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

摘要：

为解决多目标优化问题，对经典的蚁狮算法进行改进，提出了基于差分进化的准对立学习多目标蚁狮算法（DEQOMALO）。该算法针对蚁狮算法易陷入局部最优的不足，一方面，该算法引用差分进化的思想，充分利用种群和精英蚁狮的信息对原算法中蚂蚁个体的位置更新方式进行改进；另一方面采用反向学习策略对蚂蚁种群进行优化，将原种群个体和其准对立个体进行混合并择优作为新的种群，大大增加种群的多样性。选取典型的标准测试函数，将提出的算法与原始蚁狮算法以及其他传统进化策略优化的蚁狮算法进行比较。实验结果表明，改进算法在收敛性和分布性上均有很大程度的提升，在解决双目标优化问题上具有较好的鲁棒性和有效性。

关键词: 多目标优化, 蚁狮算法, 差分进化, 准对立策略