Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions
Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global o...
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| Published in | International journal of computational intelligence systems Vol. 16; no. 1; pp. 1 - 17 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Dordrecht
Springer Netherlands
26.09.2023
Springer |
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| Abstract | Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions. |
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| AbstractList | Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions. Abstract Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain storm optimization (BSO) is a relatively new paradigm of swarm intelligence algorithm that has been shown to be effective in solving global optimization problems, but it has not been fully exploited for multimodal optimization problems. A simple control strategy for the step size parameter in BSO cannot meet the need of optima finding task in multimodal landscapes and can possibly be refined and optimized. In this paper, we propose an adaptive BSO (ABSO) algorithm that adaptively adjusts the step size parameter according to the quality of newly created solutions. Extensive experiments are conducted on a set of multimodal optimization problems to evaluate the performance of ABSO and the experimental results show that ABSO outperforms existing BSO algorithms and some recently developed algorithms. BSO has great potential in multimodal optimization and is expected to be useful for solving real-world optimization problems that have multiple optimal solutions. |
| ArticleNumber | 160 |
| Author | Wei, Wenhong Xie, Shaohao Zhang, Yuhui Wang, Zijia |
| Author_xml | – sequence: 1 givenname: Yuhui orcidid: 0000-0002-5769-3456 surname: Zhang fullname: Zhang, Yuhui email: yhzhang@dgut.edu.cn organization: School of Computer Sciences and Technology, Dongguan University of Technology – sequence: 2 givenname: Wenhong surname: Wei fullname: Wei, Wenhong organization: School of Computer Sciences and Technology, Dongguan University of Technology – sequence: 3 givenname: Shaohao surname: Xie fullname: Xie, Shaohao organization: School of Electronic Information, Shantou Polytechnic – sequence: 4 givenname: Zijia surname: Wang fullname: Wang, Zijia organization: School of Computer Science and Cyber Engineering, Guangzhou University |
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| Keywords | Dynamic parameter control strategy Adaptive parameter control strategy Multimodal optimization Brain storm optimization |
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| Snippet | Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet challenging. Brain... Abstract Real-world optimization problems often have multiple optimal solutions and simultaneously finding these optimal solutions is beneficial yet... |
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| SubjectTerms | Adaptive parameter control strategy Artificial Intelligence Brain storm optimization Computational Intelligence Control Dynamic parameter control strategy Engineering Mathematical Logic and Foundations Mechatronics Multimodal optimization Research Article Robotics |
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| Title | Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions |
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