multiobjective evolutionary optimization has been demonstrated to be an efficient method for some difficult multiobjective optimization problems;particularly the quadraticassignmentproblem which is a provably diffic...
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ISBN:
(纸本)0780393635
multiobjective evolutionary optimization has been demonstrated to be an efficient method for some difficult multiobjective optimization problems;particularly the quadraticassignmentproblem which is a provably difficult NP-complete problem with a multitude of real-world applications. This paper introduces the use of a segment-based external memory in evolutionary multiobjective optimization. In principle, variable-size solution segments taken from a number of previously promising solutions are stored in an external memory whose elements are used in the construction of new solutions. In the construction of a solution, a solution segment is retrieved from the external memory and used in the construction of complete solutions through evolutionary recombination operators. The aim is to provide further intensification around promising solutions without weakening the exploration capabilities. Different instances of the multiobjective quadratic assignment problem are used for performance evaluations and, almost in all trials, the proposed external memory strategy provided significantly better results than the multiobjective genetic algorithm (MOGA).
Evolutionary multiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a grea...
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ISBN:
(纸本)9781450326629
Evolutionary multiobjective optimization (EMO) has been successfully applied to various real-world scenarios with usually two or three contradicting optimization goals. However, several studies have pointed out a great deterioration of computational performance when handling more than three objectives. In order to improve the scalability of multiobjective evolutionary algorithms (MOEAs) onto higher-dimensional objective spaces, techniques using e.g. scalarizing functions and preference-or indicator-based guidance have been proposed. Most of those proposals require a-priori information or a decision maker during optimization, which increases the complexity of the algorithms. In this paper, we propose a divide and conquer method for many-objective optimization. First, we partition a problem into lower-dimensional subproblems for which standard algorithms are known to perform very well. Our key improvement is the sequential usage of MOEAs, utilizing the results of one suboptimization as initial population for another MOEA. This technique allows modular optimization phases and can be applied to common evolutionary algorithms. We test our enhanced method on the hard to solve multiobjective quadratic assignment problem (mQAP), using a variety of established MOEAs.
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