An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Artificial Intelligence Review, Journal Year: 2023, Volume and Issue: 56(11), P. 13187 - 13257

Published: April 9, 2023

Language: Английский

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Puma optimizer (PO): a novel metaheuristic optimization algorithm and its application in machine learning

Cluster Computing, Journal Year: 2024, Volume and Issue: 27(4), P. 5235 - 5283

Published: Jan. 19, 2024

Language: Английский

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Mother optimization algorithm: a new human-based metaheuristic approach for solving engineering optimization

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: June 26, 2023

This article's innovation and novelty are introducing a new metaheuristic method called mother optimization algorithm (MOA) that mimics the human interaction between her children. The real inspiration of MOA is to simulate mother's care children in three phases education, advice, upbringing. mathematical model used search process exploration presented. performance assessed on set 52 benchmark functions, including unimodal high-dimensional multimodal fixed-dimensional CEC 2017 test suite. findings optimizing functions indicate MOA's high ability local exploitation. global exploration. fixed-dimension multi-model suite show with balance exploitation effectively supports can generate appropriate solutions for problems. outcomes quality obtained from has been compared 12 often-used algorithms. Upon analysis comparison simulation results, it was found proposed outperforms competing algorithms superior significantly more competitive performance. Precisely, delivers better results most objective functions. Furthermore, application four engineering design problems demonstrates efficacy approach solving real-world statistical Wilcoxon signed-rank significant superiority twelve well-known managing studied this paper.

Language: Английский

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Lyrebird Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 507 - 507

Published: Oct. 23, 2023

In this paper, a new bio-inspired metaheuristic algorithm called the Lyrebird Optimization Algorithm (LOA) that imitates natural behavior of lyrebirds in wild is introduced. The fundamental inspiration LOA strategy when faced with danger. situation, scan their surroundings carefully, then either run away or hide somewhere, immobile. theory described and mathematically modeled two phases: (i) exploration based on simulation lyrebird escape (ii) exploitation hiding strategy. performance was evaluated optimization CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. results show proposed approach has high ability terms exploration, exploitation, balancing them during search process problem-solving space. order evaluate capability dealing tasks, obtained from were compared twelve well-known algorithms. superior competitor algorithms by providing better most benchmark functions, achieving rank first best optimizer. A statistical analysis shows significant superiority comparison addition, efficiency handling real-world applications investigated through twenty-two constrained problems 2011 four engineering design problems. effective tasks while

Language: Английский

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Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2024, Volume and Issue: 9(2), P. 65 - 65

Published: Jan. 23, 2024

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates natural behavior of pufferfish in nature, is introduced this paper. The fundamental inspiration POA adapted from defense mechanism against predators. In mechanism, by filling its elastic stomach with water, becomes a spherical ball pointed spines, and as result, hungry predator escapes threat. theory stated then mathematically modeled two phases: (i) exploration based on simulation predator’s attack (ii) exploitation escape spiny pufferfish. performance evaluated handling CEC 2017 test suite for problem dimensions equal to 10, 30, 50, 100. optimization results show has achieved an effective solution appropriate ability exploration, exploitation, balance between them during search process. quality process compared twelve well-known algorithms. provides superior achieving better most benchmark functions order solve competitor Also, effectiveness handle tasks real-world applications twenty-two constrained problems 2011 four engineering design problems. Simulation solutions

Language: Английский

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Dollmaker Optimization Algorithm: A Novel Human-Inspired Optimizer for Solving Optimization Problems

International journal of intelligent engineering and systems, Journal Year: 2024, Volume and Issue: 17(3), P. 816 - 828

Published: May 3, 2024

In this article, a new human-based metaheuristic algorithm named Dollmaker Optimization Algorithm (DOA) is introduced, which imitates the strategy and skill of dollmaker when making dolls.The basic inspiration DOA derived from two natural behaviors in doll process (i) general changes to dollmaking materials (ii) precise small on appearance characteristics theory proposed then modeled mathematically phases exploration based simulation large made doll-making exploitation performance optimization evaluated twenty-three standard benchmark functions unimodal, high-dimensional multimodal, fixed-dimensional multimodal types.The results show that has achieved suitable for problems with its ability exploration, exploitation, balance them during search process.Comparison twelve competing algorithms shows superior compared by providing better all getting rank first best optimizer.In addition, efficiency handling real-world applications four engineering design problems.Simulation acceptable real world values variables objective algorithms.

Language: Английский

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Machine learning-aided modeling for predicting freshwater production of a membrane desalination system: A long-short-term memory coupled with election-based optimizer

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 86, P. 690 - 703

Published: Dec. 28, 2023

Membrane desalination (MD) is an efficient process for desalinating saltwater, combining the uniqueness of both thermal and separation distillation configurations. In this context, optimization strategies sizing methodologies are developed from balance system's energy demand. Therefore, robust prediction modeling thermodynamic behavior freshwater production crucial optimal design MD systems. This study presents a new advanced machine-learning model to obtain permeate flux tubular direct contact membrane unit. The was established by optimizing long-short-term memory (LSTM) election-based algorithm (EBOA). inputs were temperatures feed flow, rate salinity flow. optimized compared with other LSTM models sine–cosine (SCA), artificial ecosystem optimizer (AEO), grey wolf (GWO). All trained, tested, evaluated using different accuracy measures. LSTM-EBOA outperformed in predicting based on had highest coefficient determination 0.998 0.988 lowest root mean square error 1.272 4.180 training test, respectively. It can be recommended that paper provide useful pathway parameters selection performance systems makes optimally designed rates without costly experiments.

Language: Английский

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Kookaburra Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(6), P. 470 - 470

Published: Oct. 1, 2023

In this paper, a new bio-inspired metaheuristic algorithm named the Kookaburra Optimization Algorithm (KOA) is introduced, which imitates natural behavior of kookaburras in nature. The fundamental inspiration KOA strategy when hunting and killing prey. theory stated, its mathematical modeling presented following two phases: (i) exploration based on simulation prey (ii) exploitation kookaburras’ ensuring that their killed. performance has been evaluated 29 standard benchmark functions from CEC 2017 test suite for different problem dimensions 10, 30, 50, 100. optimization results show proposed approach, by establishing balance between exploitation, good efficiency managing effective search process providing suitable solutions problems. obtained using have compared with 12 well-known algorithms. analysis shows KOA, better most functions, provided superior competition addition, implementation 22 constrained problems 2011 suite, as well 4 engineering design problems, approach acceptable to competitor algorithms handling real-world applications.

Language: Английский

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Giant Armadillo Optimization: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

Biomimetics, Journal Year: 2023, Volume and Issue: 8(8), P. 619 - 619

Published: Dec. 17, 2023

In this paper, a new bio-inspired metaheuristic algorithm called Giant Armadillo Optimization (GAO) is introduced, which imitates the natural behavior of giant armadillo in wild. The fundamental inspiration design GAO derived from hunting strategy armadillos moving towards prey positions and digging termite mounds. theory expressed mathematically modeled two phases: (i) exploration based on simulating movement mounds, (ii) exploitation armadillos' skills order to rip open performance handling optimization tasks evaluated solve CEC 2017 test suite for problem dimensions equal 10, 30, 50, 100. results show that able achieve effective solutions problems by benefiting its high abilities exploration, exploitation, balancing them during search process. quality obtained compared with twelve well-known algorithms. simulation presents superior competitor algorithms providing better most benchmark functions. statistical analysis Wilcoxon rank sum confirms has significant superiority over implementation 2011 four engineering proposed approach dealing real-world applications.

Language: Английский

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Drawer Algorithm: A New Metaheuristic Approach for Solving Optimization Problems in Engineering

Biomimetics, Journal Year: 2023, Volume and Issue: 8(2), P. 239 - 239

Published: June 6, 2023

Metaheuristic optimization algorithms play an essential role in optimizing problems. In this article, a new metaheuristic approach called the drawer algorithm (DA) is developed to provide quasi-optimal solutions The main inspiration for DA simulate selection of objects from different drawers create optimal combination. process involves dresser with given number drawers, where similar items are placed each drawer. based on selecting suitable items, discarding unsuitable ones and assembling them into appropriate described, its mathematical modeling presented. performance tested by solving fifty-two objective functions various unimodal multimodal types CEC 2017 test suite. results compared twelve well-known algorithms. simulation demonstrate that DA, proper balance between exploration exploitation, produces solutions. Furthermore, comparing shows effective problems much more competitive than against which it was to. Additionally, implementation twenty-two constrained 2011 suite demonstrates high efficiency handling real-world applications.

Language: Английский

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