Abstract An improved non-dominated sorting genetic algorithm (INSGA) is introduced for multi-objective optimization. In order to keep the diversity of the population, a modified elite preservation strategy is adopted and the evaluation of solutions’ crowding degree is integrated in crossover operations during the evolution. The INSGA is compared with the NSGA-II and other algorithms by applications to five classical test functions and an environmental/economic dispatch (EED) problem in power systems. It is shown that the Pareto solution obtained by INSGA has a good convergence and diversity.


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    Title :

    An Improved Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization Based on Crowding Distance


    Contributors:


    Publication date :

    2014-01-01


    Size :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English





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