Evolutionary Algorithms of Multi-Objective Optimization Problems

Multi-objective optimization (MOO) has become an important research area of evolutionary computations in recent years, and the current research work focuses on the Pareto optimal-based MOO evolutionary approaches. The evolutionary MOO techniques are used to find the non-dominated set of solutions and distribute them uniformly in the Pareto front. After comparing and analyzing the developing history of evolutionary MOO techniques, this paper takes the multi-objective genetic algorithm as an example and introduces the main techniques and theoretical results for the Pareto optimal-based evolutionary approaches, mainly focusing on the preference based-individual ordering, fitness assignment, fitness sharing and niche size setting etc.. In addition, some problems that deserve further studying are also addressed.