EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
On the performance metrics of multiobjective optimization
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
Recombination of similar parents in SMS-EMOA on many-objective 0/1 knapsack problems
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
Iterated multi-swarm: a multi-swarm algorithm based on archiving methods
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
A novel evolutionary algorithm inspired by the states of matter for template matching
Expert Systems with Applications: An International Journal
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In evolutionary multiobjective optimization, the task of the optimizer is to obtain an accurate and useful approximation of the true Pareto-optimal front. Proximity to the front and diversity of solutions within the approximation set are important requirements. Most established multiobjective evolutionary algorithms (MOEAs) have mechanisms that address these requirements. However, in many-objective optimization, where the number of objectives is greater than 2 or 3, it has been found that these two requirements can conflict with one another, introducing problems such as dominance resistance and speciation. In this paper, two diversity management mechanisms are introduced to investigate their impact on overall solution convergence. They are introduced separately, and in combination, and tested on a set of test functions with an increasing number of objectives (6-20). It is found that the inclusion of one of the mechanisms improves the performance of a well-established MOEA in many-objective optimization problems, in terms of both convergence and diversity. The relevance of this for many-objective MOEAs is discussed.