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    Title: The development of a Sub-Population Genetic Algorithm II (SPGAII) for the Multi-objective Combinatorial Problems
    Authors: Chang, P.C.;Chen, S. H.
    Keywords: Genetic algorithm;Parallel scheduling problems;Multidimensional knapsack problem;Multi-objective optimization
    Date: 2009
    Issue Date: 2021-10-05 12:10:21 (UTC+8)
    Abstract: Previous research has shown that sub-population genetic algorithm is effective in solving the multi-objective combinatorial problems. Based on these pioneering efforts, this paper extends the SPGA algorithm with a global Pareto archive technique and a two-stage approach to solve the multi-objective problems. In the first stage, the areas next to the two single objectives are searched and solutions explored around these two extreme areas are reserved in the global archive for later evolutions. Then, in the second stage, larger searching areas except the middle area are further extended to explore the solution space in finding the near-optimal frontiers. Through extensive experimental results, SPGA II does outperform SPGA, NSGA II, and SPEA 2 in the parallel scheduling problems and knapsack problems; it shows that the approach improves the sub-population genetic algorithm significantly. It may be of interests for researchers in solving multi-objective combinatorial problems.
    Relation: Applied Soft Computing Journal 9(1), p.173-181
    DOI: 10.1016/j.asoc.2008.04.002
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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