An Improved Lion Swarm Optimization Algorithm Based on Tent-map and Differential Evolution

Abstract

Aiming at the poor optimization performance of traditional Lion Swarm optimization algorithm, an improved algorithm is proposed based on Tent-map and differential evolution. Firstly, to address the problem of uneven population distribution and low efficiency in the later search stage, the chaotic sequence is introduced to improve the diversity and uniform traversal of the population so as to enhance the global search capability. Secondly, owing to the algorithm is prone to local optimum and unsatisfactory convergence accuracy, the lioness position update method is improved by the differential evolution to enhance its ability to jump out of the local optimum and boost the optimization accuracy. Experiments are carried out on 8 representative multi type benchmark functions, and compared with 4 optimization algorithms. Results show that the improved algorithm has higher convergence speed, training accuracy and stability.