A Molecular Interactions-Based Social Learning Particle Swarm Optimization Algorithm

Abstract

Social learning particle swarm optimization (SL-PSO) allows individuals to learn from others to improve the scalability with easy parameter settings. However, it still suffers from the poor convergence for those multi-modal problems due to the loss of swarm diversity. To improve both the diversity and the convergence, this paper proposes a novel algorithm to apply the mechanism of molecular interactions to SL-PSO, in which the molecular attraction aims to improve the convergence, and the molecular repulsion intends to enhance the diversity. In the experiments, we compare our algorithm with the SL-PSO algorithm and other representative PSO and evolutionary algorithms on 49 benchmark functions. The results show the performance of the proposed algorithm is better than that of the SL-PSO algorithm and other representative PSO and evolutionary algorithms on average. This work builds the solid foundation for the integration of the molecular interaction mechanism with PSO and other optimization algorithms.

Enhanced Comprehensive Learning Particle Swarm Optimization with Dimensional Independent and Adaptive Parameters

“…Xia et al [ 14 ] discussed GPSO with tabu detection and local search in a shrunk space; each dimension d is segmented into 7 regions of equivalent sizes; for every 5 consecutive generations, the variant calculates the excellence level of each region on dimension d based on the ranking of all the particles' personal fitness values and the distribution of all the particles' personal best positions in the regions; according to the excellence level of the region that the global best position belongs to, the variant appropriately randomly generates a possible replacement from some other region to assist escaping from a local optimum; when the global best position falls in a region on dimension d for 80 consecutive generations, the variant shrinks the dimensional search space to that specific region for the purpose of speeding up convergence; moreover, the variant conducts local search with the aid of differential evolution. Other recent works related to integrating GPSO/LPSO with multistrategy and/or adaptivity include [ 15 – 32 ].…”