Hybrid Particle Swarm Optimization Algorithm Based on the Theory of Reinforcement Learning in Psychology

To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and systems, many intelligent methods have been proposed. Among these algorithms, particle swarm (PSO) algorithm has attracted scholars’ attention. However, traditional PSO can easily become an individual optimal solution, leading to transition of process from global exploration local development. this problem, paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on theory reinforcement learning psychology. First, used strategy optimize initial population initialization stage; then, chaotic adaptive weights factors were balance development process, solution obtained using dimension learning. Finally, improved mutation applied improve quality solution. The HRLPSO was tested by optimizing 12 benchmarks as well CEC2013 test suite, results show it ability social ability, verifying its effectiveness.