Ant Lion-Based Random Walk Differential Evolution Algorithm for Optimization and Clustering

Ant lion optimization algorithm (ALO) is a swarm-based metaheuristic for optimization inspired by the nature of ant lion hunting. One of the main step of hunting is the random walk of ants around the ant lion, which ensures ALO to possess a good local searching ability. Differential evolution (DE) is an evolutionary algorithm with a structure including mutation, crossover, and selection. The operations of DE are randomly executed which makes DE suffering from week exploiting ability. In this paper, a hybrid differential evolution based on the random walk of ants around the ant lion is presented, which combines the advantages of ant lion optimization algorithm and differential evolution, aiming to well balance the exploitation and exploration of the search. The hybrid algorithm is tested on CEC'17 benchmark suit and clustering problems. Experimental results verify the superiority of the proposed algorithm in comparison with other related algorithms.