<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.</p></abstract>

The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

Population Diversity Maintenance In Brain Storm Optimization Algorithm

International Doctoral Innovation Centre, The University of Nottingham Ningbo, China Division of Computer Science, University of Nottingham Ningbo, China, Ningbo, 315100, Zhejiang, China

Department of Electrical & Electronic Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, Jiangsu, China

Department of Management Science, Shenzhen University, Shenzhen, China, Shenzhen, 518060, Guangdong, China

Division of Computer Science, University of Nottingham Ningbo, China, Ningbo, 315100, Zhejiang, China

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Population Diversity Maintenance In Brain Storm Optimization Algorithm"}

The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on...

Population Diversity Maintenance In Brain Storm Optimization Algorithm

Publié en ligne: 01 mars 2015

Pages: 83 - 97

DOI: https://doi.org/10.1515/jaiscr-2015-0001

© Academy of Management (SWSPiZ), Lodz

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.