关键词: 多目标优化, 蚁狮算法, 差分进化, 准对立策略

Abstract:

In order to solve the multi-objective problem, an improved Quasi-Oppositional Multi-Objective Ant Lion Optimization algorithm based on Differential Evolution（DEQOMALO） is proposed. This algorithm overcomes the defect that ant lion algorithm is easy to fall into local optimum. On the one hand, this algorithm uses the idea of differential evolution to make full use of the information of the population and the elite ant lion to improve the position updating method of the original algorithm. On the other hand, the population is optimized by quasi-opposite learning strategy, and the original population and its quasi-opposite individuals are mixed and selected as the new population, which greatly increases the diversity of the population. Finally, typical benchmarks are selected to compare the algorithm with the original ant lion algorithm and other MALO algorithms with traditional evolution strategies. Experimental results show that both convergence and distribution of the improved algorithm are greatly improved. The proposed DEQOMALO algorithm has good adaptability and effectiveness in solving the two-objective optimization problem.

Key words: multi-objective optimization, ant lion optimizer, differential evolution, quasi-oppositional learn strategy