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.
An Improved Non-dominated Sorting Genetic Algorithm for Multi-objective Optimization Based on Crowding Distance
2014-01-01
11 pages
Article/Chapter (Book)
Electronic Resource
English
genetic algorithm , crowding distance , elite preservation , multiobjective optimization Computer Science , Artificial Intelligence (incl. Robotics) , Computational Intelligence , Computer Imaging, Vision, Pattern Recognition and Graphics , Simulation and Modeling , System Performance and Evaluation
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