Hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism

During recent decades, multi-objective optimization has aroused extensive attention, and a variety of related algorithms have been proposed. A hybrid multi-objective optimization algorithm based on angle competition and neighborhood protection mechanism (HCPMOEA) is proposed in this paper. First, an environmental selection strategy based on neighborhood protection is introduced to make great compromises between optimization performance and time consumption. Then, the difference between Genetic algorithm and Differential evolution is analyzed from the perspective of offspring distribution and a hybrid operator is proposed to obtain good balances between exploration and exploitation. Besides, an elite set is employed to improve chances of the superior solutions generating offspring, and angle competition strategy is adopted to realize optimization matching of parents, thus improving the quality of offspring. The performance of HCPMOEA has been proved by comparing with 13 classic or state-of-the-arts algorithms on 19 standard benchmark, and the corresponding results show the competitive advantages in effectiveness and efficiency. In addition, the practicality of the proposed HCPMOEA is further verified by two real-world instances. Therefore, all of the aforementioned results have proved the superiority of the proposed HCPMOEA in solving bi-objective and tri-objective problems.