Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm

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DOI: 10.4236/am.2017.83032    422 Downloads   512 Views  


We develop a new evolutionary method of generating epsilon-efficient solutions of a continuous multiobjective programming problem. This is achieved by discretizing the problem and then using a genetic algorithm with some derived probabilistic stopping criteria to obtain all minimal solutions for the discretized problem. We prove that these minimal solutions are the epsilon-optimal solutions to the original problem. We also present some computational examples illustrating the efficiency of our method.

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Rahmo, E. and Studniarski, M. (2017) Generating Epsilon-Efficient Solutions in Multiobjective Optimization by Genetic Algorithm. Applied Mathematics, 8, 395-409. doi: 10.4236/am.2017.83032.


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