Dual-Population Hybrid Particle Swarm Optimization Algorithm Based on Hooke’s Law Competition Mechanism

The Particle swarm optimization (PSO) algorithm has strong universality and fast convergence speed, but when solving complex multimodal optimization problems, it is prone to fall into local optimum due to insufficient population diversity. To address this issue, this paper proposes a dual-population hybrid particle swarm optimization algorithm based on Hooke’s law competition mechanism (HLCM-DHPSO). This algorithm integrates the differential evolution algorithm into the PSO framework, and the two subpopulation sizes dynamically compete for computing resources according to the adaptive mechanism of Hooke’s law. When the algorithm stagnates, HLCM-DHPSO can automatically trace back to historical archives and adjust the inertia weight based on excellent experience data. Meanwhile, HLCM-DHPSO adaptively adjusts the acceleration coefficient through the Sine function to enhance the algorithm’s ability to escape from local optimum. To verify the effectiveness of the HLCM-DHPSO algorithm, it is compared with eight advanced optimization algorithms on the CEC2017 benchmark test set. The experimental results show that HLCM-DHPSO significantly outperforms the comparison algorithms in terms of solution performance, especially in handling high-dimensional and multi-peak complex functions, demonstrating superior global search and optimization capabilities.