Utopian point based decomposition for multi-objective optimization problems with complicated Pareto fronts |
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| Affiliation: | 1. School of Internet, Anhui University, Hefei 230039, China;2. Key Laboratory of Intelligent Computing & Signal Processing (ICSP), Ministry of Education, Anhui University, Hefei 230601, China;3. School of Artificial Intelligence, Anhui University, Hefei 230601, China;1. Center for Control Theory and Guidance Technology, Harbin Institute of Technology, Harbin 150001, China;2. Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory, Beijing Electro-mechanical Engineering Institute, Beijing 100074, China |
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| Abstract: | Multi-objective evolutionary algorithm based on decomposition (MOEA/D) has been considered as a promising method for solving multi-objective optimization problems (MOPs). It devotes most of its effort on convergence by optimizing a set of scalar optimization subproblems in a collaborative manner, while maintaining the diversity by using a set of uniformly distributed weight vectors. However, more recent studies illustrated that MOEA/D faces difficulties on MOPs with complicated Pareto fronts, mainly because the uniformity of weight vectors no longer lead to an evenly scattered approximation of the Pareto fronts in these cases. To remedy this, we suggest replacing the ideal point in the reciprocal Tchebycheff decomposition method with a more optimistic utopian point, with the aim of alleviating the sensitivity of MOEA/D to the Pareto front shape of MOPs. Experimental studies on benchmark and real-world problems have shown that such simple modification can significantly improve the performances of MOEA/D with reciprocal Tchebycheff decomposition on MOPs with complicated Pareto fronts. |
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| Keywords: | Evolutionary multi-objective optimization Decomposition method Utopian point Complicated Pareto fronts |
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