A Multi-Mechanism Particle Swarm Optimization Algorithm Combining Hunger Games Search and Simulated Annealing

Abstract

Particle Swarm Optimization (PSO) algorithm is a meta-heuristic algorithm inspired by the foraging behavior of birds, which has received a lot of attention from many scholars because of its simple principle and fast convergence rate. However, the traditional particle update mechanism limits the performance of the algorithm and makes it easy to fall into local extremums, leading to a reduced convergence rate at a later stage. In this paper, we propose a Multi-Mechanism Particle Swarm Optimization (HGSPSO) algorithm. The algorithm optimizes the position update formula of the particles by the Hunger Game Search (HGS) algorithm to accelerate the convergence speed at the later stage of the algorithm, and then the Simulated Annealing (SA) algorithm is introduced to dynamically update the inertia weights to balance the exploration and utilization of the algorithm to help the particles jump out of the local extrema. In addition, the double variational restrictions strategy is used to simultaneously restrict the velocity and position of the particles to avoid particle transgressions. We tested the proposed algorithm with five compare algorithms on 20 benchmark functions in 30, 50, 100, and 1000 dimensions using Eclipse Kepler Release software. The experimental results show that HGSPSO shows significant superiority in all four evaluation metrics and five assessment schemes.