A Novel Multi-Swarm Approach for Numeric Optimization

Abstract

In order to solve the numeric optimization problems, swarm-based meta-heuristic algorithms can be used as an alternative to solve optimization problems. Meta-heuristic algorithms do not guarantee finding the optimal solution but they produce acceptable solutions in a reasonable computation time. By depending on the nature of the problems and the structure of the meta-heuristic algorithms, different results are obtained by different algorithms, and none of the meta-heuristic algorithm could guarantee to find the optimal solution. Particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms are well known meta-heuristic algorithms often used for solving numeric optimization problems. In this study, a novel multi-swarm approach based on PSO and ABC algorithms is suggested. The proposed multi-swarm approach includes PSO and ABC algorithms together and replacing the swarm which achieves better solutions than the other algorithm in a pre-defined migration period. By this migration, swarm always include better solutions concerned to the algorithm which achieves better results. While running PSO and ABC algorithms competitively, this migration ensures to utilize better solutions of both the solutions of PSO or ABC algorithms, and the convergence characteristic of each algorithm provides different approximation to the solution space. Thus, it is expected to obtain successful solutions and increasing the success rate at each migration cycle. The suggested approach has been tested on 14 well-known benchmark functions, and the results of the study are compared with the results in literature. The experimental results and comparisons show that the proposed approach is better than the other algorithms.