Memory-based adaptive partitioning (MAP) of search space for the enhancement of convergence in Pareto-based multi-objective evolutionary algorithms |
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Affiliation: | 1. Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street Holmes 383, Honolulu, HI 96822, USA;2. Seawater Utilization Plant Research Center (SUPRC), Korea Research Institute of Ships & Ocean Engineering, 124-32, Simcheungsu-gil, Jukwang-myeon, Goseong-gun, Gangwon-do 219-822, Republic of Korea |
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Abstract: | A new algorithm, dubbed memory-based adaptive partitioning (MAP) of search space, which is intended to provide a better accuracy/speed ratio in the convergence of multi-objective evolutionary algorithms (MOEAs) is presented in this work. This algorithm works by performing an adaptive-probabilistic refinement of the search space, with no aggregation in objective space. This work investigated the integration of MAP within the state-of-the-art fast and elitist non-dominated sorting genetic algorithm (NSGAII). Considerable improvements in convergence were achieved, in terms of both speed and accuracy. Results are provided for several commonly used constrained and unconstrained benchmark problems, and comparisons are made with standalone NSGAII and hybrid NSGAII-efficient local search (eLS). |
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Keywords: | Multi-objective evolutionary algorithms Memory-based adaptive partitioning Convergence improvement |
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