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Статті в журналах з теми "Optimization algorithms"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 25 липня 2025

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1

Celik, Yuksel, and Erkan Ulker. "An Improved Marriage in Honey Bees Optimization Algorithm for Single Objective Unconstrained Optimization." Scientific World Journal 2013 (2013): 1–11. http://dx.doi.org/10.1155/2013/370172.

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Marriage in honey bees optimization (MBO) is a metaheuristic optimization algorithm developed by inspiration of the mating and fertilization process of honey bees and is a kind of swarm intelligence optimizations. In this study we propose improved marriage in honey bees optimization (IMBO) by adding Levy flight algorithm for queen mating flight and neighboring for worker drone improving. The IMBO algorithm’s performance and its success are tested on the well-known six unconstrained test functions and compared with other metaheuristic optimization algorithms.

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2

Luan, Yuxuan, Junjiang He, Jingmin Yang, Xiaolong Lan, and Geying Yang. "Uniformity-Comprehensive Multiobjective Optimization Evolutionary Algorithm Based on Machine Learning." International Journal of Intelligent Systems 2023 (November 10, 2023): 1–21. http://dx.doi.org/10.1155/2023/1666735.

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When solving real-world optimization problems, the uniformity of Pareto fronts is an essential strategy in multiobjective optimization problems (MOPs). However, it is a common challenge for many existing multiobjective optimization algorithms due to the skewed distribution of solutions and biases towards specific objective functions. This paper proposes a uniformity-comprehensive multiobjective optimization evolutionary algorithm based on machine learning to address this limitation. Our algorithm utilizes uniform initialization and self-organizing map (SOM) to enhance population diversity and

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3

Wen, Xiaodong, Xiangdong Liu, Cunhui Yu, et al. "IOOA: A multi-strategy fusion improved Osprey Optimization Algorithm for global optimization." Electronic Research Archive 32, no. 3 (2024): 2033–74. http://dx.doi.org/10.3934/era.2024093.

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<abstract><p>With the widespread application of metaheuristic algorithms in engineering and scientific research, finding algorithms with efficient global search capabilities and precise local search performance has become a hot topic in research. The osprey optimization algorithm (OOA) was first proposed in 2023, characterized by its simple structure and strong optimization capability. However, practical tests have revealed that the OOA algorithm inevitably encounters common issues faced by metaheuristic algorithms, such as the tendency to fall into local optima and reduced populat

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4

Jay, Kishore Sahani, and Kumar Yadav Arvind. "The Bees Algorithms in Optimization: An Overview." MATHEMATICS EDUCATION LV, no. 3, September 2021 (2021): 20–28. https://doi.org/10.5281/zenodo.7275730.

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Анотація:

            Metaheuristic algorithms have become powerful tools for modeling and optimization. In this article, we provide an overview of Bee Algorithms and their applications. We will briefly introduce algorithms such as bee algorithms, virtual bee algorithm, artificial bee algorithm, bee mating algorithm, etc. We also briefly the main characteristics of these algorithms and outline some recent applications of these algorithms. 

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5

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 alg

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6

Kim, Minsu, Areum Han, Jaewon Lee, Sunghyun Cho, Il Moon, and Jonggeol Na. "Comparison of Derivative-Free Optimization: Energy Optimization of Steam Methane Reforming Process." International Journal of Energy Research 2023 (June 3, 2023): 1–20. http://dx.doi.org/10.1155/2023/8868540.

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In modern chemical engineering, various derivative-free optimization (DFO) studies have been conducted to identify operating conditions that maximize energy efficiency for efficient operation of processes. Although DFO algorithm selection is an essential task that leads to successful designs, it is a nonintuitive task because of the uncertain performance of the algorithms. In particular, when the system evaluation cost or computational load is high (e.g., density functional theory and computational fluid dynamics), selecting an algorithm that quickly converges to the near-global optimum at the

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7

Gireesha. B, Mr, and . "A Literature Survey on Artificial Swarm Intelligence based Optimization Techniques." International Journal of Engineering & Technology 7, no. 4.5 (2018): 455. http://dx.doi.org/10.14419/ijet.v7i4.5.20205.

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From few decades’ optimizations techniques plays a key role in engineering and technological field applications. They are known for their behaviour pattern for solving modern engineering problems. Among various optimization techniques, heuristic and meta-heuristic algorithms proved to be efficient. In this paper, an effort is made to address techniques that are commonly used in engineering applications. This paper presents a basic overview of such optimization algorithms namely Artificial Bee Colony (ABC) Algorithm, Ant Colony Optimization (ACO) Algorithm, Fire-fly Algorithm (FFA) and Particle

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8

A., Hanif Halim, and Ismail I. "Tree Physiology Optimization in Constrained Optimization Problem." TELKOMNIKA Telecommunication, Computing, Electronics and Control 16, no. 2 (2018): 876–82. https://doi.org/10.12928/TELKOMNIKA.v16i2.9021.

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Metaheuristic algorithms are proven to be more effective on finding global optimum in numerous problems including the constrained optimization area. The algorithms have the capacity to prevail over many deficiencies in conventional algorithms. Besides of good quality of performance, some metaheuristic algorithms have limitations that may deteriorate by certain degree of difficulties especially in real-world application. Most of the real-world problems consist of constrained problem that is significantly important in modern engineering design and must be considered in order to perform any optim

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9

RAO, Xiong, Run DU, Wenming CHENG, and Yi YANG. "Modified proportional topology optimization algorithm for multiple optimization problems." Mechanics 30, no. 1 (2024): 36–45. http://dx.doi.org/10.5755/j02.mech.34367.

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Three modified proportional topology optimization (MPTO) algorithms are presented in this paper, which are named MPTOc, MPTOs and MPTOm, respectively. MPTOc aims to address the minimum compliance problem with volume constraint, MPTOs aims to solve the minimum volume fraction problem under stress constraint, and MPTOm aims to tackle the minimum volume fraction problem under compliance and stress constraints. In order to get rid of the shortcomings of the original proportional topology optimization (PTO) algorithm and improve the comprehensive performance of the PTO algorithm, the proposed algor

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10

Arıcı, FerdaNur, and Ersin Kaya. "Comparison of Meta-heuristic Algorithms on Benchmark Functions." Academic Perspective Procedia 2, no. 3 (2019): 508–17. http://dx.doi.org/10.33793/acperpro.02.03.41.

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Анотація:

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolu

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11

Acherjee, Bappa, Debanjan Maity, and Arunanshu S. Kuar. "Ultrasonic Machining Process Optimization by Cuckoo Search and Chicken Swarm Optimization Algorithms." International Journal of Applied Metaheuristic Computing 11, no. 2 (2020): 1–26. http://dx.doi.org/10.4018/ijamc.2020040101.

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The ultrasonic machining (USM) process has been analyzed in the present study to obtain the desired process responses by optimizing machining parameters using cuckoo search (CS) and chicken swarm optimization (CSO), two powerful nature-inspired, population and swarm-intelligence-based metaheuristic algorithms. The CS and CSO algorithms have been compared with other non-conventional optimization techniques in terms of optimal results, convergence, accuracy, and computational time. It is found that CS and CSO algorithms predict superior single and multi-objective optimization results than gravit

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12

Alfarhisi, Zikrie Pramudia, Hadi Suyono, and Fakhriy Hario Partiansyah. "4G LTE Network Coverage Optimization Using Metaheuristic Approach." International Journal of Computer Applications Technology and Researc 10, no. 01 (2021): 010–13. http://dx.doi.org/10.7753/ijcatr1001.1003.

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The main focus of this paper is to optimize the coverage of each 4G LTE network cell within the service area. There are many algorithms can be implemented to determine the optimal 4G LTE coverage area including the deterministic and heuristic approaches. The deterministic approach could solve accurately the optimization problem but need more resources and time consuming to determine the convergence parameters. Therefore, the heuristic approaches were introduced to improve the deterministic approach drawback. The methods used are the Differential Evolution Algorithm (DEA) and Adaptive Mutation

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13

Si, Binghui, Feng Liu, and Yanxia Li. "Metamodel-Based Hyperparameter Optimization of Optimization Algorithms in Building Energy Optimization." Buildings 13, no. 1 (2023): 167. http://dx.doi.org/10.3390/buildings13010167.

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Building energy optimization (BEO) is a promising technique to achieve energy efficient designs. The efficacy of optimization algorithms is imperative for the BEO technique and is significantly dependent on the algorithm hyperparameters. Currently, studies focusing on algorithm hyperparameters are scarce, and common agreement on how to set their values, especially for BEO problems, is still lacking. This study proposes a metamodel-based methodology for hyperparameter optimization of optimization algorithms applied in BEO. The aim is to maximize the algorithmic efficacy and avoid the failure of

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14

T, Mathi Murugan, and Baburaj E. "COMPARISON OF HYBRID ELEPHANT HERDING OPTIMIZATION WITH DIFFERENT EVOLUTIONARY OPTIMIZATION ALGORITHMS." ICTACT Journal on Soft Computing 10, no. 4 (2020): 2171–82. https://doi.org/10.21917/ijsc.2020.0309.

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Many optimization algorithms that imitate the social behaviour of animals and natural biological evolution have been proposed in the recently preceding years. These nature inspired algorithms known as evolutionary algorithms have considerably enhanced the development of the optimization process. In this paper, a hybrid elephant herding opposition algorithm is proposed and a comparative study is conducted to analyse the effectiveness of the proposed algorithm. For the purpose of the comparison, the optimization algorithms that have been taken up for the study are Refined Selfish Herd Optimizati

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15

Zou, Tingting, and Changyu Wang. "Adaptive Relative Reflection Harris Hawks Optimization for Global Optimization." Mathematics 10, no. 7 (2022): 1145. http://dx.doi.org/10.3390/math10071145.

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The Harris Hawks optimization (HHO) is a population-based metaheuristic algorithm; however, it has low diversity and premature convergence in certain problems. This paper proposes an adaptive relative reflection HHO (ARHHO), which increases the diversity of standard HHO, alleviates the problem of stagnation of local optimal solutions, and improves the search accuracy of the algorithm. The main features of the algorithm define nonlinear escape energy and adaptive weights and combine adaptive relative reflection with the HHO algorithm. Furthermore, we prove the computational complexity of the AR

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16

Hao, Shangqing, Xuewen Wang, Jiacheng Xie, and Zhaojian Yang. "Rigid framework section parameter optimization and optimization algorithm research." Transactions of the Canadian Society for Mechanical Engineering 43, no. 3 (2019): 398–404. http://dx.doi.org/10.1139/tcsme-2018-0085.

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This article compares the optimization algorithms included with ANSYS Software for optimizing the dimensions of a large steel framework to minimize weight while maintaining stiffness. A finite element model of the structure was prepared, and the section parameters were optimized using the sub-problem and first-order algorithms. These reduce the weight of the structure by 33.8%. The sub-problem algorithm and the first-order algorithm are explained from the rationale, iteration method, and convergence criterion. According to the optimized result, these two algorithms were compared. The results s

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17

Cui-Cui Cai, Cui-Cui Cai, Mao-Sheng Fu Cui-Cui Cai, Xian-Meng Meng Mao-Sheng Fu, Qi-Jian Wang Xian-Meng Meng, and Yue-Qin Wang Qi-Jian Wang. "Modified Harris Hawks Optimization Algorithm with Multi-strategy for Global Optimization Problem." 電腦學刊 34, no. 6 (2023): 091–105. http://dx.doi.org/10.53106/199115992023123406007.

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<p>As a novel metaheuristic algorithm, the Harris Hawks Optimization (HHO) algorithm has excellent search capability. Similar to other metaheuristic algorithms, the HHO algorithm has low convergence accuracy and easily traps in local optimal when dealing with complex optimization problems. A modified Harris Hawks optimization (MHHO) algorithm with multiple strategies is presented to overcome this defect. First, chaotic mapping is used for population initialization to select an appropriate initiation position. Then, a novel nonlinear escape energy update strategy is presented to control t

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18

Liang, Jianhui, Lifang Wang, and Miao Ma. "An Adaptive Dual-Population Collaborative Chicken Swarm Optimization Algorithm for High-Dimensional Optimization." Biomimetics 8, no. 2 (2023): 210. http://dx.doi.org/10.3390/biomimetics8020210.

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With the development of science and technology, many optimization problems in real life have developed into high-dimensional optimization problems. The meta-heuristic optimization algorithm is regarded as an effective method to solve high-dimensional optimization problems. However, considering that traditional meta-heuristic optimization algorithms generally have problems such as low solution accuracy and slow convergence speed when solving high-dimensional optimization problems, an adaptive dual-population collaborative chicken swarm optimization (ADPCCSO) algorithm is proposed in this paper,

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19

Wu, Xingtao, Yunfei Ding, Lin Wang, and Hongwei Zhang. "A Multi-Strategy Adaptive Coati Optimization Algorithm for Constrained Optimization Engineering Design Problems." Biomimetics 10, no. 5 (2025): 323. https://doi.org/10.3390/biomimetics10050323.

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Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporat

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20

Manikandan, K., and B. Sudhakar. "Hybrid Optimization Algorithm for Multi-level Image Thresholding Using Salp Swarm Optimization Algorithm and Ant Colony Optimization." International Journal of Electrical and Electronic Engineering & Telecommunications 12, no. 6 (2023): 411–23. http://dx.doi.org/10.18178/ijeetc.12.6.411-423.

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The process of identifying optimal threshold for multi-level thresholding in image segmentation is a challenging process. An efficient optimization algorithm is required to find the optimal threshold and various nature inspired; evolutionary optimization algorithms are presented by the research community. However, to improve the performance in finding optimal threshold value and minimize the error, reduces the searching time a hybrid optimization algorithm is presented in this research work using salp swarm optimization and ant colony optimization algorithm. The ant colony optimization algorit

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21

Okindo, Geoffrey, Prof George Kamucha, and Dr Nicholas Oyie. "Dynamic Optimization in 5G Network Slices: A Comparative Study of Whale Optimization, Particle Swarm Optimization, and Genetic Algorithm." International Journal of Electrical and Electronics Research 12, no. 3 (2024): 849–62. http://dx.doi.org/10.37391/ijeer.120316.

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This study presents a comprehensive framework for optimizing 5G network slices using metaheuristic algorithms, focusing on Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine Type Communications (mMTC) scenarios. The initial setup involves a MATLAB-based 5G New Radio (NR) Physical Downlink Shared Channel (PDSCH) simulation and OpenAir-Interface (OAI) 5G network testbed, utilizing Ubuntu 22.04 Long Term Support (LTS), MicroStack, Open-Source MANO (OSM), and k3OS to create a versatile testing environment. Key network parameters are identified

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22

Koulianos, Athanasios, Antonios Litke, and Nikolaos K. Papadakis. "A Hybrid Whale Optimization Approach for Fast-Convergence Global Optimization." Journal of Experimental and Theoretical Analyses 3, no. 2 (2025): 17. https://doi.org/10.3390/jeta3020017.

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In this paper, we introduce the Levy Flight-enhanced Whale Optimization Algorithm with Tabu Search elements (LWOATS), an innovative hybrid optimization approach that enhances the standard Whale Optimization Algorithm (WOA) with advanced local search techniques and elite solution management to improve performance on global optimization problems. Techniques from the Tabu Search algorithm are adopted to balance the exploration and exploitation phases, while an elite reintroduction strategy is implemented to retain and refine the best solutions. The efficient optimization of LWOATS is further aide

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23

Zhang, Chuang, Yue-Han Pei, Xiao-Xue Wang, Hong-Yu Hou, and Li-Hua Fu. "Symmetric cross-entropy multi-threshold color image segmentation based on improved pelican optimization algorithm." PLOS ONE 18, no. 6 (2023): e0287573. http://dx.doi.org/10.1371/journal.pone.0287573.

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To address the problems of low accuracy and slow convergence of traditional multilevel image segmentation methods, a symmetric cross-entropy multilevel thresholding image segmentation method (MSIPOA) with multi-strategy improved pelican optimization algorithm is proposed for global optimization and image segmentation tasks. First, Sine chaotic mapping is used to improve the quality and distribution uniformity of the initial population. A spiral search mechanism incorporating a sine cosine optimization algorithm improves the algorithm’s search diversity, local pioneering ability, and convergenc

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24

Asghari, Ali, Mahdi Zeinalabedinmalekmian, Hossein Azgomi, Mahmoud Alimoradi, and Shirin Ghaziantafrishi. "Farmer Ants Optimization Algorithm: A Novel Metaheuristic for Solving Discrete Optimization Problems." Information 16, no. 3 (2025): 207. https://doi.org/10.3390/info16030207.

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Анотація:

Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is ev

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25

Pan, Jeng-Shyang, Zhen Zhang, Shu-Chuan Chu, Zne-Jung Lee, and Wei Li. "Application of Diversity-Maintaining Adaptive Rafflesia Optimization Algorithm to Engineering Optimisation Problems." Symmetry 15, no. 11 (2023): 2077. http://dx.doi.org/10.3390/sym15112077.

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The Diversity-Maintained Adaptive Rafflesia Optimization Algorithm represents an enhanced version of the original Rafflesia Optimization Algorithm. The latter draws inspiration from the unique characteristics displayed by the Rafflesia during its growth, simulating the entire lifecycle from blooming to seed dispersion. The incorporation of the Adaptive Weight Adjustment Strategy and the Diversity Maintenance Strategy assists the algorithm in averting premature convergence to local optima, subsequently bolstering its global search capabilities. When tested on the CEC2013 benchmark functions und

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26

Kaya, Ebubekir, Ceren Baştemur Kaya, Emre Bendeş, Sema Atasever, Başak Öztürk, and Bilgin Yazlık. "Training of Feed-Forward Neural Networks by Using Optimization Algorithms Based on Swarm-Intelligent for Maximum Power Point Tracking." Biomimetics 8, no. 5 (2023): 402. http://dx.doi.org/10.3390/biomimetics8050402.

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One of the most used artificial intelligence techniques for maximum power point tracking is artificial neural networks. In order to achieve successful results in maximum power point tracking, the training process of artificial neural networks is important. Metaheuristic algorithms are used extensively in the literature for neural network training. An important group of metaheuristic algorithms is swarm-intelligent-based optimization algorithms. In this study, feed-forward neural network training is carried out for maximum power point tracking by using 13 swarm-intelligent-based optimization al

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27

Belazi, Akram, Héctor Migallón, Daniel Gónzalez-Sánchez, Jorge Gónzalez-García, Antonio Jimeno-Morenilla, and José-Luis Sánchez-Romero. "Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization." Mathematics 10, no. 7 (2022): 1166. http://dx.doi.org/10.3390/math10071166.

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The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficie

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28

Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization." Evolutionary Computation 18, no. 1 (2010): 127–56. http://dx.doi.org/10.1162/evco.2010.18.1.18105.

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This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using sever

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29

ARICI, Ferda Nur, and Ersin KAYA. "Hierarchical Approaches to Solve Optimization Problems." Academic Platform Journal of Engineering and Smart Systems 10, no. 3 (2022): 124–39. http://dx.doi.org/10.21541/apjess.1065912.

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Анотація:

Optimization is the operation of finding the most appropriate solution for a particular problem or set of problems. In the literature, there are many population-based optimization algorithms for solving optimization problems. Each of these algorithms has different characteristics. Although optimization algorithms give optimum results on some problems, they become insufficient to give optimum results as the problem gets harder and more complex. Many studies have been carried out to improve optimization algorithms to overcome these difficulties in recent years. In this study, six well-known popu

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30

Karataş, Osman, Celal Yaşar, Hasan Temurtaş, and Serdar Özyön. "Crayfish Optimization Algorithm." International Scientific and Vocational Studies Journal 9, no. 1 (2025): 94–117. https://doi.org/10.47897/bilmes.1666766.

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Анотація:

This study aims to improve the performance of the Crayfish Optimization Algorithm (COA), a swarm intelligence algorithm recently introduced in the literature, on various test functions with fixed and variable dimensions. Optimization can be defined as making a system as efficient as possible at the least cost, within certain constraints. Numerous optimization algorithms have been designed in the literature to obtain the best solutions for specific problems. The most critical aspects in solving these problems are modeling the problem correctly, determining the parameters and constraints, and se

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31

Pan, Anqi, Hongjun Tian, Lei Wang, and Qidi Wu. "A Decomposition-Based Unified Evolutionary Algorithm for Many-Objective Problems Using Particle Swarm Optimization." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/6761545.

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Анотація:

Evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching performance degenerates in high-dimensional objective optimizations. In this paper we propose an algorithm for many-objective optimization with particle swarm optimization as the underlying metaheuristic technique. In the proposed algorithm, the objectives are decomposed and reconstructed using discrete decoupling strategy, and the subgroup procedures are integrated into unified coe

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32

López, Luis Fernando de Mingo, Francisco Serradilla García, José Eugenio Naranjo Hernández, and Nuria Gómez Blas. "Speed Proportional Integrative Derivative Controller: Optimization Functions in Metaheuristic Algorithms." Journal of Advanced Transportation 2021 (November 3, 2021): 1–12. http://dx.doi.org/10.1155/2021/5538296.

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Анотація:

Recent advancements in computer science include some optimization models that have been developed and used in real applications. Some metaheuristic search/optimization algorithms have been tested to obtain optimal solutions to speed controller applications in self-driving cars. Some metaheuristic algorithms are based on social behaviour, resulting in several search models, functions, and parameters, and thus algorithm-specific strengths and weaknesses. The present paper proposes a fitness function on the basis of the mathematical description of proportional integrative derivate controllers sho

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33

Sabery, Ghulam Ali, Ghulam Hassan Danishyar, and Ghulam Sarwar Mubarez. "A Comparative Study of Metaheuristic Optimization Algorithms for Solving Engineering Design Problems." Journal of Mathematics and Statistics Studies 4, no. 4 (2023): 56–69. http://dx.doi.org/10.32996/jmss.2023.4.4.6.

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Metaheuristic optimization algorithms (Nature-Inspired Optimization Algorithms) are a class of algorithms that mimic the behavior of natural systems such as evolution process, swarm intelligence, human activity and physical phenomena to find the optimal solution. Since the introduction of meta-heuristic optimization algorithms, they have shown their profound impact in solving the high-scale and non-differentiable engineering problems. This paper presents a comparative study of the most widely used nature-inspired optimization algorithms for solving engineering classical design problems, which

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Coufal, Petr, Štěpán Hubálovský, Marie Hubálovská, and Zoltan Balogh. "Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems." Mathematics 9, no. 21 (2021): 2832. http://dx.doi.org/10.3390/math9212832.

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Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the p

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Peng, Qiang, Renjun Zhan, Husheng Wu, and Meimei Shi. "Comparative Study of Wolf Pack Algorithm and Artificial Bee Colony Algorithm." International Journal of Swarm Intelligence Research 15, no. 1 (2024): 1–24. http://dx.doi.org/10.4018/ijsir.352061.

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Swarm intelligence optimization algorithms have been widely used in the fields of machine learning, process control and engineering prediction, among which common algorithms include ant colony algorithm (ACO), artificial bee colony algorithm (ABC) and particle swarm optimization (PSO). Wolf pack algorithm (WPA) as a newer swarm intelligence optimization algorithm has many similarities with ABC. In this paper, the basic principles, algorithm implementation processes, and related improvement strategies of these two algorithms were described in detail; A comparative analysis of their performance

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Li, Chunfang, Yuqi Yao, Mingyi Jiang, et al. "Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA." Biomimetics 9, no. 10 (2024): 639. http://dx.doi.org/10.3390/biomimetics9100639.

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This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm’s global search capability, convergence velocity, and precision. Moreover, MISWO

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Gao, Yang, and Liang Cheng. "A multi-swarm greedy selection enhanced fruit fly optimization algorithm for global optimization in oil and gas production." PLOS One 20, no. 6 (2025): e0322111. https://doi.org/10.1371/journal.pone.0322111.

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Optimizing oil and gas production is of paramount importance in the petroleum sector, as it ensures the economic success of oil companies and meets the growing global demand for energy. The optimization of subsurface oil and gas production is critical for decision-makers, as it determines essential strategies like optimal well placement and well control parameters. Traditional reservoir production optimization methods often involve high computational costs and difficulties in achieving effective optimization. Evolutionary algorithms, inspired by biological evolution, have proven to be powerful

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Aleardi, Mattia, Silvio Pierini, and Angelo Sajeva. "Assessing the performances of recent global search algorithms using analytic objective functions and seismic optimization problems." GEOPHYSICS 84, no. 5 (2019): R767—R781. http://dx.doi.org/10.1190/geo2019-0111.1.

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We have compared the performances of six recently developed global optimization algorithms: imperialist competitive algorithm, firefly algorithm (FA), water cycle algorithm (WCA), whale optimization algorithm (WOA), fireworks algorithm (FWA), and quantum particle swarm optimization (QPSO). These methods have been introduced in the past few years and have found very limited or no applications to geophysical exploration problems thus far. We benchmark the algorithms’ results against the particle swarm optimization (PSO), which is a popular and well-established global search method. In particular

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HOSSAİN, Md Al Amin, and Züleyha YILMAZ ACAR. "Comparison of New and Old Optimization Algorithms for Traveling Salesman Problem on Small, Medium, and Large-scale Benchmark Instances." Bitlis Eren Üniversitesi Fen Bilimleri Dergisi 13, no. 1 (2023): 216–31. http://dx.doi.org/10.17798/bitlisfen.1380086.

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The Traveling Salesman Problem (TSP), a prominent combinatorial optimization issue, is the subject of this study's evaluation of the performance of new and old optimization techniques. This paper seeks to expand knowledge of optimization techniques and how they might be applied to solve TSP challenges. The goal of the research is to compare various algorithms' scalability, convergence, and computation times on benchmark instances of several sizes. To achieve this goal, this paper carried out extensive testing using the Artificial Bee Colony (ABC), Grey Wolf Optimization (GWO), and Salp Swarm A

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Yang, Shengsen, Zihan Xu, and Kun Ren. "An Improved Parameter Extraction Optimization Algorithm for RF Devices." Micromachines 16, no. 4 (2025): 432. https://doi.org/10.3390/mi16040432.

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This paper proposes an improved parameter extraction optimization algorithm for radio frequency (RF) devices. The algorithm integrates parameter classification and correction, gradient-based performance handling, bias-aware updates, and group-based optimization strategies, achieving enhanced optimization accuracy, accelerated convergence, and improved stability. It effectively addresses the limitations of deterministic algorithms in RF device parameter extraction optimization, such as low efficiency, sensitivity to initial values, and unstable convergence. To validate the algorithm’s effective

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D., KARDASH, and KOLLAROV O. "Solving optimization problems in energy with genetic algorithm." Journal of Electrical and power engineering 28, no. 1 (2023): 37–41. http://dx.doi.org/10.31474/2074-2630-2023-1-37-41.

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The article discusses the application of genetic algorithms in the field of energy optimization. Linear programming is commonly used for optimization problems in energy systems. Linear programming is a mathematical optimization method that seeks the optimal solution under constraints, where all constraints and the objective function are linear functions. In the realm of artificial intelligence,genetic algorithms are employed for optimization tasks. genetic algorithms mimic natural evolution processes, including selection, crossover, mutation, and adaptation, to solve optimization and search pr

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Deepak, Malini, and Rabee Rustum. "Review of Latest Advances in Nature-Inspired Algorithms for Optimization of Activated Sludge Processes." Processes 11, no. 1 (2022): 77. http://dx.doi.org/10.3390/pr11010077.

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The activated sludge process (ASP) is the most widely used biological wastewater treatment system. Advances in research have led to the adoption of Artificial Intelligence (AI), in particular, Nature-Inspired Algorithm (NIA) techniques such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize treatment systems. This has aided in reducing the complexity and computational time of ASP modelling. This paper covers the latest NIAs used in ASP and discusses the advantages and limitations of each algorithm compared to more traditional algorithms that have been utilized over t

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Abdulrahman, Abdulrahman, Ziad Mohammed .., Ahmed Mohamed Zaki, Faris H. H.Rizk, Marwa M. Eid, and EL-Sayed M. EL EL-Kenawy. "Exploring Optimization Algorithms: A Review of Methods and Applications." Journal of Artificial Intelligence and Metaheuristics 7, no. 2 (2024): 08–17. http://dx.doi.org/10.54216/jaim.070201.

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This article review focuses on feature selection as the main parameter that plays a major role in tuning machine learning models. Several algorithms of optimization such as MFO (Moth-Flame Optimization), the GA-GSA algorithm’s hybrid type, SOA (Seagull Optimization Algorithm), WOA (Whale Optimization Algorithm), GOA (Grasshopper Optimization Algorithm), HGSO (Henry Gas Solubility Optimization), and SafeOpt are widely used in engineering design, power systems scheduling, The paper stresses the importance of optimization in improving efficiency, lessening mistakes and increasing understandabilit

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Chen, Qinghua, Gang Yao, Lin Yang, Tangying Liu, Jin Sun, and Shuxiang Cai. "Research on Ship Replenishment Path Planning Based on the Modified Whale Optimization Algorithm." Biomimetics 10, no. 3 (2025): 179. https://doi.org/10.3390/biomimetics10030179.

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Ship replenishment path planning has always been a critical concern for researchers in the field of security. This study proposes a modified whale optimization algorithm (MWOA) to address single-task ship replenishment path planning problems. To ensure high-quality initial solutions and maintain population diversity, a hybrid approach combining the nearest neighbor search with random search is employed for initial population generation. Additionally, crossover operations and destroy and repair operators are integrated to update the whale’s position, significantly enhancing the algorithm’s sear

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Wang, Yi, and Kangshun Li. "A Lévy Flight-Inspired Random Walk Algorithm for Continuous Fitness Landscape Analysis." International Journal of Cognitive Informatics and Natural Intelligence 17, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijcini.330535.

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Heuristic algorithms are effective methods for solving complex optimization problems. The optimal algorithm selection for a specific optimization problem is a challenging task. Fitness landscape analysis (FLA) is used to understand the optimization problem's characteristics and help select the optimal algorithm. A random walk algorithm is an essential technique for FLA in continuous search space. However, most currently proposed random walk algorithms suffer from unbalanced sampling points. This article proposes a Lévy flight-based random walk (LRW) algorithm to address this problem. The Lévy

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Shi, Yuhui, Jingqian Xue, and Yali Wu. "Multi-Objective Optimization Based on Brain Storm Optimization Algorithm." International Journal of Swarm Intelligence Research 4, no. 3 (2013): 1–21. http://dx.doi.org/10.4018/ijsir.2013070101.

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In recent years, many evolutionary algorithms and population-based algorithms have been developed for solving multi-objective optimization problems. In this paper, the authors propose a new multi-objective brain storm optimization algorithm in which the clustering strategy is applied in the objective space instead of in the solution space in the original brain storm optimization algorithm for solving single objective optimization problems. Two versions of multi-objective brain storm optimization algorithm with different characteristics of diverging operation were tested to validate the usefuln

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Che, Yanhui, and Dengxu He. "A Hybrid Whale Optimization with Seagull Algorithm for Global Optimization Problems." Mathematical Problems in Engineering 2021 (January 28, 2021): 1–31. http://dx.doi.org/10.1155/2021/6639671.

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Seagull optimization algorithm (SOA) inspired by the migration and attack behavior of seagulls in nature is used to solve the global optimization problem. However, like other well-known metaheuristic algorithms, SOA has low computational accuracy and premature convergence. Therefore, in the current work, these problems are solved by proposing the modified version of SOA. This paper proposes a novel hybrid algorithm, called whale optimization with seagull algorithm (WSOA), for solving global optimization problems. The main reason is that the spiral attack prey of seagulls is very similar to the

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Wisam, Abdulelah Qasim. "A HYBRID ALGORITHM BASED ON INVASIVE WEED OPTIMIZATION ALGORITHM AND GREY WOLF OPTIMIZATION ALGORITHM." International Journal of Artificial Intelligence and Applications (IJAIA) 11, January (2020): 31–44. https://doi.org/10.5281/zenodo.3690787.

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In this research, two algorithms first, considered to be one of hybrid algorithms. And it is algorithm represents invasive weed optimization. This algorithm is a random numerical algorithm and the second algorithm representing the grey wolves optimization. This algorithm is one of the algorithms of swarm intelligence in intelligent optimization. The algorithm of invasive weed optimization is inspired by nature as the weeds have colonial behavior and were introduced by Mehrabian and Lucas in 2006. Invasive weeds are a serious threat to cultivated plants because of their adaptability and are a t

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Yao, Jinyan, Yongbai Sha, Yanli Chen, and Xiaoying Zhao. "A Novel Ensemble of Arithmetic Optimization Algorithm and Harris Hawks Optimization for Solving Industrial Engineering Optimization Problems." Machines 10, no. 8 (2022): 602. http://dx.doi.org/10.3390/machines10080602.

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Recently, numerous new meta-heuristic algorithms have been proposed for solving optimization problems. According to the Non-Free Lunch theorem, we learn that no single algorithm can solve all optimization problems. In order to solve industrial engineering design problems more efficiently, we, inspired by the algorithm framework of the Arithmetic Optimization Algorithm (AOA) and the Harris Hawks Optimization (HHO), propose a novel hybrid algorithm based on these two algorithms, named EAOAHHO in this paper. The pinhole imaging opposition-based learning is introduced into the proposed algorithm t

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Soghrati, F., and R. Moeini. "Deriving optimal operation of reservoir proposing improved artificial bee colony algorithm: standard and constrained versions." Journal of Hydroinformatics 22, no. 2 (2019): 263–80. http://dx.doi.org/10.2166/hydro.2019.125.

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Abstract In this paper, one of the newest meta-heuristic algorithms, named artificial bee colony (ABC) algorithm, is used to solve the single-reservoir operation optimization problem. The simple and hydropower reservoir operation optimization problems of Dez reservoir, in southern Iran, have been solved here over 60, 240, and 480 monthly operation time periods considering two different decision variables. In addition, to improve the performance of this algorithm, two improved artificial bee colony algorithms have been proposed and these problems have been solved using them. Furthermore, in ord

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