Adjustable driving force based particle swarm optimization algorithm

Particle swarm optimization algorithm (PSO) is a popular optimizer, in which each particle selects its learning exemplars relying on their fitness. Thus, the search process of each particle can be seen as driven by a fitness-based force. Intuitively, the driving force is conducive to the optimizing process. However, it may bring a premature convergence of a population. In this work, a novelty-based driving force is put forward to overcome deficiencies of the fitness-based driving force. In the new proposed adjustable driving force based PSO, named as ADFPSO, two types of exemplars respectively with high fitness and high novelty are saved in two archives. In each generation, a particle respectively chooses two exemplars from the two archives to update its velocity. In addition, three time-varying parameters are introduced to adjust the particle's learning weights for the two exemplars aiming to satisfy distinct requirements of different evolution stages. Comprehensive properties of ADFPSO are extensively testified by a set of experiments, in which nine PSO variants are adopted as peer algorithms and two CEC test suites are selected as optimization problems. Moreover, distinct characteristics of the proposed novelty-based driving force are also analyzed based on a few experiments. (C) 2022 Elsevier Inc. All rights reserved.