A fast steady‐state e‐dominance multi‐objective evolutionary algorithm

Multi‐objective evolutionary algorithms (MOEAs) have become an increasingly popular tool for design and optimization tasks in real‐world applications. Most of the popular baseline algorithms are pivoted on the use of Pareto‐ranking (that is empirically inefficient) to improve the convergence to the Pareto front of a multi‐objective optimization problem. This paper proposes a new ϵ‐dominance MOEA (EDMOEA) which adopts pair‐comparison selection and steady‐state replacement instead of the Pareto‐ranking. The proposed algorithm is an elitist algorithm with a new preservation technique of population diversity based on the ϵ‐dominance relation. It is demonstrated that superior results could be obtained by the EDMOEA compared with other algorithms: NSGA‐II, SPEA2, IBEA, ϵ‐MOEA, PESA and PESA‐II on test problems. The EDMOEA is able to converge to the Pareto optimal set much faster especially on the ZDT test functions with a large number of decision variables.