关键词: 优化方法, 元启发式算法, 平衡优化算法, 差分变异, 机器人路径规划

Abstract: Equilibrium Optimizer (EO) is a recently proposed excellent metaheuristic algorithm, but it encounters issues such as insufficient search ability, poor operability, and low search efficiency when solving complex optimization problems. Therefore, this paper proposes an improved EO, namely Differential mutation and Territorial search EO (DTEO). Firstly, a differential mutation method with territorial search is proposed to update the concentration of the best particle. Then, an elite-worst individual particle differential mutation strategy is proposed to strengthen the worst individual. Finally, a differential mutation strategy with information sharing and a simplified concentration updating way in EO are proposed and integrated dynamically to update the other particles' centralizations to improve the operability and search ability of the algorithm and reduce the running time. The experimental results on the complex functions from CEC2014 test set demonstrate that compared with EO and other excellent algorithms, DTEO has stronger search ability, higher efficiency, and stronger operability. Experimental results on robot path planning also show DTEO is more competitive. 

Key words: optimization method, meta-heuristic algorithm, equilibrium optimizer, differential mutation, robot path planning