Multi-objective Brainstorming Optimization Algorithm Based on Adaptive Mutation Strategy

Multi-objective Problems (MOP) is a classic combinatorial optimization problem. A brainstorming optimization algorithm based on multiple adaptive mutation methods in multiple regions of the population (DE_MOBSO) is proposed in this paper to solve the MOP. Firstly, the algorithm uses differential mutation to evolve the population, which can improve the diversity of population. Secondly, an adaptive mutation learning factor is introduced on the mutations to enhance the search efficiency of the algorithm considering the characteristics of the MOP. The effectiveness and practicability of the algorithm are verified by a set of simulation example. The results show that the proposed algorithm has better performance in solving large-scale MOP.