A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection. (22nd December 2021) - Shen, Xiaoning; You, Xuan; Huang, Yao; Guo, Yinan
A local region enhanced multi-objective fireworks algorithm with subpopulation cooperative selection (LREMOFWA) is proposed for multi-objective optimisation. In LREMOFWA, the ranking based on the non-dominated sorting and the crowding distance is taken as the fitness evaluation indicator. A novel way to calculate the explosion amplitude is designed to enhance the search for the local region. The concept of subpopulation is introduced, and the selection operation is performed by the elites in the archive cooperating with the optimal subpopulation sparks. The differential mutation operator is utilised to deal with the repeated individuals in the fireworks, which prevents the algorithm from falling into the local optimum. The proposed algorithm is compared with five state-of-the-art algorithms on the WFG test functions. Experimental results show that the proposed algorithm has better performance with respect to the searching accuracy and diversity. It is suitable for solving multi-objective function optimisation problems with various complex characteristics.