An Improved Particle Swarm Algorithm for Constrained Optimization Problem

Abstract

Particle swarm optimization is a global random search algorithm that is simulated by mimicking the behavior of migration and aggregation of birds. In order to improve the global search ability of the algorithm, this paper proposes a new inertia weight. For the constrained optimization problem, this paper controls the number of particles that violate the constraint conditions, and proposes a new particle selection method to improve the ability of the particle swarm algorithm to search for boundaries. Finally, experiments were performed using three benchmark functions, and the results show that the optimization speed of the improved particle swarm algorithm has been greatly improved.