EMOCA: An evolutionary multi-objective crowding algorithm

Abstract

A.new evolutionary algorithm is proposed for solving multi-objective optimization problems, focusing on the issue of developing a diverse population of non-dominated solutions. The key new approach in this algorithm is to use a diversity-emphasizing probabilistic approach in determining whether an off-spring individual is considered in the replacement selection phase, along with the use of a non-domination ranking scheme. This evolutionary multi-objective crowding algorithm (EMOCA) is evaluated using nine bench-mark multi-objective optimization problems and shown to produce non-dominated solutions with significant diversity, outperforming three state-of-the-art multi-objective evolutionary algorithms on most ofthe test problems.