An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Archiving With Guaranteed Convergence And Diversity In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
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Diversity plays an important role in evolutionary multi-objective optimization. Because of this, the development of mechanisms which provide and maintain diversity to multi-objective evolutionary algorithms (MOEAs) have been studied since their inception. Fitness sharing and niching are probably the most popular density estimator used with non-elitist MOEAs. However, the main downside of these techniques is the need to define the niche radius, which is a critical parameter that is not trivial to set. In recent years, the use of external archives to store the non-dominated solutions found by elitist MOEAs has become a popular choice. This has triggered more effective and simple diversity maintenance techniques for MOEAs. In this paper, we introduce a new archiving strategy based on the Convex Hull of Individual Minima (CHIM), which is intended to maintain a well-distributed set of non-dominated solutions. Our proposed approach is compared with NSGA-II using standard test problems and performance measures taken from the specialized literature.