A new human-based metahurestic optimization method based on mimicking cooking training

Trojovská, Eva; Dehghani, Mohammad

doi:10.1038/s41598-022-19313-2

Cited by 65 publications

(41 citation statements)

References 32 publications

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“…( 2018 ) 113 Cheetah Chase Algorithm (CCA) Goudhaman ( 2018 ) 114 Cheetah Optimizer (CO) Akbari et al. ( 2022 ) 115 Chef-Based Optimization Algorithm (CBOA) Trojovská and Dehghani ( 2022 ) 116 Chemical Reaction Optimization (CRO) Alatas ( 2011 ) 117 Chicken Swarm Optimization (CSO) Meng et al. ( 2014 ) 118 Child Drawing Development Optimization (CDDO) Abdulhameed and Rashid ( 2022 ) 119 Chimp Optimization Algorithm (ChOA) Khishe and Mosavi ( 2020 ) 120 Circle Search Algorithm (CSA) Qais et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

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

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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Section: Metaheuristicsmentioning

confidence: 99%

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

2023

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“…Besides animal behavior, several shortcomings of metaheuristics use social activities, traditional games, and leadership as metaphors. Chef-based optimization algorithm (CBOA) [24], election-based optimization algorithm (EBOA) [25], sewing training-based optimization (STBO) [26], and driving training-based optimization (DTBO) [27] are the example of metaheuristics that use the social activity as a metaphor. Football game-based optimization (FBGO) [28], dart game optimization (DGO) [29], and shell game optimization (SGO) [30] are an example of metaheuristics that use the traditional game as a metaphor.…”

Section: Introductionmentioning

confidence: 99%

Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

2023

IJIES

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This paper introduces a new metaphor-free metaheuristic called attack-leave optimizer (ALO). As the name suggests, ALO deploys two strategies to find the optimal solution. The central concept of ALO is to intensify guided searches as a required method. Then, a random search is performed only if the guided search fails to improve the current solution. ALO consists of four guided searches and one random search, performed in three phases: two mandatory and one optional. In the first phase, the guided search is conducted with the best global solution as the reference. In the second phase, the guided search is conducted with a randomly selected solution as the reference. The random search is performed in the third phase. Evaluating ALO, it was tested on 23 classic functions and benchmarked against five existing metaheuristics with known shortcomings: Mixed leader-based optimizer (MLBO), slime mould algorithm (SMA), golden search optimizer (GSO), zebra optimization algorithm (ZOA), and coati optimization algorithm (COA). The results indicate that ALO is highly competitive, outperforming MLBO, SMA, GSO, COA, and ZOA in solving 16, 16, 14, 10, and 9 functions respectively, and demonstrating ALO as a promising new metaheuristic.

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“…Human Mental Search (HMS) is a human-based method that is inspired by exploration strategies of the bid space in online auctions [44]. Examples of well-established and recently developed human-based metaheuristic algorithms are: Driving Training-Based Optimization (DTBO) [45], Chef-based Optimization Algorithm (CBOA) [46], and Poor and Rich Optimization (PRO) [47].…”

Section: Introductionmentioning

confidence: 99%

Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

Dehghani¹,

Milkova²

2023

Computer Modeling in Engineering &Amp; Sciences

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In this paper, based on the concept of the NFL theorem, that there is no unique algorithm that has the best performance for all optimization problems, a new human-based metaheuristic algorithm called Language Education Optimization (LEO) is introduced, which is used to solve optimization problems. LEO is inspired by the foreign language education process in which a language teacher trains the students of language schools in the desired language skills and rules. LEO is mathematically modeled in three phases: (i) students selecting their teacher, (ii) students learning from each other, and (iii) individual practice, considering exploration in local search and exploitation in local search. The performance of LEO in optimization tasks has been challenged against fifty-two benchmark functions of a variety of unimodal, multimodal types and the CEC 2017 test suite. The optimization results show that LEO, with its acceptable ability in exploration, exploitation, and maintaining a balance between them, has efficient performance in optimization applications and solution presentation. LEO efficiency in optimization tasks is compared with ten well-known metaheuristic algorithms. Analyses of the simulation results show that LEO has effective performance in dealing with optimization tasks and is significantly superior and more competitive in combating the compared algorithms. The implementation results of the proposed approach to four engineering design problems show the effectiveness of LEO in solving real-world optimization applications.

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A new human-based metahurestic optimization method based on mimicking cooking training

Cited by 65 publications

References 32 publications

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

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

Attack-Leave Optimizer: A New Metaheuristic that Focuses on The Guided Search and Performs Random Search as Alternative

Language Education Optimization: A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems

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