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Journal articles on the topic 'Meta heuristics algorithms'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 April 2024

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1

Sheta, Alaa F., Hossam Faris, and Ibrahim Aljarah. "Estimating ARMA Model Parameters of an Industrial Process Using Meta-Heuristic Search Algorithms." International Journal of Engineering & Technology 7, no. 3.10 (2018): 187. http://dx.doi.org/10.14419/ijet.v7i3.10.14357.

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This paper addresses the parameter estimation problem for a manufacturing process based on the Auto-Regressive Moving Average (ARMA) model. The accurate estimation of the ARMA model’s parameter helps to reduce the production costs, provide better product quality, increase productivity and profit. Meta-heuristic algorithms are among these approximate techniques which have been successfully used to search for an optimal solution in complex search space. Meta-heuristic algorithms can converge to an optimal global solution despite traditional parameter estimation techniques which stuck by local op

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de Carvalho, Vinicius Renan, Ender Özcan, and Jaime Simão Sichman. "Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems." Applied Sciences 11, no. 19 (2021): 9153. http://dx.doi.org/10.3390/app11199153.

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As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-trivial task, and often requires a time-consuming trial and error process. Hyper-heuristics, which are heuristics to choose heuristics, have been proposed as a means to both simplify and improve algorithm selection or configuration for optimization problems. This paper novel presents a novel cross-domain evaluation for multi-objective optimization: we

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Dahiya, Brahm Prakash, Shaveta Rani, and Paramjeet Singh. "A Hybrid Artificial Grasshopper Optimization (HAGOA) Meta-Heuristic Approach: A Hybrid Optimizer For Discover the Global Optimum in Given Search Space." International Journal of Mathematical, Engineering and Management Sciences 4, no. 2 (2019): 471–88. http://dx.doi.org/10.33889/ijmems.2019.4.2-039.

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Meta-heuristic algorithms are used to get optimal solutions in different engineering branches. Here four types of meta-heuristics algorithms are used such as evolutionary algorithms, swarm-based algorithms, physics based algorithms and human based algorithms respectively. Swarm based meta-heuristic algorithms are given more effective result in optimization problem issues and these are generated global optimal solution. Existing swarm intelligence techniques are suffered with poor exploitation and exploration in given search space. Therefore, in this paper Hybrid Artificial Grasshopper Optimiza

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Golab, Amir, Ehsan Sedgh Gooya, Ayman Al Falou, and Mikael Cabon. "Review of conventional metaheuristic techniques for resource-constrained project scheduling problem." Journal of Project Management 7, no. 2 (2022): 95–110. http://dx.doi.org/10.5267/j.jpm.2021.10.002.

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This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known t

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K, Gayathri Devi. "A Hybrid Firefly Algorithm Approach for Job Shop Scheduling Problem." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (2021): 1436–44. http://dx.doi.org/10.22214/ijraset.2021.39536.

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Abstract: Job shop scheduling has always been one of the most sought out research problems in Combinatorial optimization. Job Shop Scheduling problems (JSSP) are categorized under NP hard problems. In recent years the meta heuristic algorithms have been proved effective to solve hardcore NP problem. Firefly Algorithm is one of such meta heuristic techniques which is nature inspired from firefly characteristic. Its potential can be enhanced further by hybridizing it with other known evolutionary algorithms and thereby getting improved results in less computational time. In this paper we have pr

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Aungkulanon, Pasura. "A Comparative Study of Global-Best Harmony Search and Bat Algorithms on Optimization Problems." Applied Mechanics and Materials 464 (November 2013): 352–57. http://dx.doi.org/10.4028/www.scientific.net/amm.464.352.

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The engineering optimization problems are large and complex. Effective methods for solving these problems using a finite sequence of instructions can be categorized into optimization and meta-heuristics algorithms. Meta-heuristics techniques have been proved to solve various real world problems. In this study, a comparison of two meta-heuristic techniques, namely, Global-Best Harmony Search algorithm (GHSA) and Bat algorithm (BATA), for solving constrained optimization problems was carried out. GHSA and BATA are optimization algorithms inspired by the structure of harmony improvisation search

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El-Henawy, Ibrahim, and Nagham Ahmed. "Meta-Heuristics Algorithms: A Survey." International Journal of Computer Applications 179, no. 22 (2018): 45–54. http://dx.doi.org/10.5120/ijca2018916427.

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Zaki, Shereen, and Abd El-Nasser H. Zaied. "Meta-heuristics Algorithms: A survey." International Journal of Engineering Trends and Technology 67, no. 5 (2019): 67–74. http://dx.doi.org/10.14445/22315381/ijett-v67i5p210.

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Luna Gutierrez, Ricardo, and Matteo Leonetti. "Meta Reinforcement Learning for Heuristic Planing." Proceedings of the International Conference on Automated Planning and Scheduling 31 (May 17, 2021): 551–59. http://dx.doi.org/10.1609/icaps.v31i1.16003.

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Heuristic planning has a central role in classical planning applications and competitions. Thanks to this success, there has been an increasing interest in using Deep Learning to create high-quality heuristics in a supervised fashion, learning from optimal solutions of previously solved planning problems. Meta-Reinforcement learning is a fast growing research area concerned with learning, from many tasks, behaviours that can quickly generalize to new tasks from the same distribution of the training ones. We make a connection between meta-reinforcement learning and heuristic planning, showing t

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Ariyaratne, M. K. A., and R. M. Silva. "Meta-heuristics meet sports: a systematic review from the viewpoint of nature inspired algorithms." International Journal of Computer Science in Sport 21, no. 1 (2022): 49–92. http://dx.doi.org/10.2478/ijcss-2022-0003.

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Abstract This review explores the avenues for the application of meta-heuristics in sports. The necessity of sophisticated algorithms to investigate different NP hard problems encountered in sports analytics was established in the recent past. Meta-heuristics have been applied as a promising approach to such problems. We identified team selection, optimal lineups, sports equipment optimization, scheduling and ranking, performance analysis, predictions in sports, and player tracking as seven major categories where meta-heuristics were implemented in research in sports. Some of our findings incl

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Masrom, Suraya, Abdullah Sani Abd Rahman, Nasiroh Omar, and Suriani Rapa’ee. "PSO-GAScript: A Domain-specific Scripting Language for Meta-heuristics Algorithm." International Journal of Emerging Technology and Advanced Engineering 12, no. 7 (2022): 86–93. http://dx.doi.org/10.46338/ijetae0722_09.

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PSO-GAScript is a domain-specific scripting language designed to support easy and rapid implementation of meta-heuristics algorithms focused on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The programming language has been developed to allow the hybridization of the two meta-heuristics algorithms. Hybridizations between PSO and GA are proven to be a comprehensive tool for solving different kinds of optimization problems. Moreover, the two algorithms have achieved a remarkable improvement from the adaptation of dynamic parameterization. Nevertheless, implementing the suitable h

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Abdul-Razaq, Tariq, Hanan Chachan, and Faez Ali. "Modified Heuristics for Scheduling in Flow Shop to Minimize Makespan." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 19, 2021): 1–20. http://dx.doi.org/10.55562/jrucs.v30i2.361.

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The NP-completeness of flow shops scheduling problems has been discussed for many years. Hence many heuristics have been proposed to obtain solutions of good quality with a small computational effort. The CDS (Campbell et al) and NEH (Nawaz, Enscore and Ham) heuristics are efficient among meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA).This paper discusses some methods and suggests new developing to the methods of the scheduling in flow shop to minimize makespan problems. Our main object in this paper, from one side, is to improve efficient heuristics which

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Peng, Yudong. "Advancing signal integrity and application analysis through data-flow mapping." Applied and Computational Engineering 47, no. 1 (2024): 131–40. http://dx.doi.org/10.54254/2755-2721/47/20241255.

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Data-flow mapping is a crucial method in signal processing and optimization, managing data flow within systems. Its essential in signal compensation, particularly in telecommunications, audio processing, and biomedical signal processing. Four main algorithm categories underpin data-flow mapping: heuristics, meta-heuristics, Integer Linear Programming (ILP), and Constraint Satisfaction Problems (CSP). Heuristic and meta-heuristic methods like Genetic Algorithms (GA) and Ant Colony Optimization (ACO) provide approximate solutions, crucial for complex problems. ILP and Branch and Bound (B&B)

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Kamboj, Vikram Kumar, Sobhit Saxena, and Kamalpreet Sandhu. "Optimal Selection of Gear Ratio for Hybrid Electric Vehicles Using Modern Meta-Heuristics Search Algorithm." E3S Web of Conferences 87 (2019): 01006. http://dx.doi.org/10.1051/e3sconf/20198701006.

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Gear Train Design problem is most important design problem for machine tools manufacturers. Recent work on gear train improvement has been bound towards multi-shaft gear trains of the speed-change kind, where major focus is to maximize the range of operating speeds and to minimize the number of gears and spindles. In the proposed research, a hybrid meta-heuristic search algorithm is presented to design and optimize multi-spindle gear trains problem. The objective of the research is to optimize gear trains on the basis of minimum overall centre distance, minimum overall size, minimum gear volum

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Hudaib, Amjad A., and Hussam N. Fakhouri. "Supernova Optimizer: A Novel Natural Inspired Meta-Heuristic." Modern Applied Science 12, no. 1 (2017): 32. http://dx.doi.org/10.5539/mas.v12n1p32.

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Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to solve optimization and preliminary convergence problems. It significantly has wide effect that is integrated in many scientific fields. Thereby justifying the relevance development of many applications that relay on optimization algorithms, which allow finding the best solution in the shortest possible time. Therefore it is necessary to further consider and develop new swarm intelligence optimization algorithms. This paper proposes a novel optimization algorithm called supernova optimizer (SO) inspired by th

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Ma, Zhenfang, Kaizhou Gao, Hui Yu, and Naiqi Wu. "Solving Heterogeneous USV Scheduling Problems by Problem-Specific Knowledge Based Meta-Heuristics with Q-Learning." Mathematics 12, no. 2 (2024): 339. http://dx.doi.org/10.3390/math12020339.

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This study focuses on the scheduling problem of heterogeneous unmanned surface vehicles (USVs) with obstacle avoidance pretreatment. The goal is to minimize the overall maximum completion time of USVs. First, we develop a mathematical model for the problem. Second, with obstacles, an A* algorithm is employed to generate a path between two points where tasks need to be performed. Third, three meta-heuristics, i.e., simulated annealing (SA), genetic algorithm (GA), and harmony search (HS), are employed and improved to solve the problems. Based on problem-specific knowledge, nine local search ope

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Cetin Yagmur, Ece, and Ahmet Sarucan. "Nurse Scheduling with Opposition-Based Parallel Harmony Search Algorithm." Journal of Intelligent Systems 28, no. 4 (2019): 633–47. http://dx.doi.org/10.1515/jisys-2017-0150.

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Abstract One of the advances made in the management of human resources for the effective implementation of service delivery is the creation of personnel schedules. In this context, especially in terms of the majority of health-care systems, creating nurse schedules comes to the fore. Nurse scheduling problem (NSP) is a complex optimization problem that allows for the preparation of an appropriate schedule for nurses and, in doing so, considers the system constraints such as legal regulations, nurses’ preferences, and hospital policies and requirements. There are many studies in the literature

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Abdessemed, Ahmed Adnane, Leila Hayet Mouss, and Khaled Benaggoune. "BASA: An improved hybrid bees algorithm for the single machine scheduling with early/tardy jobs." International Journal of Production Management and Engineering 11, no. 2 (2023): 167–78. http://dx.doi.org/10.4995/ijpme.2023.18077.

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In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive per

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Jakwa, Ali Garba, Dr Gital, Professor Souley, and Dr Fatima Umar Zambuk Umar Zambuk. "Hybrid Meta-Heuristics Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing." International Journal of Advances in Scientific Research and Engineering 09, no. 02 (2023): 20–28. http://dx.doi.org/10.31695/ijasre.2023.9.2.3.

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of Internet of Things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques are reassessed based on deterministic and meta-heuristics to find out solution to scheduling problem in fog computing. This paper proposes Hybrid Meta-Heuristics Optimiz

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Fazelabdolabadi, Babak, Hadi Bagherzadeh, Abbas Shahrabadi, and Sayed Ehsan Samimi. "On the prediction of pseudo relative permeability curves: meta-heuristics versus Quasi-Monte Carlo." Oil & Gas Science and Technology – Revue d’IFP Energies nouvelles 74 (2019): 42. http://dx.doi.org/10.2516/ogst/2019014.

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This article reports the first application of the Quasi-Monte Carlo (QMC) method for estimation of the pseudo relative permeability curves. In this regards, the performance of several meta-heuristics algorithms have also been compared versus QMC, including the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and the Artificial Bee Colony (ABC). The mechanism of minimizing the objective-function has been studied, for each method. The QMC has outperformed its counterparts in terms of accuracy and efficiently sweeping the entire search domain. Nevertheless, its computational time requir

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SWAN, JERRY, GABRIELA OCHOA, GRAHAM KENDALL, and MARTIN EDJVET. "FITNESS LANDSCAPES AND THE ANDREWS–CURTIS CONJECTURE." International Journal of Algebra and Computation 22, no. 02 (2012): 1250009. http://dx.doi.org/10.1142/s0218196711006753.

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Attempts have been made to eliminate some potential counterexamples to the Andrews–Curtis conjecture using the combinatorial optimization methods of blind-search and the genetic algorithms meta-heuristic. Breadth-first search with secondary storage is currently the most successful method, which raises questions regarding the inferior performance of heuristic search. In order to understand the underlying reasons we obtain fitness landscape metrics for a number of balanced presentations and draw conclusions regarding the likely effectiveness of other meta-heuristics.

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Suresh, Vishnu, Michal Jasinski, Zbigniew Leonowicz, Dominika Kaczorowska, Jithendranath J., and Hemachandra Reddy K. "Political-Optimizer-Based Energy-Management System for Microgrids." Electronics 10, no. 24 (2021): 3119. http://dx.doi.org/10.3390/electronics10243119.

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This paper presents an energy-management strategy based on a recently introduced Political Optimizer (PO) for a microgrid installation at Wroclaw University of Science and Technology. The aim of the study is to check the effectiveness of two recently introduced meta-heuristic algorithms at power-system-operations planning. The optimization algorithms were compared with other conventional meta-heuristics wherein performance tests were carried out by minimizing costs in an IEEE 30-bus system. The best performing algorithm was then used to minimize the Levelized Cost of Energy (LCOE) in a microgr

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Aungkulanon, Pasura. "Two Meta-Heuristics for Solving Unconstrained Optimization Problems and Machinery Problems." Advanced Materials Research 1044-1045 (October 2014): 1418–23. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.1418.

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Machining optimization problem aims to optimize machinery conditions which are important for economic settings. The effective methods for solving these problems using a finite sequence of instructions can be categorized into two groups; exact optimization algorithm and meta-heuristic algorithms. A well-known meta-heuristic approach called Harmony Search Algorithm was used to compare with Particle Swarm Optimization. We implemented and analysed algorithms using unconstrained problems under different conditions included single, multi-peak, curved ridge optimization, and machinery optimization pr

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Goel, Gauvav, and Rajeev Tiwari. "Task management in IoT-Fog-Cloud environment employing static scheduling Techniques." ENP Engineering Science Journal 2, no. 1 (2022): 13–20. http://dx.doi.org/10.53907/enpesj.v2i1.76.

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In a distributed computing system, there are limited resources, which needs to be utilized effectively. Then for improving QoS Fog computing paradigm is an effective way, with suitable allocations. Thus, different resource scheduling and optimization algorithms exist. However, still, there is a scope to improve bandwidth, latency, energy consumption, and total communication cost in the Fog environment. In this work investigation is done to show significance of task management in such resource constrained environment. Various heuristics and meta-heuristic algorithms are evaluated using simulati

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Liu, Wei, Yongkun Huang, Zhiwei Ye, et al. "Renyi’s Entropy Based Multilevel Thresholding Using a Novel Meta-Heuristics Algorithm." Applied Sciences 10, no. 9 (2020): 3225. http://dx.doi.org/10.3390/app10093225.

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Multi-level image thresholding is the most direct and effective method for image segmentation, which is a key step for image analysis and computer vision, however, as the number of threshold values increases, exhaustive search does not work efficiently and effectively and evolutionary algorithms often fall into a local optimal solution. In the paper, a meta-heuristics algorithm based on the breeding mechanism of Chinese hybrid rice is proposed to seek the optimal multi-level thresholds for image segmentation and Renyi’s entropy is utilized as the fitness function. Experiments have been run on

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Mohamed, Marwa F., Mohamed Meselhy Eltoukhy, Khalil Al Ruqeishi, and Ahmad Salah. "An Adapted Multi-Objective Genetic Algorithm for Healthcare Supplier Selection Decision." Mathematics 11, no. 6 (2023): 1537. http://dx.doi.org/10.3390/math11061537.

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With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heu

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Prajapati, Pankaj P., Anilkumar J. Kshatriya, Dhavalkumar N. Patel, Sima K. Gonsai, Hardik B. Tank, and Kinjal R. Sheth. "Comparative analysis of meta heuristics algorithm for differential amplifier design." Bulletin of Electrical Engineering and Informatics 12, no. 6 (2023): 3395–401. http://dx.doi.org/10.11591/eei.v12i6.6153.

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The design of analog circuits is very essential for the development of system design based on electronics as the world is analog in nature. Furthermore, performance of emerging products depends on analog circuits for reduction of power dissipation and improvement of speed. Though in the system on chip (SoC) analog circuit consumes less, it is more complex to design it due to the analog circuit nature complexity. The job of the evolutionary algorithm (EA) in the process of optimization of an analog circuit based on complementary metal oxide semiconductor (CMOS) is to get the appropriate values

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Aswin, B., Tapan Lokhande, and Rajesh S. Prabhu Gaonkar. "Solving Redundancy Allocation Problems using Jaya Algorithm." International Journal of Mathematical, Engineering and Management Sciences 8, no. 5 (2023): 804–16. http://dx.doi.org/10.33889/ijmems.2023.8.5.046.

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Reliability-based design is related to the performance analysis of engineering systems. The redundancy allocation problem is one of the most common problems in the reliability-based design approach. The redundancy allocation problem determines the redundancy level of components in a system to maximize system reliability, subject to several constraints. In recent years, obtaining solutions to reliability-related redundancy allocation problems by means of evolving meta-heuristic algorithms has appealed to researchers due to the several drawbacks of classical mathematical methods. Meta-heuristics

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Werth, Bernhard, Johannes Karder, Michael Heckmann, Stefan Wagner, and Michael Affenzeller. "Applying Learning and Self-Adaptation to Dynamic Scheduling." Applied Sciences 14, no. 1 (2023): 49. http://dx.doi.org/10.3390/app14010049.

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Real-world production scheduling scenarios are often not discrete, separable, iterative tasks but rather dynamic processes where both external (e.g., new orders, delivery shortages) and internal (e.g., machine breakdown, timing uncertainties, human interaction) influencing factors gradually or abruptly impact the production system. Solutions to these problems are often very specific to the application case or rely on simple problem formulations with known and stable parameters. This work presents a dynamic scheduling scenario for a production setup where little information about the system is

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Rahbari, Dadmehr. "Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application." Algorithms 15, no. 11 (2022): 397. http://dx.doi.org/10.3390/a15110397.

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In recent years, the increasing use of the Internet of Things (IoT) has generated excessive amounts of data. It is difficult to manage and control the volume of data used in cloud computing, and since cloud computing has problems with latency, lack of mobility, and location knowledge, it is not suitable for IoT applications such as healthcare or vehicle systems. To overcome these problems, fog computing (FC) has been used; it consists of a set of fog devices (FDs) with heterogeneous and distributed resources that are located between the user layer and the cloud on the edge of the network. An a

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Ferrari, Allan Christian Krainski, Leandro dos Santos Coelho, Gideon Villar Leandro, Cristiano Osinski, and Carlos Alexandre Gouvea da Silva. "Meta-heuristic inspired by the behavior of the humpback whale tuned by a fuzzy inference system." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7993–8000. http://dx.doi.org/10.3233/jifs-201459.

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The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test

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El Karim, Tahari Abdou, Bendakmousse Abdeslam, and Ait Aoudia Samy. "Computing the Medical Image Registration Using Meta-Heuristics." Applied Mechanics and Materials 643 (September 2014): 237–42. http://dx.doi.org/10.4028/www.scientific.net/amm.643.237.

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The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-he

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Sheta, Alaa, Malik Braik, Dheeraj Reddy Maddi, Ahmed Mahdy, Sultan Aljahdali, and Hamza Turabieh. "Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms." Applied Sciences 11, no. 14 (2021): 6492. http://dx.doi.org/10.3390/app11146492.

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Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcop

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Udaiyakumar, K. C., and M. Chandrasekaran. "Optimization of Multi Objective Job Shop Scheduling Problems Using Firefly Algorithm." Applied Mechanics and Materials 591 (July 2014): 157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.591.157.

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Scheduling is the allocation of resources over time to carry out a collection of tasks assigned in any field of engineering and non engineering. Majority of JSSP are categorized into non deterministic (NP) hard problem because of its complexity. Scheduling are generally solved by using heuristics to obtain optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using available resources in many cases. Many researchers attempted to solve the problem by applying various optimization techniques. While using traditional methods they observe

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Alzaghoul, Esra F., and Sandi N. Fakhouri. "Collaborative Strategy for Grey Wolf Optimization Algorithm." Modern Applied Science 12, no. 7 (2018): 73. http://dx.doi.org/10.5539/mas.v12n7p73.

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Grey wolf Optimizer (GWO) is one of the well known meta-heuristic algorithm for determining the minimum value among a set of values. In this paper, we proposed a novel optimization algorithm called collaborative strategy for grey wolf optimizer (CSGWO). This algorithm enhances the behaviour of GWO that enhances the search feature to search for more points in the search space, whereas more groups will search for the global minimal points. The algorithm has been tested on 23 well-known benchmark functions and the results are verified by comparing them with state of the art algorithms: Polar part

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Guo, Yurong, Quan Shi, and Chiming Guo. "A Performance-Oriented Optimization Framework Combining Meta-Heuristics and Entropy-Weighted TOPSIS for Multi-Objective Sustainable Supply Chain Network Design." Electronics 11, no. 19 (2022): 3134. http://dx.doi.org/10.3390/electronics11193134.

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The decision-making of sustainable supply chain network (SSCN) design is a strategy capacity for configuring network facility and product flow. When optimizing conflicting economic, environmental, and social performance objectives, it is difficult to select the optimal scheme from obtained feasible decision schemes. In this article, according to the triple bottom line of sustainability, a multi-objective sustainable supply chain network optimization model is developed, and a novel performance-oriented optimization framework is proposed. This framework, referred to as performance-oriented optim

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Abdel-Basset, Mohamed, Laila A. Shawky, and Arun Kumar Sangaiah. "A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems." Library Hi Tech 35, no. 4 (2017): 588–601. http://dx.doi.org/10.1108/lht-04-2017-0077.

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Purpose The purpose of this paper is to present a comparison between two well-known Lévy-based meta-heuristics called cuckoo search (CS) and flower pollination algorithm (FPA). Design/methodology/approach Both the algorithms (Lévy-based meta-heuristics called CS and Flower Pollination) are tested on selected benchmarks from CEC 2017. In addition, this study discussed all CS and FPA comparisons that were included implicitly in other works. Findings The experimental results show that CS is superior in global convergence to the optimal solution, while FPA outperforms CS in terms of time complexit

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Abdul-Niby, M., M. Alameen, A. Salhieh, and A. Radhi. "Improved Genetic and Simulating Annealing Algorithms to Solve the Traveling Salesman Problem Using Constraint Programming." Engineering, Technology & Applied Science Research 6, no. 2 (2016): 927–30. http://dx.doi.org/10.48084/etasr.627.

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The Traveling Salesman Problem (TSP) is an integer programming problem that falls into the category of NP-Hard problems. As the problem become larger, there is no guarantee that optimal tours will be found within reasonable computation time. Heuristics techniques, like genetic algorithm and simulating annealing, can solve TSP instances with different levels of accuracy. Choosing which algorithm to use in order to get a best solution is still considered as a hard choice. This paper suggests domain reduction as a tool to be combined with any meta-heuristic so that the obtained results will be al

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Kushwaha, Nand Lal, Jitendra Rajput, Ahmed Elbeltagi, et al. "Data Intelligence Model and Meta-Heuristic Algorithms-Based Pan Evaporation Modelling in Two Different Agro-Climatic Zones: A Case Study from Northern India." Atmosphere 12, no. 12 (2021): 1654. http://dx.doi.org/10.3390/atmos12121654.

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Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi

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Shao, Kaili, Ying Song, and Bo Wang. "PGA: A New Hybrid PSO and GA Method for Task Scheduling with Deadline Constraints in Distributed Computing." Mathematics 11, no. 6 (2023): 1548. http://dx.doi.org/10.3390/math11061548.

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Distributed computing, e.g., cluster and cloud computing, has been applied in almost all areas for data processing, while high resource efficiency and user satisfaction are still the ambition of distributed computing. Task scheduling is indispensable for achieving the goal. As the task scheduling problem is NP-hard, heuristics and meta-heuristics are frequently applied. Every method has its own advantages and limitations. Thus, in this paper, we designed a hybrid heuristic task scheduling problem by exploiting the high global search ability of the Genetic Algorithm (GA) and the fast convergenc

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Sabha, Muath, Thaer Thaher, and Marwa M. Emam. "Cooperative Swarm Intelligence Algorithms for Adaptive Multilevel Thresholding Segmentation of COVID-19 CT-Scan Images." JUCS - Journal of Universal Computer Science 29, no. 7 (2023): 759–804. http://dx.doi.org/10.3897/jucs.93498.

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The Coronavirus Disease 2019 (COVID-19) is widespread throughout the world and poses a serious threat to public health and safety. A COVID-19 infection can be recognized using computed tomography (CT) scans. To enhance the categorization, some image segmentation techniques are presented to extract regions of interest from COVID-19 CT images. Multi-level thresholding (MLT) is one of the simplest and most effective image segmentation approaches, especially for grayscale images like CT scan images. Traditional image segmentation methods use histogram approaches; however, these approaches encounte

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Zarook, Yaser, Javad Rezaeian, Iraj Mahdavi, and Masoud Yaghini. "Efficient algorithms to minimize makespan of the unrelated parallel batch-processing machines scheduling problem with unequal job ready times." RAIRO - Operations Research 55, no. 3 (2021): 1501–22. http://dx.doi.org/10.1051/ro/2021062.

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This paper considers the minimization of makespan in the unrelated parallel batch processing machines scheduling problem with considering non-identical job size and dynamic job ready time. The considered unrelated machines have different capacity and different processing speed. Each machine processes a number of the jobs as a batch at the same time so that the machine’s capacity is not exceeded. The batch processing time and the batch ready time are equal to the largest processing time and the largest ready time of jobs in the same batch, respectively. In this paper, a Mixed Integer Linear Pro

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Jakwa, Ali Garba, Abdulsalam Yau Gital, Souley Boukari, and Fatima Umar Zambuk. "Performance Evaluation of Hybrid Meta-Heuristics-Based Task Scheduling Algorithm for Energy Efficiency in Fog Computing." International Journal of Cloud Applications and Computing 13, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijcac.324758.

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Task scheduling in fog computing is one of the areas where researchers are having challenges as the demand grows for the use of internet of things (IoT) to access cloud computing resources. Many resource scheduling and optimization algorithms were used by many researchers in fog computing; some used single techniques while others used combined schemes to achieve dynamic scheduling in fog computing, many optimization techniques were assessed based on deterministic and meta-heuristic to find out solution to task scheduling problem in fog computing but could not achieve excellent results as requi

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Arora, Neeraj, and Rohitash Kumar Banyal. "Hybrid scheduling algorithms in cloud computing: a review." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 1 (2022): 880. http://dx.doi.org/10.11591/ijece.v12i1.pp880-895.

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<p><span>Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algo

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Kalaf, Bayda Atiya, Ghadeer Jasim Mohammed, and Muna D. Salman. "A New Hybrid Meta-Heuristics Algorithms to Solve APP Problems." Journal of Physics: Conference Series 1897, no. 1 (2021): 012011. http://dx.doi.org/10.1088/1742-6596/1897/1/012011.

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Ruiz-Vanoye, Jorge A., and Ocotlán Díaz-Parra. "Similarities between meta-heuristics algorithms and the science of life." Central European Journal of Operations Research 19, no. 4 (2010): 445–66. http://dx.doi.org/10.1007/s10100-010-0135-x.

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BURDETT, ROBERT L., and ERHAN KOZAN. "EVOLUTIONARY ALGORITHMS FOR RESOURCE CONSTRAINED NON-SERIAL MIXED FLOW SHOPS." International Journal of Computational Intelligence and Applications 03, no. 04 (2003): 411–35. http://dx.doi.org/10.1142/s1469026803001105.

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In this paper the resource-constrained flow shop (RCF) problem is addressed. A number of realistic extensions are incorporated, including non-serial precedence requirements, mixed flow shop situations, and the distribution of the human workforce among a number of pre-determined groups. The RCF is then solved by meta-heuristics, primarily of the evolutionary type. An extensive numerical investigation, including a case study of a particular industrial situation, details the implementation and execution of the heuristics, and the efficiency of the proposed algorithms.

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Sheikholeslami, Razi, Aaron C. Zecchin, Feifei Zheng, and Siamak Talatahari. "A hybrid cuckoo–harmony search algorithm for optimal design of water distribution systems." Journal of Hydroinformatics 18, no. 3 (2015): 544–63. http://dx.doi.org/10.2166/hydro.2015.174.

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Meta-heuristic algorithms have been broadly used to deal with a range of water resources optimization problems over the past decades. One issue that exists in the use of these algorithms is the requirement of large computational resources, especially when handling real-world problems. To overcome this challenge, this paper develops a hybrid optimization method, the so-called CSHS, in which a cuckoo search (CS) algorithm is combined with a harmony search (HS) scheme. Within this hybrid framework, the CS is employed to find the promising regions of the search space within the initial explorative

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LV, Zhaoming, and Rong PENG. "Improving the Efficiency of Multi-Objective Grasshopper Optimization Algorithm to Enhance Ontology Alignment." Wuhan University Journal of Natural Sciences 27, no. 3 (2022): 240–54. http://dx.doi.org/10.1051/wujns/2022273240.

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Ontology alignment is an essential and complex task to integrate heterogeneous ontology. The meta-heuristic algorithm has proven to be an effective method for ontology alignment. However, it only applies the inherent advantages of meta-heuristics algorithm and rarely considers the execution efficiency, especially the multi-objective ontology alignment model. The performance of such multi-objective optimization models mostly depends on the well-distributed and the fast-converged set of solutions in real-world applications. In this paper, two multi-objective grasshopper optimization algorithms (

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Wang, Wentao, and Jun Tian. "An Improved Nonlinear Tuna Swarm Optimization Algorithm Based on Circle Chaos Map and Levy Flight Operator." Electronics 11, no. 22 (2022): 3678. http://dx.doi.org/10.3390/electronics11223678.

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The tuna swarm optimization algorithm (TSO) is a new heuristic algorithm proposed by observing the foraging behavior of tuna populations. The advantages of TSO are a simple structure and fewer parameters. Although TSO converges faster than some classical meta-heuristics algorithms, it can still be further accelerated. When TSO solves complex and challenging problems, it often easily falls into local optima. To overcome the above issue, this article proposed an improved nonlinear tuna swarm optimization algorithm based on Circle chaos map and levy flight operator (CLTSO). In order to compare it

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