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Hyper-volume evolutionary algorithm

Le, Khoi Nguyen; Landa-Silva, Dario


Khoi Nguyen Le


We propose a multi-objective evolutionary algorithm (MOEA), named the Hyper-volume Evolutionary Algorithm (HVEA). The algorithm is characterised by three components. First, individual fitness evaluation depends on the current Pareto front, specifically on the ratio of its dominated hyper-volume to the current Pareto front hyper-volume, hence giving an indication of how close the individual is to the current Pareto front. Second, a ranking strategy classifies individuals based on their fitness instead of Pareto dominance, individuals within the same rank are non-guaranteed to be mutually non-dominated. Third, a crowding assignment mechanism that adapts according to the individual’s neighbouring area, controlled by the neighbouring area radius parameter, and the archive of non-dominated solutions. We perform extensive experiments on the multiple 0/1 knapsack problem using different greedy repair methods to compare the performance of HVEA to other MOEAs including NSGA2, SEAMO2, SPEA2, IBEA and MOEA/D. This paper shows that by tuning the neighbouring area radius parameter, the performance of the proposed HVEA can be pushed towards better convergence, diversity or coverage and this could be beneficial to different types of problems.


Le, K. N., & Landa-Silva, D. (2016). Hyper-volume evolutionary algorithm

Journal Article Type Article
Acceptance Date Feb 1, 2016
Publication Date Mar 1, 2016
Deposit Date Aug 1, 2016
Publicly Available Date Aug 1, 2016
Journal VNU journal of science: computer science and communication engineering
Electronic ISSN 0866-8612
Peer Reviewed Not Peer Reviewed
Volume 32
Issue 1
Keywords Multi-objective evolutionary alogorithm, Pareto optimization, Hyper-volume, Knapsack problem
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Copyright Statement
Copyright information regarding this work can be found at the following address:

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