Top-level dual exploitation particle swarm optimization

This paper proposes top-level dual-exploitation particle swarm optimization (TLDEPSO), which aims to use the evolutionary experience between particles better and enhance the convergence performance of the algorithm. In TLDEPSO, the population is divided into top-level particles and ordinary particles according to fitness, and each iteration is divided into two stages to be executed. For the first stage, a particle modification method based on gene editing technology is proposed and applied to top-level particles to improve the search direction of the population and explore the problem space better. For other ordinary particles in the population, the learning strategy of the canonical ring neighborhood topology PSO is used to update the velocity and the position to maintain the diversity of the population. For the second stage, a top-level neighborhood exploration mechanism is proposed for top-level particles to accelerate the algorithm’s convergence. In addition, an adaptive dynamic adjustment mechanism for the parameters of acceleration coefficient, inertia coefficient and the number of top-level particles is proposed to balance better the global exploration and local exploitation capabilities of the algorithm. On the latest CEC2022 test benchmark, comparison and statistical analysis with seven advanced PSO variants and three CEC competition top algorithms demonstrate TLDEPSO’s superior performance in solving functional problems with different fitness landscapes.