Hybrid particle swarm optimization based on pressure equilibrium

Particle swarm optimization (PSO) is a type of swarm intelligence algorithm characterized by its simple structure and rapid computation speed. However, when addressing complex multi-modal optimization problems, it is prone to local optimization due to insufficient diversity within the population. To mitigate this limitation, we propose a novel hybrid particle swarm optimization algorithm based on pressure equilibrium (HPSO-PE). HPSO-PE integrates differential evolution (DE) into the PSO framework and adaptively adjusts the sizes of two sub-populations based on pressure equilibrium theory. In HPSO-PE, the PSO sub-population employs a multiple-perception topology structure to facilitate information exchange among particles, thereby enhancing the convergence stability of the population. Concurrently, the DE sub-population introduces a prophet group as an evolutionary guide, improving the direction of differential operations. To evaluate the performance of HPSO-PE, we conducted experiments using the CEC2017 benchmark test suite, comparing the results with eight state-of-the-art algorithms. The experimental findings demonstrate that HPSO-PE exhibits high performance and strong adaptability.