Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism

Abstract

In this paper, based on the sand cat swarm optimization (SCSO) algorithm, a dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism (EDSCSO) is proposed. EDSCSO aims to solve the problems of the original SCSO, such as the limited diversity of the population, low efficiency of solving complex functions, and ease of falling into a local optimal solution. First, an escape mechanism was proposed to balance the exploration and exploitation of the algorithm. Second, a random elite cooperative guidance strategy was used to utilize the elite population to guide the general population to improve the convergence speed of the algorithm. Finally, the dual-path differential perturbation strategy is used to continuously perturb the population using two differential variational operators to enrich population diversity. EDSCSO obtained the best average fitness for 27 of 39 test functions in the IEEE CEC2017 and IEEE CEC2019 test suites, indicating that the algorithm is an efficient and feasible solution for complex optimization problems. In addition, EDSCSO is applied to optimize the three-dimensional wireless sensor network coverage as well as the unmanned aerial vehicle path planning problem, and it provides optimal solutions for both problems. The applicability of EDSCSO in real-world optimization scenarios was verified.