Abstract: To address the problems of low solution accuracy and slow convergence speed of equilibrium optimizer,a bidirectional learning equilibrium optimizer combining sparrow search and random difference is presented.Firstly,an adaptive population division strategy based on sparrow search algorithm is proposed to balance the global exploration and local exploitation of the algorithm,so as to improve the convergence accuracy and convergence speed of the algorithm.Secondly,a random difference strategy is introduced to reconstruct the equilibrium pool and to increase the information exchange between individuals,so as to facilitate the algorithm to jump out of the local optimum.Finally,a bidirectional chaotic opposition learning strategy is designed and applied to the updated population to increase the population diversity and hence to further improve the convergence accuracy of the algorithm.Simulation experiments are conducted with 14 test functions,the performance of algorithm is evaluated using Wilcoxon rank-sum test and mean absolute error,and the improved algorithm is applied to two engineering design problems.Experimental results show that the three improvement strategies are effective and the convergence accuracy,convergence speed and robustness of the improved algorithm are significantly enhanced.

Key words: Equilibrium optimizer, Bidirectional chaotic opposition learning, Algorithm fusion, Random difference, Swarm intelligence optimization algorithms