Work

Brain Storm Optimization Algorithm with an Adaptive Parameter Control Strategy for Finding Multiple Optimal Solutions

Year: 2023

Type: article

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.

Source: International Journal of Computational Intelligence Systems

DOI: 10.1007/s44196-023-00326-2

Authors: Yuhui Zhang, Wenhong Wei, Shaohao Xie, Zijia Wang

Institutions: Dongguan University of Technology, Shantou Polytechnic, Guangzhou University

Language: English

Topic: Metaheuristic Optimization Algorithms Research

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,090

Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangdong Province