Improved Salp Swarm Algorithm with Simulated Annealing for Solving Engineering Optimization Problems

Swarm-based algorithm can successfully avoid the local optimal constraints, thus achieving a smooth balance between exploration and exploitation. Salp swarm (SSA), as swarm-based on account of predation behavior salp, solve complex daily life optimization problems in nature. SSA also has stagnation slow convergence rate. This paper introduces an improved salp algorithm, which improve by using chaotic sequence initialization strategy symmetric adaptive population division. Moreover, simulated annealing mechanism based perturbation is introduced to enhance jumping ability algorithm. The referred SASSA. CEC standard benchmark functions are used evaluate efficiency SASSA results demonstrate that better global search capability. applied engineering problems. experimental exploratory exploitative proclivities proposed its patterns vividly improved.