Abstract:
Hypervolume-based evolutionary algorithms can effectively solve the multi-objective optimization problem and obtain promising solution sets with fast convergence and uniform distribution. However, this kind of algorithms have higher computational complexity and lower programming efficiency. Aiming at the two-dimensional and three-dimensional multi-objective optimization problems, this paper proposes a hypervolume-based multi-objective evolutionary algorithm (MOEA-HV) so that the individuals’ exclusive hypervolume contributions can be accurately calculated to guide the evolution of the whole population. Before the indicator-based evolutionary algorithm(IBEA) being utilized, the proposed algorithm employs non-dominated sorting among all individuals to delete dominated individuals so that the amount of calculation of the individuals’ exclusive hypervolume contributions can be reduced and the operational efficiency can be improved. Meanwhile, other strategies in NSGA-Ⅲ are utilized to optimize the distribution of the proposed algorithm. It is shown via the experiment results that the proposed MOEA-HV has higher efficiency while maintaining the trade-off between the fast convergence and the uniform distribution.