Heterogeneous Genetic Learning and Comprehensive Learning Strategy Particle Swarm Optimizer

In canonical PSO, maintaining an appropriate balance between exploration and exploitation is vital for optimizing particle behavior. However, this balance can be difficult to achieve in some complex scenarios. To tackle this issue, this study proposes an innovative PSO variant, named the heterogeneous genetic learning and comprehensive learning strategy PSO (HGCLPSO). The HGCLPSO incorporates the genetic learning strategy (GLS) and comprehensive learning strategy (CLS) to form heterogeneous sub-populations, effectively balancing the exploration and exploitation capabilities. Furthermore, a potentially excellent gene activation (PEGA) mechanism is designed to update the archived position of gbest (Abest) by learning some excellence gene of individual particles to further enhance the GLS sub-population exploitation ability. A repulsive mechanism is incorporated into the CLS sub-population to prevent premature convergence and preserve diversity. Additionally, a local search operator based on the BFGS Quasi-Newton method is utilized to fine-tune the best solution during the later stages of evolution. To evaluate the performance of HGCLPSO, it is benchmarked against eight renowned PSO variants and six additional evolutionary algorithms using the CEC2014 and CEC2017 test suites as well as a real-world WSN coverage engineering problem. Experimental outcomes show that HGCLPSO obtains the optimal average rank in the majority of test problems, which verifies its robustness and competitiveness as an optimization tool for addressing continuous optimization tasks.