Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization
A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discr...
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| Published in | Biomimetics (Basel, Switzerland) Vol. 7; no. 4; p. 144 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English |
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27.09.2022
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| ISSN | 2313-7673 2313-7673 |
| DOI | 10.3390/biomimetics7040144 |
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| Abstract | A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92. |
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| AbstractList | A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92. A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92. |
| Audience | Academic |
| Author | Chen, Zuyan Li, Jianfeng Xiao, Dunhui Cao, Xinwei Ha, Tran Li, Shuai Ding, Lei Francis, Adam Liao, Bolin |
| AuthorAffiliation | 3 School of Mathematics, Tongji University, Shanghai 200092, China 5 School of Business, Jiangnan University, Wuxi 214122, China 2 College of Computer Science and Engineering, Jishou University, Jishou 416000, China 4 Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi 000084, Vietnam 1 College of Engineering, Swansea University, Swansea SA1 3UA, UK |
| AuthorAffiliation_xml | – name: 3 School of Mathematics, Tongji University, Shanghai 200092, China – name: 5 School of Business, Jiangnan University, Wuxi 214122, China – name: 4 Institute of Mechanics, Vietnam Academy of Science and Technology, Hanoi 000084, Vietnam – name: 1 College of Engineering, Swansea University, Swansea SA1 3UA, UK – name: 2 College of Computer Science and Engineering, Jishou University, Jishou 416000, China |
| Author_xml | – sequence: 1 givenname: Zuyan surname: Chen fullname: Chen, Zuyan – sequence: 2 givenname: Adam surname: Francis fullname: Francis, Adam – sequence: 3 givenname: Shuai orcidid: 0000-0001-8316-5289 surname: Li fullname: Li, Shuai – sequence: 4 givenname: Bolin surname: Liao fullname: Liao, Bolin – sequence: 5 givenname: Dunhui orcidid: 0000-0003-2461-523X surname: Xiao fullname: Xiao, Dunhui – sequence: 6 givenname: Tran surname: Ha fullname: Ha, Tran – sequence: 7 givenname: Jianfeng surname: Li fullname: Li, Jianfeng – sequence: 8 givenname: Lei orcidid: 0000-0001-7403-4770 surname: Ding fullname: Ding, Lei – sequence: 9 givenname: Xinwei surname: Cao fullname: Cao, Xinwei |
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| Snippet | A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species’ hunting... A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting... |
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| SubjectTerms | Algorithms Behavior Birds constrained optimization Efficiency egret swarm optimization algorithm Engineering Fault diagnosis Genetic algorithms Heuristic Mathematical models metaheuristic algorithm Mutation Neural networks Optimization algorithms Predatory behavior swarm intelligence |
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| Title | Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization |
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