Elite subgroup guided particle swarm optimisation algorithm with multi-strategy adaptive learning. (19th January 2023) - Wu, Runxiu; Wang, Lulu; Wu, Shuixiu; Sun, Hui
In order to address the premature convergence of the standard particle swarm optimisation (PSO) algorithm, this paper proposes an elite subgroup guided particle swarm optimisation algorithm with multi-strategy adaptive learning (EGAPSO). In order to enhance the particle's ability to escape from the local extremum point, the social cognitive part of originally learning only from the global optimal particle is changed to the part of adaptively choosing to learn from the global optimal particle and the particle in the elite subgroup in the model. Meanwhile, in order to make the algorithm more universal, a variety of learning strategies such as elite particle opposition-based learning, subspace Gaussian learning and mean centre learning with different search characteristics are adaptively selected in the evolutionary process. Combination of the two improved measures can not only increase the universality of the algorithm, but also enhance the diversity of the population, which effectively helps the algorithm escape from the local optimum and avoid the premature convergence. Simulation results on the typical test function set and test results of comparison with other classical and newly improved PSO algorithms show that the proposed algorithm performs better in optimisation and stability.