Quasi-oppositional Multi-objective Antlion Optimizer Based on Differential Evolution

Abstract In order to solve the multi-objective problem, an improved quasi-oppositional multi-objective antlion optimization algorithm based on differential evolution (DEQOMALO) is proposed. This algorithm overcomes the defect that antlion 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 ant and the elite antlion 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 then 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 antlion 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.