An improved antlion optimizer with dynamic random walk and dynamic opposite learning

Abstract

Antlion optimization algorithm (ALO) has been one of the most popular meta-heuristic algorithms to solve engineering optimization problems with a fast convergence speed and steady performance. However, the long runtime and the weak capability of local optima avoidance prevent ALO from being applied in real-time and complex engineering problems. This paper proposes a dynamic random walk and dynamic opposite learning improved ALO (DALO), where a new random walk method named dynamic random walk (DRW) is presented, and a dynamic opposite learning strategy (DOL) is embedded in ALO. The DOL-inspired DRW method has a better exploration capability for converging to the best optimum due to the dynamic changing search space and is more efficient in computation due to the low complexity. The DOL strategy contributes to two steps of the ALO algorithm, i.e. DOL population initialization and DOL generation jumping, for further enhancing the algorithm to avoid premature convergence. The improvements of DRW and DOL were verified by testing benchmark problems from ALO paper, CEC 2014 and CEC 2017, and an optimal weight factor of DRW was selected by conducting a sensitivity analysis. The results, confirmed by Wilcoxon rank-sum test and Friedman test, show that DRW helps ALO greatly reduce the runtime with an excellent performance in finding the best optimum, and the DALO algorithm with the further enhancement enable by DOL is significantly superior to ALO. In addition, the results of the test on three representative engineering problems show that the ability of DALO to solve real-world problems is enhanced.