Year: 2019
Type: article
Abstract: Brain storm optimization (BSO) algorithm is a simple and effective evolutionary algorithm. Some multiobjective brain storm optimization algorithms have low search efficiency. This paper combines the decomposition technology and multiobjective brain storm optimization algorithm (MBSO/D) to improve the search efficiency. Given weight vectors transform a multiobjective optimization problem into a series of subproblems. The decomposition technology determines the neighboring clusters of each cluster. Solutions of adjacent clusters generate new solutions to update population. An adaptive selection strategy is used to balance exploration and exploitation. Besides, MBSO/D compares with three efficient state‐of‐the‐art algorithms, e.g., NSGAII and MOEA/D, on twenty‐two test problems. The experimental results show that MBSO/D is more efficient than compared algorithms and can improve the search efficiency for most test problems.
Source: Complexity
DOI: 10.1155/2019/5301284
Authors: Cai Dai, Xiujuan Lei
Institution: Shaanxi Normal University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,760
Awards: G1170839758, G1251743232, G1572513237, G169550720, G2601127192, G2763570945, G2800471605, G316783314, G3360795578, G3894349201, G6140578506, G6193601323, G6485970289, 动态蛋白质网络复合物识别方法研究及其应用, G7549148348, G7717298466, G7778331940, G7779581260, G7904066442, G7905153775, G8513270824, G8535955237, G8686470721
OpenAlex ID: W2912957247