Multiobjective Prototype Optimization with Evolved Improvement Steps
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Abstract
Recently, a new iterative optimization framework utilizing an evolutionary algorithm called ”Prototype Optimization with Evolved iMprovement Steps” (POEMS) was introduced, which showed good performance on hard optimization problems - large instances of TSP and real-valued optimization problems. Especially, on discrete optimization problems such as the TSP the algorithm exhibited much better search capabilities than the standard evolutionary approaches. In many real-world optimization problems a solution is sought for multiple (conflicting) optimization criteria. This paper proposes a multiobjective version of the POEMS algorithm (mPOEMS), which was experimentally evaluated on the multiobjective 0/1 knapsack problem with alternative multiobjective evolutionary algorithms. Major result of the experiments was that the proposed algorithm performed comparable to or better than the alternative algorithms.
Keywords
multiobjective optimization evolutionary algorithms multiobjective 0/1 knapsack problemPreview
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