The Method to Integrate Species Explode and Deracinate Algorithm With Particle Swarm Optimization Algorithm

Effectively combining various evolutionary computing algorithms and leveraging the advantages of each can significantly enhance the convergence speed and solution quality of the algorithm. However, a mere combination of evolutionary computing algorithms may not comprehensively improve optimization performance and may even lead to poorer performance in certain optimization problems. The aim of the paper is to provide a fundamental integrating platform and method based on species explode and deracinate algorithm. Utilizing the species explode and deracinate algorithm as a foundation, this study presents a hybrid algorithm named SED-PSO algorithm by utilizing the particle swarm optimization algorithm as an exemplar. The outcomes of the simulations conducted on 27 benchmark functions published by the Competition on Evolutionary Constrained demonstrate that the SED-PSO algorithm exhibits exceptional convergence accuracy, robust stability, and rapid convergence speed. The simulation results comprehensively illustrate that the species explode and deracinate algorithm serves as a fundamental integrating platform for diverse evolutionary computing algorithms, while also incorporating the strengths of each algorithm. Additionally, the outcomes of the optimization of sensor network coverage reveal that the SED-PSO algorithm exhibits superior solution quality, minimal occurrence of local extremum, and enhanced stability and efficacy.