Journal articles: 'Nature-Inspired Algorithms'

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Gautam, Raj Kumar. "Nature Inspired Metaheuristic based Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32390.

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This paper is a comprehensive study on nature inspired Hyperparameter optimization, with a distinct focus on Honey Badger Algorithm, along with Aquila Optimizer algorithm. The study involves in-depth analysis of the above algorithms, their weaknesses and strengths and comparing them with the theoretical advantages. The implementation of these algorithms, This paper demonstrate the promise of these algorithms on optimization of Hyperparameters like learning rate, number of hidden layers for our various datasets. The findings of this paper show that HBA and Aquila Optimization algorithms offer potential alternatives to the existing approaches, providing more effective and efficient solutions for hyperparameter optimization. This paper contributes to ongoing discourse on the place of nature inspired algorithms and their place in solutions to unconventional places

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Kumar, Deepak, Sushil Kumar, Rohit Bansal, and Parveen Singla. "A Survey to Nature Inspired Soft Computing." International Journal of Information System Modeling and Design 8, no. 2 (2017): 112–33. http://dx.doi.org/10.4018/ijismd.2017040107.

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This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

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Jain, Vidhi. "Nature-inspired approaches in Software Fault Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34235.

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In software engineering, predicting software faults is a crucial task for ensuring high software quality and reducing costs. In recent years, nature inspired approaches have been increasingly used in software fault prediction. In this paper, we explore the effectiveness of six nature inspired algorithms, namely Ant Colony, Particle Swarm Optimization, Firefly, Bat, Harris Hawks, and Genetic Algorithm, for software fault prediction. We evaluate the algorithms using three commonly used datasets, JM1, CM1, and PC1. Our experimental results show that nature inspired approaches can effectively predict software faults, with some algorithms performing better than others depending on the dataset used. Our findings suggest that these approaches have potential to be used as a practical and efficient means for software fault prediction. Keywords— nature inspired algorithms; PSO; Ant Colony Optimization; Harris Hawks; Genetic Algorithm (GA); python programming; Jupyter Notebook; confusion matrix;

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Sukale, Sakshi, and Tanaji D. Biradar. "Review of Nature Inspired Algorithms." International Journal of Computer Applications 109, no. 3 (2015): 6–8. http://dx.doi.org/10.5120/19166-0625.

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POLAK, Iwona, and Mariusz BORYCZKA. "CRYPTANALYSIS USING NATURE-INSPIRED ALGORITHMS." National Security Studies 6, no. 2 (2014): 185–97. http://dx.doi.org/10.37055/sbn/135230.

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W dzisiejszych czasach ochrona informacji jest niezwykle istotna, a jednym z elementów zapewniających ową ochronę jest kryptografia. Tu z kolei ważną rolę odgrywa kryptoanaliza, która pozwala badać bezpieczeństwo używanych szyfrów. Oprócz typowo analitycznego podejścia do łamania szyfrów (jak kryptoanaliza różnicowa, kryptoanaliza liniowa czy analiza statystyczna) od kilkunastu lat do tego celu zaprzęga się różnego rodzaju niedeterministyczne systemy inspirowane naturą. Użycie takich technik nie jest do końca intuicyjne – w kryptoanalizie często ważne jest znalezienie jednego konkretnego klucza (rozwiązania optymalnego), a każde inne rozwiązanie daje kiepskie rezultaty, nawet jeśli jest blisko optimum globalnego.

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Yadav, SanehLata, and Manu Phogat. "Study of Nature Inspired Algorithms." International Journal of Computer Trends and Technology 49, no. 2 (2017): 100–105. http://dx.doi.org/10.14445/22312803/ijctt-v49p115.

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Bujok, Petr, Josef Tvrdik, and Radka Polakova. "Nature-Inspired Algorithms in Real-World Optimization Problems." MENDEL 23, no. 1 (2017): 7–14. http://dx.doi.org/10.13164/mendel.2017.1.007.

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Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.

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Yazdani, Maziar, and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3, no. 1 (2015): 24–36. http://dx.doi.org/10.1016/j.jcde.2015.06.003.

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Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.

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Abualigah, Laith, Amir H. Gandomi, Mohamed Abd Elaziz, et al. "Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis." Algorithms 13, no. 12 (2020): 345. http://dx.doi.org/10.3390/a13120345.

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.

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Deepika, N., and O. S. Abdul Qadir. "A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment." Asian Journal of Computer Science and Technology 8, S2 (2019): 79–82. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2019.

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Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.

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Hussein, Eslam, Ahmed Ibrahem Hafez, Aboul Ella Hassanien, and Aly A. Fahmy. "Nature inspired algorithms for solving the community detection problem." Logic Journal of the IGPL 25, no. 6 (2017): 902–14. http://dx.doi.org/10.1093/jigpal/jzx043.

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Abstract Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

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Sasmita, Kumari Nayak. "Nature inspired algorithms in dynamic task scheduling: A review." World Journal of Advanced Research and Reviews 20, no. 3 (2023): 829–33. https://doi.org/10.5281/zenodo.12748311.

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The process of scheduling involves allocating shared resources gradually so that tasks can be completed effectively within the allotted time. In task scheduling and resource allocation, the terms are used independently for tasks and resources, respectively. In computer science and operational management, scheduling is a hot topic. Efficient schedules guarantee system effectiveness, facilitate sound decision-making, reduce resource waste and expenses, and augment total productivity. Selecting the most accurate resources to complete work items and schedules for computing and business process execution is typically a laborious task. Particularly in dynamic real-world systems, where scheduling different dynamic tasks involves multiple tasks, is a difficult problem. Emerging technology known as "nature inspired algorithms" has the ability to dynamically solve the problem of optimal task and resource scheduling. This review paper discusses a study that looked at algorithms inspired by nature and used them to schedule tasks dynamically. The Nature Inspired Algorithms used in dynamic task scheduling and a comparative analysis of those methods are used in this paper to address the study's findings.

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Zang, Hongnian, Shujun Zhang, and Kevin Hapeshi. "A Review of Nature-Inspired Algorithms." Journal of Bionic Engineering 7, S4 (2010): S232—S237. http://dx.doi.org/10.1016/s1672-6529(09)60240-7.

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Siddique, Nazmul, and Hojjat Adeli. "Nature-Inspired Chemical Reaction Optimisation Algorithms." Cognitive Computation 9, no. 4 (2017): 411–22. http://dx.doi.org/10.1007/s12559-017-9485-1.

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Priyadarshini, Ishaani. "Dendritic Growth Optimization: A Novel Nature-Inspired Algorithm for Real-World Optimization Problems." Biomimetics 9, no. 3 (2024): 130. http://dx.doi.org/10.3390/biomimetics9030130.

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In numerous scientific disciplines and practical applications, addressing optimization challenges is a common imperative. Nature-inspired optimization algorithms represent a highly valuable and pragmatic approach to tackling these complexities. This paper introduces Dendritic Growth Optimization (DGO), a novel algorithm inspired by natural branching patterns. DGO offers a novel solution for intricate optimization problems and demonstrates its efficiency in exploring diverse solution spaces. The algorithm has been extensively tested with a suite of machine learning algorithms, deep learning algorithms, and metaheuristic algorithms, and the results, both before and after optimization, unequivocally support the proposed algorithm’s feasibility, effectiveness, and generalizability. Through empirical validation using established datasets like diabetes and breast cancer, the algorithm consistently enhances model performance across various domains. Beyond its working and experimental analysis, DGO’s wide-ranging applications in machine learning, logistics, and engineering for solving real-world problems have been highlighted. The study also considers the challenges and practical implications of implementing DGO in multiple scenarios. As optimization remains crucial in research and industry, DGO emerges as a promising avenue for innovation and problem solving.

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Carreon-Ortiz, Hector, Fevrier Valdez, and Oscar Castillo. "A New Discrete Mycorrhiza Optimization Nature-Inspired Algorithm." Axioms 11, no. 8 (2022): 391. http://dx.doi.org/10.3390/axioms11080391.

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This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better.

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Karunanidy, Dinesh, Subramanian Ramalingam, Ankur Dumka, et al. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem." Mathematics 10, no. 5 (2022): 688. http://dx.doi.org/10.3390/math10050688.

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In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.

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Hatti, Daneshwari I., and Ashok V. Sutagundar. "Nature Inspired Computing for Wireless Networks Applications." International Journal of Applied Evolutionary Computation 10, no. 1 (2019): 1–29. http://dx.doi.org/10.4018/ijaec.2019010101.

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Nature inspired computing (NIC) is a computing paradigm inspired by the attractive behavior of nature. NIC has influenced the researchers to perform optimization in many approaches using physics/chemistry-based algorithms and biology-based algorithms. Physics/chemistry-based algorithms include the water cycle, a galaxy base, or gravitational-based algorithms. Biology-based algorithms, namely bio-inspired and swarm intelligence-related algorithms are discussed with their importance in the field of wireless networks. A wireless network such as MANET's, VANET, AdHoc, and IoT are playing a vital role in all sectors. Some of the issues such as finding the optimal path in routing, clustering, dynamic allocation of motes, energy and lifetime of the network pertaining to a wireless network can be solved using an NIC approach. Algorithms derived by the inspiration from nature are discussed briefly in this article.

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Bansal, Shonak, Neena Gupta, and Arun Kumar Singh. "Nature–inspired metaheuristic algorithms to find near–OGR sequences for WDM channel allocation and their performance comparison." Open Mathematics 15, no. 1 (2017): 520–47. http://dx.doi.org/10.1515/math-2017-0045.

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Abstract Nowadays, nature–inspired metaheuristic algorithms are most powerful optimizing algorithms for solving the NP–complete problems. This paper proposes three approaches to find near–optimal Golomb ruler sequences based on nature–inspired algorithms in a reasonable time. The optimal Golomb ruler (OGR) sequences found their application in channel–allocation method that allows suppression of the crosstalk due to four–wave mixing in optical wavelength division multiplexing systems. The simulation results conclude that the proposed nature–inspired metaheuristic optimization algorithms are superior to the existing conventional and nature–inspired algorithms to find near–OGRs in terms of ruler length, total optical channel bandwidth, computation time, and computational complexity. Based on the simulation results, the performance of proposed different nature–inspired metaheuristic algorithms are being compared by using statistical tests. The statistical test results conclude the superiority of the proposed nature–inspired optimization algorithms.

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Goudhaman, M. "Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm." International Journal of Engineering & Technology 7, no. 3 (2018): 1804. http://dx.doi.org/10.14419/ijet.v7i3.18.14616.

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In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. Many metaheuristic algorithms have been developed by inspiring various nature phenomena’s. Exploration and exploitation are distinctive capacities and confine each other, along these lines, customary calculations require numerous parameters and bunches of expenses to accomplish the adjust, and furthermore need to modify parameters for various enhancement issues. In this paper, another populace based algorithm, the Cheetah Chase Algorithm (CCA), is presented. Distinctive features of Cheetah and their characteristics has been the essential motivation for advancement of this optimization algorithm. Cheetah Chase Algorithm (CCA) has awesome capacities both in exploitation and exploration, is proposed to address these issues. To start with, CCA endeavours to locate the optimal solution in the assigned hunt territory. It at that point utilizes history data to pursue its prey. CCA can, hence, decide the situation of the worldwide ideal. CCA accomplishes solid exploitation and exploration with these highlights. Additionally, as indicated by various issues, CCA executes versatile parameter change. The self-examination and analysis of this exploration show that each CCA capacity can have different beneficial outcomes, while the execution correlation exhibits CCAs predominance over conventional metaheuristic algorithms. The proposed Cheetah Chase Algorithm is developed by the process of hunting and chasing of Cheetah to capture its prey with the parameters of high speed, velocity and greater accelerations.

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Reda, Ahmad, and Zsolt Csaba Johanyák. "Survey on five nature-Inspired Optimization Algorithms." Gradus 8, no. 1 (2021): 173–83. http://dx.doi.org/10.47833/2021.1.csc.001.

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Tekchandani, Prakash, and Aditya Trivedi. "Clock Drift Management Using Nature Inspired Algorithms." Journal of Information Technology Research 5, no. 4 (2012): 48–62. http://dx.doi.org/10.4018/jitr.2012100104.

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Time Synchronization is common requirement for most network applications. It is particularly essential in a Wireless Sensor Networks (WSNs) to allow collective signal processing, proper correlation of diverse measurements taken from a set of distributed sensor elements and for an efficient sharing of the communication channel. The Flooding Time Synchronization Protocol (FTSP) was developed explicitly for time synchronization of wireless sensor networks. In this paper, we optimized FTSP for clock drift management using Particle Swarm Optimization (PSO), Variant of PSO and Differential Evolution (DE). The paper estimates the clock offset, clock skew, generates linear line and optimizes the value of average time synchronization error using PSO, Variant of PSO and DE. In this paper we present implementation and experimental results that produces reduced average time synchronization error using PSO, Variant of PSO and DE, compared to that of linear regression used in FTSP.

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R B, Madhumala. "Virtual Machine Optimization using Nature Inspired Algorithms." International Journal for Research in Applied Science and Engineering Technology 8, no. 1 (2020): 90–96. http://dx.doi.org/10.22214/ijraset.2020.1016.

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Mello-Roman, Jorge Daniel, and Adolfo Hernandez. "KPLS Optimization With Nature-Inspired Metaheuristic Algorithms." IEEE Access 8 (2020): 157482–92. http://dx.doi.org/10.1109/access.2020.3019771.

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Shahab, Muhammad Luthfi, and Mohammad Isa Irawan. "Sequence Alignment Using Nature-Inspired Metaheuristic Algorithms." International Journal of Computing Science and Applied Mathematics 3, no. 1 (2017): 27. http://dx.doi.org/10.12962/j24775401.v3i1.2118.

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Kumar, Lalit, Manish Pandey, and Mitul Kumar Ahirwal. "A Survey on Parallel Nature Inspired Algorithms." Wireless Personal Communications 138, no. 3 (2024): 1893–918. http://dx.doi.org/10.1007/s11277-024-11584-4.

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Cecilia, José M., Andy Nisbet, Martyn Amos, José M. García, and Manuel Ujaldón. "Enhancing GPU parallelism in nature-inspired algorithms." Journal of Supercomputing 63, no. 3 (2012): 773–89. http://dx.doi.org/10.1007/s11227-012-0770-1.

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Tzanetos, Alexandros, Iztok Fister, and Georgios Dounias. "A comprehensive database of Nature-Inspired Algorithms." Data in Brief 31 (August 2020): 105792. http://dx.doi.org/10.1016/j.dib.2020.105792.

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Meera P. S. and Lavanya V. "Nature-Inspired Algorithms for Energy Management Systems." International Journal of Swarm Intelligence Research 14, no. 1 (2023): 1–16. http://dx.doi.org/10.4018/ijsir.319310.

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The electric grid is being increasingly integrated with renewable energy sources whose output is mostly fluctuating in nature. The load demand is also increasing day by day, mainly due to the increased interest in electric vehicles and other automated devices. An energy management system helps in maintaining the balance between the available generation and the load demand and thus optimizes the energy usage. It also helps in reducing the peak load, green-house gas emissions, and the operational cost. Energy management can be performed at different levels and is essential for realizing smart homes, smart buildings, and even smart grid. The different objectives considered for designing energy management systems are reduction of emissions, energy cost, operational cost, peak demand, etc. Many traditional and hybrid nature-inspired algorithms are used for optimizing these various objectives. This paper intends to give an overview about the various nature-inspired algorithms used for optimizing energy management systems in homes, buildings, and micro grid.

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Naik, Bighnaraj, Janmenjoy Nayak, and H. S. Behera. "FLANN + BHO." International Journal of Rough Sets and Data Analysis 5, no. 1 (2018): 13–33. http://dx.doi.org/10.4018/ijrsda.2018010102.

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Among some of the competent optimization algorithms, nature inspired algorithms are quite popular due to their flexibility and ease of use in diversified domains. Moreover, balancing between exploration and exploitation is one of the important aspects of nature inspired optimizations. In this paper, a recently developed nature inspired algorithm such as black hole algorithm has been used with the functional link neural network for handling the nonlinearity nature of system identification. Specifically, the proposed hybrid approach is used to solve classification problem. The results of the hybrid approach are compared with some of the other popular competent nature based approaches and found the superiority of the proposed method over others. Also, a brief discussion on the working principles of the black hole algorithm and its available literatures are discussed.

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Kumar, Abhijeet, Ankur Saini, Anushka Prajapati, and Pawan Mishra. "Advancing Multi-Robot Path Planning through Nature Inspired Algorithms." International Journal of Innovative Research in Advanced Engineering 11, no. 11 (2024): 813–18. https://doi.org/10.26562/ijirae.2024.v1111.04.

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In multi-robot systems, robots need to move efficiently without colliding with each other, which is called path planning. The challenge is to find the best way for multiple robots to reach their destinations while avoiding obstacles and each other. Traditional methods can struggle with complex environments, so researchers are turning to nature-inspired computing and machine learning algorithms to solve this problem. Nature-inspired algorithms, like those based on the behaviour of ants, bees, or birds, Firefly Algorithm (FA)[1], Bat Algorithm [2]help robots make decisions similar to how animals navigate in the natural world. These techniques can optimize robot paths by mimicking processes found in nature, such as how ants find the shortest route to food or how birds flock together without crashing [3]. Machine learning algorithms, on the other hand, enable robots to learn from their environment and past experiences [4]. By continuously learning, robots can adapt and improve their path planning over time [5]. Combining these two approaches – nature-inspired computing and machine learning – offers a powerful way to tackle the complex problem of multi-robot path planning. This study explores how these advanced techniques can work together to make robot teams more efficient, reducing travel time, avoiding collisions, and navigating through dynamic environments.

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Valdez, Fevrier, Oscar Castillo, and Patricia Melin. "Bio-Inspired Algorithms and Its Applications for Optimization in Fuzzy Clustering." Algorithms 14, no. 4 (2021): 122. http://dx.doi.org/10.3390/a14040122.

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In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering.

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Sindhuja, P., P. Ramamoorthy, and M. Suresh Kumar. "A Brief Survey on Nature Inspired Algorithms: Clever Algorithms for Optimization." Asian Journal of Computer Science and Technology 7, no. 1 (2018): 27–32. http://dx.doi.org/10.51983/ajcst-2018.7.1.1835.

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This paper presents a brief survey on various optimization algorithms. To be more precise, the paper elaborates on clever Algorithms – a class of Nature inspired Algorithms. The Nature Inspired Computing (NIC) is an emerging area of research that focuses on Physics and Biology Based approach to the Algorithms for optimization. The Algorithms briefed in this paper have understood, explained, adapted and replicated the phenomena of Nature to replicate them in the artificial systems. This Cross – fertilisation of Nature Inspired Computing (NIC) and Computational Intelligence (CI) will definitely provide optimal solutions to existing problems and also open up new arenas in Research and Development. This paper briefs the classification of clever algorithms and the key strategies employed for optimization.

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Virk, Amandeep Kaur, and Kawaljeet Singh. "Solving Two-Dimensional Rectangle Packing Problem Using Nature-Inspired Metaheuristic Algorithms." Journal of Industrial Integration and Management 03, no. 02 (2018): 1850009. http://dx.doi.org/10.1142/s2424862218500094.

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This paper applies cuckoo search and bat metaheuristic algorithms to solve two-dimensional non-guillotine rectangle packing problem. These algorithms have not been found to be used before in the literature to solve this important industrial problem. The purpose of this work is to explore the potential of these new metaheuristic methods and to check whether they can contribute in enhancing the performance of this problem. Standard benchmark test data has been used to solve the problem. The performance of these algorithms was measured and compared with genetic algorithm and tabu search techniques which can be found to be used widely in the literature to solve this problem. Good optimal solutions were obtained from all the techniques and the new metaheuristic algorithms performed better than genetic algorithm and tabu search. It was seen that cuckoo search algorithm excels in performance as compared to the other techniques.

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Patil, Ramakant S., Sharad P. Jadhav, and Machhindranath D. Patil. "Review of Intelligent and Nature-Inspired Algorithms-Based Methods for Tuning PID Controllers in Industrial Applications." Journal of Robotics and Control (JRC) 5, no. 2 (2024): 336–58. https://doi.org/10.18196/jrc.v5i2.20850.

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PID controllers can regulate and stabilize processes in response to changes and disturbances. This paper provides a comprehensive review of PID controller tuning methods for industrial applications, emphasizing intelligent and nature-inspired algorithms. Techniques such as Fuzzy Logic (FL), Artificial Neural Networks (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS) are explored. Additionally, nature-inspired algorithms, including evolutionary algorithms like Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Simulated Annealing (SA), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Cuckoo Search (CS), Harmony Search (HS), and Grey Wolf Optimization (GWO), are examined. While conventional PID tuning methods are valuable, the evolving landscape of control engineering has led to the exploration of intelligent and nature-inspired algorithms to further enhance PID controller performance in specific applications. The study conducts a thorough analysis of these tuning methods, evaluating their effectiveness in industrial applications through a comprehensive literature review. The primary aim is to offer empirical evidence on the efficacy of various algorithms in PID tuning. This work presents a comparative analysis of algorithmic performance and their real-world applications, contributing to a comprehensive understanding of the discussed tuning methods. Findings aim to uncover the strengths and weaknesses of diverse PID tuning methods in industrial contexts, guiding practitioners and researchers. This paper is a sincere effort to address the lack of specific quantitative comparisons in existing literature, bridging the gap in empirical evidence and serving as a valuable reference for optimizing intelligent and nature-inspired algorithms-based PID controllers in various industrial applications. Keywords— PID controller; Intelligent and Nature-Inspired Algorithms; Fuzzy Logic; Artificial Neural Network; Adaptive NeuroFuzzy Inference System; Genetic Algorithm; Particle Swarm Optimization; Differential Evolution; Ant Colony Optimization; Simulated Annealing; Artificial Bee Colony; Firefly Algorithm; Cuckoo Search; Harmony Search; Grey Wolf Optimization.

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Pleszczyński, Mariusz, Adam Zielonka, and Marcin Woźniak. "Application of Nature-Inspired Algorithms to Computed Tomography with Incomplete Data." Symmetry 14, no. 11 (2022): 2256. http://dx.doi.org/10.3390/sym14112256.

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This paper discusses and compares several computed tomography (CT) algorithms capable of dealing with incomplete data. This type of problem has been proposed for a symmetrical grid and symmetrically distributed transmitters and receivers. The use of symmetry significantly speeds up the process of constructing a system of equations that is the foundation of all CT algebraic algorithms. Classic algebraic approaches are effective in incomplete data scenarios, but suffer from low convergence speed. For this reason, we propose the use of nature-inspired algorithms which are proven to be effective in many practical optimization problems from various domains. The efficacy of nature-inspired algorithms strongly depends on the number of parameters they maintain and reproduce, and this number is usually substantial in the case of CT applications. However, taking into account the specificity of the reconstructed object allows to reduce the number of parameters and effectively use heuristic algorithms in the field of CT. This paper compares the efficacy and suitability of three nature-inspired heuristic algorithms: Artificial BeeColony (ABC), Ant Colony Optimization (ACO), and Clonal Selection Algorithm (CSA) in the CT context, showing their advantages and weaknesses. The best algorithm is identified and some ideas of how the remaining methods could be improved so as to better solve CT tasks are presented.

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Chmiel, W., P. Kadłuczka, J. Kwiecień, and B. Filipowicz. "A comparison of nature inspired algorithms for the quadratic assignment problem." Bulletin of the Polish Academy of Sciences Technical Sciences 65, no. 4 (2017): 513–22. http://dx.doi.org/10.1515/bpasts-2017-0056.

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AbstractThis paper presents an application of the ant algorithm and bees algorithm in optimization of QAP problem as an example of NP-hard optimization problem. The experiments with two types of algorithms: the bees algorithm and the ant algorithm were performed for the test instances of the quadratic assignment problem from QAPLIB, designed by Burkard, Karisch and Rendl. On the basis of the experiments results, an influence of particular elements of algorithms, including neighbourhood size and neighbourhood search method, will be determined.

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Yusup, Norfadzlan, Izzatul Nabila Sarbini, Dayang Nurfatimah Awang Iskandar, Azlan Mohd Zain, and Didik Dwi Prasetya. "Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection." Journal of Advanced Research in Applied Sciences and Engineering Technology 56, no. 1 (2024): 1–12. https://doi.org/10.37934/araset.56.1.112.

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This research paper explores the performance of binary nature-inspired optimization algorithms as feature selection to enhance the identification of human activities using wearable technology. Utilization of nature-inspired algorithms for feature selection, as documented in scholarly literature, presents a promising opportunity to enhance machine learning and data analysis tasks, given their effectiveness in identifying relevant features, resulting in models with reduced computational complexity, improved predictive accuracy and easier interpretation. In the experiment, we conducted an evaluation of the effectiveness and efficiency of four nature-inspired binary algorithms for optimization namely Binary Particle Swarm Optimization (BPSO), Binary Grey Wolf Optimization algorithm (BGWO), Binary Differential Evolution algorithm (BDE), and Binary Salp Swarm algorithm (BSS) - in the context of human activity recognition (HAR). The outcomes of this comprehensive experimentation, conducted on two distinct human activity recognition (HAR) datasets, provide valuable insights. BPSO algorithm emerges as an adaptable and well-rounded performer, achieving a competitive balance between feature selection quality and computational efficiency in SBHAR dataset. Conversely, for the PAMAP2 dataset, BDE algorithm displays superior feature selection quality and BPSO algorithm maintains competitive performance and adaptability. In both datasets, the nature-inspired optimization algorithms have achieved remarkable feature reduction, demonstrating reductions of 48% and 50% respectively. The experiment results show how these algorithms could be used to improve methods for recognizing human activities using wearables technology, such as feature selection, parameter adjustment, and model optimization.

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Alexandros, Tzanetos, and Dounias Georgios. "Nature Inspired Optimization Algorithms Related to Physical Phenomena and Laws of Science: A Survey." International Journal on Artificial Intelligence Tools 26, no. 06 (2017): 1750022. http://dx.doi.org/10.1142/s0218213017500221.

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In the last decade a new variety of nature inspired optimization algorithms has been appeared. After the swarm based models, researchers turned their inspiration in nature phenomena and laws of science. In this way a new category of algorithms was born, equally effective or even sometimes superior to known algorithms for optimization problems, like genetic algorithms and swarm intelligence schemes. The present survey depicts the evolution of research on nature inspired optimization algorithms related to physical phenomena and laws of science and discusses the possibilities of using the presented approaches in a number of different applications. An attempt has been made to draw conclusions on what algorithm could be used in which different problem areas, for those approaches that this information could be extracted from the related papers studied. The paper also underlines the usage of this kind of nature inspired algorithms in industrial research problems, due to their better confrontation with optimization problems represented with nodes and edges.

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Sikdar, Subinoy, Sagnik Dutta, and Malay Kule. "On Cryptanalysis of 3-DES using Nature-Inspired Algorithms." International Journal of Computer Network and Information Security 17, no. 3 (2025): 54–71. https://doi.org/10.5815/ijcnis.2025.03.04.

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This paper presents a novel cryptanalysis method of DES (2-DES and 3-DES) using nature-inspired algorithms; namely Cuckoo Search Algorithm and Grey Wolf Optimization Algorithm. We have shown the loophole of 2-DES and 3-DES encryption systems and discovered the vulnerabilities by some simple mathematical calculations. The Meet-In-The-Middle approach can be executed on 2-DES along with Known Plaintext Attack, Chosen Plaintext Attack, and Chosen Ciphertext Attack. The valid key pairs along with the original key pairs can successfully be recovered by this attack algorithm. But in the Ciphertext Only Attack, the Meet-In-The-Middle approach fails to recover the plaintext as well as the valid key pairs both for 2-DES and 3-DES. To overcome this problem, we have proposed a novel cryptanalysis method of 3-DES with Ciphertext Only Attack using Cuckoo Search Algorithm and Grey Wolf Optimization Algorithm (GWO). We have developed a suitable fitness function, accelerating the algorithm toward the optimal solution. This paper shows how CSA and GWO can break a 3-DES cryptosystem using a Ciphertext Only Attack. This proposed cryptanalysis method can also be applied to any round of DES.

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Nur Maghfiroh, Meilinda Fitriani, Anak Agung Ngurah Perwira Redi, Janice Ong, and Muhamad Rausyan Fikri. "Cuckoo search algorithm for construction site layout planning." IAES International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 851. http://dx.doi.org/10.11591/ijai.v12.i2.pp851-860.

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<span lang="EN-US">A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature-inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.</span>

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Meilinda, Fitriani Nur Maghfiroh, Agung Ngurah Perwira Redi Anak, Ong Janice, and Rausyan Fikri Muhamad. "Cuckoo search algorithm for construction site layout planning." International Journal of Artificial Intelligence (IJ-AI) 12, no. 2 (2023): 851–60. https://doi.org/10.11591/ijai.v12.i2.pp851-860.

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A novel metaheuristic optimization algorithm based on cuckoo search algorithm (CSA) is presented to solve the construction site layout planning problem (CSLP). CSLP is a complex optimization problem with various applications, such as plant layout, construction site layout, and computer chip layout. Many researchers have investigated the CSLP by applying many algorithms in an exact or heuristic approach. Although both methods yield a promising result, technically, nature-inspired algorithms demonstrate high achievement in successful percentage. In the last two decades, researchers have been developing a new nature-inspired algorithm for solving different types of optimization problems. The CSA has gained popularity in resolving large and complex issues with promising results compared with other nature inspired algorithms. However, for solving CSLP, the algorithm based on CSA is still minor. Thus, this study proposed CSA with additional modification in the algorithm mechanism, where the algorithm shows a promising result and can solve CSLP cases.

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Dehghani, Mohammad, Štěpán Hubálovský, and Pavel Trojovský. "Cat and Mouse Based Optimizer: A New Nature-Inspired Optimization Algorithm." Sensors 21, no. 15 (2021): 5214. http://dx.doi.org/10.3390/s21155214.

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Numerous optimization problems designed in different branches of science and the real world must be solved using appropriate techniques. Population-based optimization algorithms are some of the most important and practical techniques for solving optimization problems. In this paper, a new optimization algorithm called the Cat and Mouse-Based Optimizer (CMBO) is presented that mimics the natural behavior between cats and mice. In the proposed CMBO, the movement of cats towards mice as well as the escape of mice towards havens is simulated. Mathematical modeling and formulation of the proposed CMBO for implementation on optimization problems are presented. The performance of the CMBO is evaluated on a standard set of objective functions of three different types including unimodal, high-dimensional multimodal, and fixed-dimensional multimodal. The results of optimization of objective functions show that the proposed CMBO has a good ability to solve various optimization problems. Moreover, the optimization results obtained from the CMBO are compared with the performance of nine other well-known algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), and Teamwork Optimization Algorithm (TOA). The performance analysis of the proposed CMBO against the compared algorithms shows that CMBO is much more competitive than other algorithms by providing more suitable quasi-optimal solutions that are closer to the global optimal.

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Kozdrowski, Stanisław, Mateusz Żotkiewicz, Kacper Wnuk, Arkadiusz Sikorski, and Sławomir Sujecki. "A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design." Applied Sciences 10, no. 19 (2020): 6840. http://dx.doi.org/10.3390/app10196840.

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The subject of the study was an application of nature-inspired metaheuristic algorithms to node configuration optimization in optical networks. The main objective of the optimization was to minimize capital expenditure, which includes the costs of optical node resources, such as transponders and amplifiers used in a new generation of optical networks. For this purpose a model that takes into account the physical phenomena in the optical network is proposed. Selected nature-inspired metaheuristic algorithms were implemented and compared with a reference, deterministic algorithm, based on linear integer programming. For the cases studied the obtained results show that there is a large advantage in using metaheuristic algorithms. In particular, the evolutionary algorithm, the bees algorithm and the harmony search algorithm showed superior performance for the considered data-sets corresponding to large optical networks; the integer programming-based algorithm failed to find an acceptable sub-optimal solution within the assumed maximum computational time. All optimization methods were compared for selected instances of realistic teletransmission networks of different dimensions subject to traffic demand sets extracted from real traffic data.

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Xu, Yiqi, Qiongqiong Li, Xuan Xu, Jiafu Yang, and Yong Chen. "Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning." Electronics 12, no. 15 (2023): 3263. http://dx.doi.org/10.3390/electronics12153263.

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The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

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Templos-Santos, Juan, Omar Aguilar-Mejia, Edgar Peralta-Sanchez, and Raul Sosa-Cortez. "Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms." Algorithms 12, no. 3 (2019): 54. http://dx.doi.org/10.3390/a12030054.

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This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms.

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Dehghani, Moslem, Mokhtar Aly, Jose Rodriguez, Ehsan Sheybani, and Giti Javidi. "A Novel Nature-Inspired Optimization Algorithm: Grizzly Bear Fat Increase Optimizer." Biomimetics 10, no. 6 (2025): 379. https://doi.org/10.3390/biomimetics10060379.

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This paper introduces a novel nature-inspired optimization algorithm called the Grizzly Bear Fat Increase Optimizer (GBFIO). The GBFIO algorithm mimics the natural behavior of grizzly bears as they accumulate body fat in preparation for winter, drawing on their strategies of hunting, fishing, and eating grass, honey, etc. Hence, three mathematical steps are modeled and considered in the GBFIO algorithm to solve the optimization problem: (1) finding food sources (e.g., vegetables, fruits, honey, oysters), based on past experiences and olfactory cues; (2) hunting animals and protecting offspring from predators; and (3) fishing. Thirty-one standard benchmark functions and thirty CEC2017 test benchmark functions are applied to evaluate the performance of the GBFIO, such as unimodal, multimodal of high dimensional, fixed dimensional multimodal, and also the rotated and shifted benchmark functions. In addition, four constrained engineering design problems such as tension/compression spring design, welded beam design, pressure vessel design, and speed reducer design problems have been considered to show the efficiency of the proposed GBFIO algorithm in solving constrained problems. The GBFIO can successfully solve diverse kinds of optimization problems, as shown in the results of optimization of objective functions, especially in high dimension objective functions in comparison to other algorithms. Additionally, the performance of the GBFIO algorithm has been compared with the ability and efficiency of other popular optimization algorithms in finding the solutions. In comparison to other optimization algorithms, the GBFIO algorithm offers yields superior or competitive quasi-optimal solutions relative to other well-known optimization algorithms.

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Naseri, Narjes Khatoon, Elankovan A. Sundararajan, Masri Ayob, and Amin Jula. "Smart Root Search (SRS): A Novel Nature-Inspired Search Algorithm." Symmetry 12, no. 12 (2020): 2025. http://dx.doi.org/10.3390/sym12122025.

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In this paper, a novel heuristic search algorithm called Smart Root Search (SRS) is proposed. SRS employs intelligent foraging behavior of immature, mature and hair roots of plants to explore and exploit the problem search space simultaneously. SRS divides the search space into several subspaces. It thereupon utilizes the branching and drought operations to focus on richer areas of promising subspaces while extraneous ones are not thoroughly ignored. To achieve this, the smart reactions of the SRS model are designed to act based on analyzing the heterogeneous conditions of various sections of different search spaces. In order to evaluate the performance of the SRS, it was tested on a set of known unimodal and multimodal test functions. The results were then compared with those obtained using genetic algorithms, particle swarm optimization, differential evolution and imperialist competitive algorithms and then analyzed statistically. The results demonstrated that the SRS outperformed comparative algorithms for 92% and 82% of the investigated unimodal and multimodal test functions, respectively. Therefore, the SRS is a promising nature-inspired optimization algorithm.

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Ouadfel, Salima, and Souham Meshoul. "Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding." International Journal of Applied Metaheuristic Computing 5, no. 4 (2014): 47–69. http://dx.doi.org/10.4018/ijamc.2014100103.

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Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding (AMT) problem. The goal is to determine the correct number of thresholds as well as their optimal values. For this purpose, the article adopts—for each metaheuristic—a new hybrid coding scheme such that each individual solution is represented by two parts: a real part which represents the thresholds values and a binary part which indicates if a given threshold will be used or not during the thresholding process. Experiments have been conducted on six real test images and the results have been compared with two automatic multilevel thresholding based PSO methods and the exhaustive search method for fair comparison. Empirical results reveal that AMT-HABC and AMT-HCS algorithms performed equally to the solution provided by the exhaustive search and are better than the other comparison algorithms. In addition, the results indicate that the ATM-HABC algorithm has a higher success rate and a speed convergence than the other metaheuristics.

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Sachan, Rohit Kumar, and Dharmender Singh Kushwaha. "A Generalized and Robust Anti-Predatory Nature-Inspired Algorithm for Complex Problems." International Journal of Applied Metaheuristic Computing 10, no. 1 (2019): 75–91. http://dx.doi.org/10.4018/ijamc.2019010105.

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This article describes how nature-inspired algorithms (NIAs) have evolved as efficient approaches for addressing the complexities inherent in the optimization of real-world applications. These algorithms are designed to imitate processes in nature that provide some ways of problem solving. Although various nature-inspired algorithms have been proposed by various researchers in the past, a robust and computationally simple NIA is still missing. A novel nature-inspired algorithm that adapts to the anti-predatory behavior of the frog is proposed. The algorithm mimics the self defense mechanism of a frog. Frogs use their reflexes as a means of protecting themselves from the predators. A mathematical formulation of these reflexes forms the core of the proposed approach. The robustness of the proposed algorithm is verified through performance evaluation on sixteen different unconstrained mathematical benchmark functions based on best and worst values as well as mean and standard deviation of the computed results. These functions are representative of different properties and characteristics of the problem domain. The strength and robustness of the proposed algorithm is established through a comparative result analysis with six well-known optimization algorithms, namely: genetic, particle swarm, differential evolution, artificial bee colony, teacher learning and Jaya. The Friedman rank test and the Holm-Sidak test have been used for statistical analysis of obtained results. The proposed algorithm ranks first in the case of mean result and scores second rank in the case of “standard deviation”. This proves the significance of the proposed algorithm.

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Journal articles on the topic 'Nature-Inspired Algorithms'

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