An Improved Particle Swarm Optimization Algorithm Combined with Bat Algorithm

In this paper, an improved Particle swarm optimization algorithm combined with Bat algorithm is proposed to solve global optimization problems. The inspiration of Bat algorithm comes from the Animal echolocation behavior of bats in nature. Because of its effectiveness, Bat algorithm has been studied by many scholars and has been applied to engineering optimization, economic scheduling, classification and other problems. However, when dealing with complex optimization problems, bat optimization algorithms tend to fall into local optimization. This paper combines the Bat algorithm (BA) with the comprehensive learning particle swarm algorithm (CLPSO), proposes an improved particle swarm algorithm(BCLPSO), and tests it on CEC2017 function. The experimental results show that the improved particle swarm optimization algorithm outperforms other comparative algorithms in terms of testing functions. In addition, the algorithm has been successfully applied to the mathematical modeling problem of multi disc clutch brake design.