of localizing two correlated sources, we found that PSO (ring and tree topologies) and DE performed the worst, then they should not be considered in large-scale EEG source localization problems. Overall, the multicompare tests allowed to demonstrate the little effect that the selection of a particular metaheuristic and the variations in their operational parameters have in this optimization problem.
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Pollaris, Hanne, Gerrit Karel Janssens, Kris Braekers, and An Caris. "Parameter tuning of a local search heuristic for a vehicle routing problem with loading constraints." Information Technology and Management Science 23 (December 15, 2020): 55–63. http://dx.doi.org/10.7250/itms-2020-0008.
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A vehicle routing problem (VRP) with sequence-based pallet loading and axle weight constraints is introduced in the study. An Iterated Local Search (ILS) metaheuristic algorithm is used to solve the problem. Like any metaheuristic, a number of parameters need to be set before running the experiments. Parameter tuning is important because the value of the parameters may have a substantial impact on the efficacy of a heuristic algorithm. While traditionally, parameter values have been set manually using expertise and experimentation, recently several automated tuning methods have been proposed. The performance of the routing algorithm is mostly improved by using parameter tuning, but no single best tuning method for routing algorithms exists. The tuning method, Iterated F-race, is chosen because it seems to be a very robust method and it has been shown to perform well on the ILS metaheuristic and other metaheuristics. The research aims at developing an algorithm, which performs well over a wide range of network sizes.
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Xie, Lei, Tong Han, Huan Zhou, Zhuo-Ran Zhang, Bo Han, and Andi Tang. "Tuna Swarm Optimization: A Novel Swarm-Based Metaheuristic Algorithm for Global Optimization." Computational Intelligence and Neuroscience 2021 (October 20, 2021): 1–22. http://dx.doi.org/10.1155/2021/9210050.
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In this paper, a novel swarm-based metaheuristic algorithm is proposed, which is called tuna swarm optimization (TSO). The main inspiration for TSO is based on the cooperative foraging behavior of tuna swarm. The work mimics two foraging behaviors of tuna swarm, including spiral foraging and parabolic foraging, for developing an effective metaheuristic algorithm. The performance of TSO is evaluated by comparison with other metaheuristics on a set of benchmark functions and several real engineering problems. Sensitivity, scalability, robustness, and convergence analyses were used and combined with the Wilcoxon rank-sum test and Friedman test. The simulation results show that TSO performs better compared to other comparative algorithms.
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Agor, Augustina Dede, Emmanuel Selase Asamoah, Godfred Yaw Koi-Akrofi, Millicent Agangiba, Selasie Aformaley Brown, Maud Adjeley Ashong Elliot, and James Tetteh Ami-Narh. "Beyond Trial and Error: A Comprehensive Classification of Metaheuristics along with Metaphor Criterion Development Trend." Indian Journal Of Science And Technology 17, no. 27 (July 31, 2024): 2778–802. http://dx.doi.org/10.17485/ijst/v17i27.2931.
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Objectives: The research aims to develop a comprehensive classification system for metaheuristics, categorize metaphor metaheuristics, and present the development trend and percentage representation of metaphor metaheuristics within each metaphor group. Method: A descriptive-based systematic review was conducted to collect data on studies concerning the classification of metaheuristics and the proposal of new metaheuristics. Data was sourced from Google Scholar, Science Direct, Springer, ResearchGate, and IEEE Xplore. For the first research objective, 148 studies were screened, resulting in the selection of six studies. The second and third research objectives involved screening 1145 studies, of which 654 were ultimately selected. This review considers studies published up to August 2023. The extracted data includes the characteristics of each classification and the name, abbreviation, author, year, and metaphor group for each metaheuristic reviewed. Findings: The results reveal that existing classifications do not cover the full range of metaheuristic characteristics. The data indicates a rising trend in the introduction of new metaheuristics over the years, with the peak occurring in 2020, boasting 68 new approaches, closely followed by 2022 with 57 introductions. However, between 1965 and 1992, progress was limited to only one or two new approaches annually, signifying periods of stagnation in the field. The majority of metaheuristics proposed are in the physics-chemistry metaphor group (20%), followed closely by human metaheuristics (18%). Novelty: The novelty of this study lies in its exhaustive classification of metaheuristics developed from 1965 to August 2023 based on the metaphor criterion, along with the development progression and percentage-wise representation of various metaphor groups using up-to-date data. Keywords: Metaheuristics, Metaphor, Classification, Optimization, Trend
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Bueno-Ferrer, Álvaro, Jaime De Pablo Valenciano, and Jerónimo De Burgos Jiménez. "Unveiling the Potential of Metaheuristics in Transportation: A Path Towards Efficiency, Optimization, and Intelligent Management." Infrastructures 10, no. 1 (December 28, 2024): 4. https://doi.org/10.3390/infrastructures10010004.
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Importance: This bibliometric analysis of the application of metaheuristics in transportation and logistics examines over two decades of research (1999–present), aiming to uncover global trends, anticipate future directions, and highlight how interconnections between key factors facilitate the development of practical and sustainable solutions for the industry. Methodology: A quantitative approach is employed to analyze the evolution of the discipline by reviewing an extensive database of relevant research and key authors and utilizing advanced data processing tools. This analysis enables the assessment of advances in the optimization of metaheuristic models, with an impact on time and cost savings from an economically sustainable perspective. Results: The use of metaheuristics optimizes the efficiency and competitiveness of the transportation sector while promoting a positive economic impact on companies. The main areas of application are optimization and metaheuristic methods, cost and operational efficiency, planning and scheduling, logistics and transportation, supply chain and logistics networks, energy and sustainability, and demand and users. Additionally, genetic algorithms stand out as particularly important. Conclusions: This research provides a comprehensive and detailed view of the impact of metaheuristics on the transportation sector, highlighting their current and future trends (such as artificial intelligence) and their economic relevance.
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Kusuma, Purba Daru, and Ashri Dinimaharawati. "Single search investigation of various searches in recent swarm-based metaheuristics." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 1 (January 1, 2025): 186. http://dx.doi.org/10.11591/ijeecs.v37.i1.pp186-196.
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Swarm intelligence has become a popular framework for developing new metaheuristics or stochastic optimization methods in recent years. Many swarm-based metaheuristics are developed by employing multiple searches whether it is conducted through swarm split, serial searches, stochastic choose. Unfortunately, many existing studies that introduced new metaheuristic focused on assessing the performance of the proposed method as a single package. On the other hand, the contribution of each search constructing the metaheuristic is still unknown as the consequence of the missing of single or individual search assessment. Based on this problem, this work is aimed to investigate the performance of five directed searches that are commonly found in recent swarm-based metaheuristics individually. These five searches include: motion toward the highest quality member, motion relative to a randomly chosen member, motion relative to a random solution along the space, motion toward a randomly chosen higher quality member, and motion toward the middle among higher quality members. In this assessment, these five searches are challenged to find the optimal solution of 23 classic functions. The result shows that the first, fourth, and five searches perform better than the second and third searches.
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Kovac, Natasa. "Metaheuristic approaches for the Berth Allocation Problem." Yugoslav Journal of Operations Research 27, no. 3 (2017): 265–89. http://dx.doi.org/10.2298/yjor160518001k.
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Berth Allocation Problem incorporates some of the most important decisions that have to be made in order to achieve maximum efficiency in a port. Terminal manager of a port has to assign incoming vessels to the available berths, which need to be loaded/unloaded in such a way that some objective function is optimized. It is well known that even simpler variants of Berth Allocation Problem are NP-hard, and thus, metaheuristic approaches are more convenient than exact methods since they provide high quality solutions in reasonable computational time. Metaheuristics are general frameworks used to build heuristic algorithms for hard optimization problems. In this paper, an overview of promising and widely used metaheuristic methods in solving different variants of Berth Allocation Problem is presented.
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Augusto, Adriano, Marlon Dumas, Marcello La Rosa, Sander J. J. Leemans, and Seppe K. L. M. vanden Broucke. "Optimization framework for DFG-based automated process discovery approaches." Software and Systems Modeling 20, no. 4 (February 27, 2021): 1245–70. http://dx.doi.org/10.1007/s10270-020-00846-x.
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AbstractThe problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.
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Omran, Mahamed G. H., Maurice Clerc, Fatme Ghaddar, Ahmad Aldabagh, and Omar Tawfik. "Permutation Tests for Metaheuristic Algorithms." Mathematics 10, no. 13 (June 24, 2022): 2219. http://dx.doi.org/10.3390/math10132219.
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Many metaheuristic approaches are inherently stochastic. In order to compare such methods, statistical tests are needed. However, choosing an appropriate test is not trivial, given that each test has some assumptions about the distribution of the underlying data that must be true before it can be used. Permutation tests (P-Tests) are statistical tests with minimal number of assumptions. These tests are simple, intuitive and nonparametric. In this paper, we argue researchers in the field of metaheuristics to adopt P-Tests to compare their algorithms. We define two statistic tests and then present an algorithm that uses them to compute the p-value. The proposed process is used to compare 5 metaheuristic algorithms on 10 benchmark functions. The resulting p-values are compared with the p-values of two widely used statistical tests. The results show that the proposed P-test is generally consistent with the classical tests, but more conservative in few cases.
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Ochoa, Gabriela, Katherine M. Malan, and Christian Blum. "Search trajectories illuminated." ACM SIGEVOlution 14, no. 2 (July 2021): 1–5. http://dx.doi.org/10.1145/3477379.3477381.
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This article summarizes our recent journal paper entitled "Search trajectory networks: A tool for analysing and visualising the behaviour of metaheuristics", where we propose a graph-based, data-driven modeling tool (STNs) to visualize and analyze the dynamics of any type of metaheuristic (evolutionary, swarm-based or single-point).
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Rosłon, Jerzy Hubert, and Janusz Edward Kulejewski. "A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction." Open Engineering 9, no. 1 (January 31, 2019): 7–13. http://dx.doi.org/10.1515/eng-2019-0006.
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AbstractPractical problems in construction can be easily qualified as NP-hard (non-deterministic, polynomial-time hard) problems. The time needed for solving these problems grows exponentially with the increase of the problem’s size – this is why mathematical and heuristic methods do not enable finding solutions to complicated construction problems within an acceptable period of time. In the view of many authors, metaheuristic algorithms seem to be the most appropriate measures for scheduling and task sequencing. However even metaheuristic approach does not guarantee finding the optimal solution and algorithms tend to get stuck around local optima of objective functions. This is why authors considered improving the metaheuristic approach by the use of neural networks. In the article, authors analyse possible benefits of using a hybrid approach with the use of metaheuristics and neural networks for solving the multi-mode, resource-constrained, project-scheduling problem (MRCPSP). The suggested approach is described and tested on a model construction project schedule. The results are promising for construction practitioners, the hybrid approach improved results in 87% of tests. Based on the research outcomes, authors suggest future research ideas.
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Fedorov, Eugene, Peter Nikolyuk, Olga Nechporenko, and Esta Chioma. "Intellectualization of a method for solving a logistics problem to optimize costs within the framework of Lean Production technology." Electronic Scientific Journal Intellectualization of Logistics and Supply Chain Management #1 2020 1, no. 3 (2020): 7–17. http://dx.doi.org/10.46783/smart-scm/2020-3-1.
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In the article, within the framework of intellectualization of the Lean Production technology, it is proposed to optimize the costs arising from the insufficient efficiency of placing goods in the warehouse by creating an optimization method based on the immune metaheuristics of the T-cell model, which allows solving the knapsack constrained optimization problem. The proposed metaheuristic method does not require specifying the probability of mutation, the number of mutations, the number of selected new cells and allows using only binary potential solutions, which makes discrete optimization possible and reduces computational complexity by preventing permanent transformations of real potential solutions into intermediate binary ones and vice versa. An immune metaheuristic algorithm based on the T-cell model has been created, intended for implementation on the GPU using the CUDA parallel information processing technology. The proposed optimization method based on immune metaheuristics can be used to intellectualize the Lean Production technology. The prospects for further researches are to test the proposed methods on a wider set of test databases.
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Kayabekir, Aylin Ece, Melda Yücel, Gebrail Bekdaş, and Sinan Melih Nigdeli. "Comparative study of optimum cost design of reinforced concrete retaining wall via metaheuristics." Challenge Journal of Concrete Research Letters 11, no. 3 (September 8, 2020): 75. http://dx.doi.org/10.20528/cjcrl.2020.03.004.
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Design engineers may find various options of metaheuristic method in optimization of their problems. Because of the randomization nature of metaheuristic methods, solutions may trap to non-optimum solutions which are just optimums in a limited part of the selected range of the design variables. Generally, metaheuristics use several options to prevent this situation, but the same optimization process may solve different performances in every run of the process. Due to that, a comparative study by using ten different algorithms was done in this study. The optimization problem is the cost minimization of an L-shaped reinforced concrete (RC) retaining wall. The evaluation is done by conducting 30 multiple cycles of optimization, and comparing minimum cost, average cost and standard deviation values.
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Virk, Amandeep K., and Kawaljeet Singh. "On Performance of Binary Flower Pollination Algorithm for Rectangular Packing Problem." Recent Advances in Computer Science and Communications 13, no. 1 (March 13, 2020): 22–34. http://dx.doi.org/10.2174/2213275911666181114143239.
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Background: Metaheuristic algorithms are optimization algorithms capable of finding near-optimal solutions for real world problems. Rectangle Packing Problem is a widely used industrial problem in which a number of small rectangles are placed into a large rectangular sheet to maximize the total area usage of the rectangular sheet. Metaheuristics have been widely used to solve the Rectangle Packing Problem. Objective: A recent metaheuristic approach, Binary Flower Pollination Algorithm, has been used to solve for rectangle packing optimization problem and its performance has been assessed. Methods: A heuristic placement strategy has been used for rectangle placement. Then, the Binary Flower Pollination Algorithm searches the optimal placement order and optimal layout. Results: Benchmark datasets have been used for experimentation to test the efficacy of Binary Flower Pollination Algorithm on the basis of utilization factor and number of bins used. The simulation results obtained show that the Binary Flower Pollination Algorithm outperforms in comparison to the other well-known algorithms. Conclusion: BFPA gave superior results and outperformed the existing state-of-the-art algorithms in many instances. Thus, the potential of a new nature based metaheuristic technique has been discovered.
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Soto, Ricardo, Broderick Crawford, Boris Almonacid, and Fernando Paredes. "Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems." Scientific Programming 2016 (2016): 1–39. http://dx.doi.org/10.1155/2016/9402503.
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The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.
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Trihardani, Luki, and Oki Anita Candra Dewi. "Pengembangan Algoritma Hybrid Metaheuristik Untuk Penentuan Rute Pengiriman Produk Perishable." Jurnal Teknik Industri 18, no. 2 (September 14, 2017): 191. http://dx.doi.org/10.22219/jtiumm.vol18.no2.191-206.
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The decision to dispatch consumers demand has become a strategic and tactical consideration to be solved in an integrated manner. In this study, the problem of determining routing problem take case study of delivery of perishable product. The routes determination should take into account the unique characteristics of perishable products possess. Perishable products continuously decreases quality over their lifetime. The challenge for distributors is how to minimize the cost of delivering perishable products by taking into account the temperature so that it can serve a number of customers within the specified timeframe,The problem of determining the route on delivery is included in the combinatorial optimization problem, thus causing this problem to be complex to be solved by the exact method. On the other hand, metaheuristic methods are increasingly being developed to be applied in the completion of combinatorial optimizations.This research started from mathematical model of perishable product delivery which pay attention to perishability (quality, temperature, quality loss) and time windows. Based on this model, this research develops the route settlement algorithm of delivery of perishable product using metaheuristic, particle swarm optimization. The algorithm development is required because route determination included in discrete issues. In addition, the development of algorithms to improve performance by combining (hybrid) algorithms, nearest neighbor and particle swarm optimization. Experiments were performed on 2 sets of Solomon data. From the experimental results with the metaheuristic hybrid algorithm is able to provide better performance than pure metaheuristik. Although the solution gap produced by these two algorithms is not very significant, but when viewed from the computation time and the number of iterations required to find the best solution, this metaheuristic hybrid algorithm can save an average time of 17 times from pure metaheuristic algorithm.
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Arslan, Sibel, Yıldız Zoralioğlu, and Muhammed Furkan Gul. "A Comparative Analysis Of African Vultures Optimization Algorithm With Current Metaheuristics." Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 8, no. 1 (January 15, 2025): 325–52. https://doi.org/10.47495/okufbed.1480875.
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With the increasing complexity of optimization problems, new metaheuristic algorithms are being developed. These algorithms show their success by exhibiting superior performances on different problems. In this paper, the performance of 4 recently proposed metaheuristic algorithms, namely Artificial Hummingbird Algorithm (AHA), African Vultures Optimization Algorithm (AVOA), Crayfish Optimization Algorithm (COA) and Marine Predators Optimization Algorithm (MPA) on 26 test functions are compared. As a result of the comparisons, it was observed that the algorithms outperformed each other with very small differences on different functions. At the same time, the comparison results were evaluated by t-test statistical test. AVOA has shown better or comparable performance to other recent metaheuristics in evaluating the quality of solutions for several test functions. It is aimed to use AVOA on different problems in future research.
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Kavoosi, Masoud, Maxim A. Dulebenets, Olumide Abioye, Junayed Pasha, Oluwatosin Theophilus, Hui Wang, Raphael Kampmann, and Marko Mikijeljević. "Berth scheduling at marine container terminals." Maritime Business Review 5, no. 1 (November 18, 2019): 30–66. http://dx.doi.org/10.1108/mabr-08-2019-0032.
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Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.
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Zajecka, Malgorzata, Mateusz Mastalerczyk, Siang Yew Chong, Xin Yao, Joanna Kwiecien, Wojciech Chmiel, Jacek Dajda, Marek Kisiel-Dorohinicki, and Aleksander Byrski. "Portfolio Optimization with Translation of Representation for Transport Problems." Journal of Artificial Intelligence and Soft Computing Research 15, no. 1 (December 8, 2024): 57–75. https://doi.org/10.2478/jaiscr-2025-0004.
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Abstract The paper presents a hybridization of two ideas closely related to metaheuristic computing, namely Portfolio Optimization (researched by Xin Yao et al.) and Translation of Representation for different metaheuristics (researched by Byrski et al.). Thus, difficult problems (discrete optimization) are approached by a sequential run through a number of steps of different metaheuristics, providing the translation of representation (since the algorithms are completely different). Therefore, close cooperation of e.g. ACO, PSO, and GA is possible. The results refer to unaltered algorithms and show the superiority of the constructed hybrid.
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Yang, Xin-She. "Metaheuristic Optimization." Scholarpedia 6, no. 8 (2011): 11472. http://dx.doi.org/10.4249/scholarpedia.11472.
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AHMIA, Ibtissam, and Méziane AÏDER. "A novel metaheuristic optimization algorithm: the monarchy metaheuristic." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 27, no. 1 (January 22, 2019): 362–76. http://dx.doi.org/10.3906/elk-1804-56.
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LESHCHENKO, Maryna, Eugen FEDOROV, Liubov KIBALNYK, and Hanna DANYLCHUK. "CONNECTIONIST-METAHEURISTIC APPROACH TO THE ANALYSIS OF THE GLOBAL ECONOMY’S INVESTMENT ENVIRONMENT INDICATORS." Computer systems and information technologies, no. 4 (December 28, 2023): 25–35. http://dx.doi.org/10.31891/csit-2023-4-4.
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The connectionist-metaheuristic approach solved the urgent task of using new approaches to analyze the foreign direct investment and macroeconomic indicators that affect the volume of their attraction to a particular country in the world economy. The proposed connectionist-metaheuristic system makes it possible to improve the quality of the approximation due to: the simplification of structural identification through the use of only one hidden layer of neural network models; reduction of the computational complexity of parametric identification and the ensuring good scalability through the use of batch mode for non-recurrent neural network models and multi-agent metaheuristics for recurrent neural network models; descriptions of non-linear dependencies through the use of neural network models; high approximation accuracy due to the use of recurrent neural network models; resistance to data incompleteness and data noise due to the use of metaheuristics for parametric identification of recurrent neural network models; lack of requirements for knowledge of distribution, homogeneity, weak correlation, and optimal factors’ choice. In the case of a GPU, an LSTM-based neural network with the highest approximation accuracy should be chosen. For LSTM, the coefficient of determination using the gradient method is 0.785, and using metaheuristics (modified wasp colony optimization) is 0.835. The proposed approach makes it possible to expand the scope of approximation methods’ application based on artificial neural networks and metaheuristics, which is confirmed by its adaptation for an economic problem and contributes to an increase in intelligent computer systems efficiency for general and special purposes.