• Theory, Research and Development •

### Adaptive Equilibrium Optimizer Algorithm Combining Oscillating Tabu Search

LIU Chenghan, HE Qing

1. 1.College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China
2.Guizhou Big Data Academy, Guizhou University, Guiyang 550025, China
• Online:2022-05-15 Published:2022-05-15

### 融合振荡禁忌搜索的自适应均衡优化算法

1. 1.贵州大学 大数据与信息工程学院，贵阳 550025
2.贵州大学 贵州省公共大数据重点实验室，贵阳 550025

Abstract: In order to solve the problems of slow convergence speed and easy to be affected by local minimum in the optimization process of equilibrium optimizer（EO） algorithm, an adaptive equilibrium optimization algorithm（CfOEO） combining oscillating tabu search is proposed. Aiming at the problem of slow convergence speed caused by high initial randomness of EO algorithm, elite reverse learning is introduced to initialize the population to increase the searching ability of the algorithm. The local and global search ability of the algorithm is balanced by adjusting the convergence factor adaptively. Finally, an oscillation operator is introduced into the tabu search strategy to improve the ability of the algorithm to leap out of the local minimum. In the simulation experiment, 10 benchmark test functions, some CEC2014 test functions and the Wilcoxon rank sum test of the standard test functions are used to test the optimization performance of the CfOEO algorithm. The test results verify the robustness of the CfOEO algorithm.