A multi-objective optimization algorithm based on subgroup stratified coarse-grained model and its application. (June 2021) - Timing, Li; Yongzhe, Zhang; Kewen, Li; Yongqi, Liang; Xiangbo, Ma
Abstract : The subgroup stratified coarse-grained particle swarm optimization model is proposed, in which the subgroups play different roles in the evolution process to improve the performance of PSO algorithms. The proposed algorithm can dynamically adjust the evolution strategy by the update situation of the Pareto solution set and increase perturbation of the local extremum to improve stability and optimization performance. The proposed algorithm is applied to solve the constrained multi-objective web service composition optimization problem and has achieved better comprehensive performance. Abstract: Low efficiency and the tendency to fall into local optimum are the major obstacles to web service composition optimization. In this paper, we propose a multi-objective optimization algorithm based on the subgroup stratified coarse-grained model to improve the performance of web service composition optimization. Compared to the general particle swarm optimization algorithms, the proposed algorithm improves population structure, dynamically adjusts evolution strategy and increases the local extremum's perturbation. We demonstrate a solution to the constrained multi-objective web service composition optimization problem based on the proposed algorithm. Theoretical analysis and experimental results show that it improves the performance of web service composition optimization. Graphical abstract: Image, graphical abstract