Improve Multi-objective Ant Lion Optimizer Based on Quasi-oppositional and Levy Fly

Abstract

In order to solve the problem that classic ant lion algorithm is easy to fall into local optimum, a quasi-opposite Levy fly multi-objective ant lion algorithm is proposed. In this algorithm, Levy fly is used to replace the ant lion random walk mechanism in the original algorithm. The combination of Brownian motion and jumping flight is used to improve the ability of local exploitation and global exploration. At the same time, after each time iterate, the ant population is optimized by quasi-opposite learning strategy. The original population and its quasi-opposite individuals are mixed and the best individuals are selected as the new population. In this way, the diversity of the population is increased, as well as, the computational efficiency of the algorithm is improved. Finally, typical benchmarks are selected to compare the algorithm with the original ant lion algorithm and other swarm intelligence algorithms. Experimental results show that both convergence and distribution of the proposed algorithm are greatly improved. The quasi-opposite Levy fly multi-objective ant lion algorithm has good adaptability and effectiveness in solving the two-objective optimization problem.