Adaptive heterogeneous particle swarm optimization with comprehensive learning strategy

Abstract

This paper proposes an adaptive heterogeneous particle swarm optimization with a comprehensive learning strategy for solving single-objective constrained optimization problems. In this algorithm, particles can use an exploration strategy and an exploitation strategy to update their positions. The historical success rates of the two strategies are used to adaptively control the adoption rates of strategies in the next iteration. The search strategy in the canonical particle swarm optimization algorithm is based on elite solutions. As a result, when no particles can discover better solutions for several generations, this algorithm is likely to fall into stagnation. To respond to this challenge, a new strategy is proposed to explore the neighbors of the elite solutions in this study. Finally, a constraint handling method is equipped to the proposed algorithm to make it be able to solve constrained optimization problems. The proposed algorithm is compared with the canonical particle swarm optimization, differential evolution, and several recently proposed algorithms on the benchmark test suite. The Wilcoxon signed-rank test results show that the proposed algorithm is significantly better on most of the benchmark problems compared with the competitors. The proposed algorithm is also applied to solve two real-world mechanical engineering problems. The experimental results show that the proposed algorithm performs consistently well on these problems.

An intelligent load balancing algorithm for 5G‐satellite networks

“…It models the network as a search space and the call admission policies as potential solutions to the problem [82]. The particles in the swarm represent different call admission policies, and their movement is influenced by their own personal best solution, the best solution of the swarm and the current state of the network.…”

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

“…To solve the problem that the PSO algorithm can become trapped near a local optimum, Liu and Nishi [33] proposed a new update formula for Pbest, which expands the search range of particles by searching adjacent positions of Pbest to avoid premature convergence to some extent. Lee et al [34] added a random noise term to the velocity update formula of PSO and proposed the repulsive method based on the repulsion theory applied to the PSO algorithm.…”