Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (13): 156-163.DOI: 10.3778/j.issn.1002-8331.1903-0320

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Differential Evolutionary Quasi-Oppositional Multi-objective Ant Lion Optimizer

WANG Yadong, SHI Quan, YOU Zhifeng, SONG Weixing   

  1. Department of Equipment Command and Management, Army Engineering University of PLA, Shijiazhuang 050003, China
  • Online:2020-07-01 Published:2020-07-02



  1. 陆军工程大学 装备指挥与管理系,石家庄 050003


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



关键词: 多目标优化, 蚁狮算法, 差分进化, 准对立策略