Pyramid particle swarm optimization with novel strategies of competition and cooperation

Particle swarm optimization (PSO) has shown its advantages in various optimization problems. Topology and updating strategies are among its key concepts and have significant impacts on optimization ability. This paper proposes a pyramid PSO (PPSO) with novel competitive and cooperative strategies to update particles' information. PPSO builds a pyramid and assigns each particle to a specific layer according to its fitness. The particles at the same layer will make a pairwise comparison to determine the winners and the losers. The losers will cooperate with their corresponding winners, while the winners will cooperate with the particles at the upper layer and those at the top layer. Each particle in PPSO has its own learning behavior, having more than one exemplar rather than the only global best to learn from. The diversity of the swarm is enhanced and it positively affects the performance of PSO. Extensive experiments demonstrate that the PPSO has superior performance in terms of accuracy, Wilcoxon signed-rank test and convergence speed, yet achieves comparable running time in most cases, when compared with the canonical PSO and eight state-of-the-art PSO variants. Furthermore, we analyze the influence of parameters for the PPSO. All these illustrate that the PPSO is promising for numerical optimization. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a novel particle swarm optimization algorithm called PPSO, which utilizes a pyramid structure and competitive-cooperative strategies to update particle information. Extensive experiments demonstrate that PPSO outperforms other algorithms in terms of accuracy and convergence speed, indicating its potential in numerical optimization.