| Summary: | 碩士 === 國立臺灣大學 === 工業工程學研究所 === 92 === This thesis proposes an objective randomly switched strategy and genetic algorithms embedded method to solve multi-objective problems. Within each evolution procedure, the method randomly selects one objective function, from the problem, to compute the fitness for the genetic algorithm. Non-dominated solutions obtained from each evolution generation are stored and kept in a non-dominated solution set. In each evolution generation, domination competition between solutions is carried out within the population first and then against the stored non-dominated solution set. At first, Non-dominated solutions are identified and extracted from the population by carrying out a domination examination. The solutions from the population are then one by one competed with the solutions in the non-dominated set. Solutions in the set that are dominated by the solution from the population are discarded first. Then, the solutions from the population can be added to the non-dominated solution set only when they are not dominated by any solution in the set. To maintain population diversity, this methods replaces partial chromosomes with new chromosomes generated by inter- and extrapolation between pairs of solutions in the non-dominated solution set. This method is implemented in a software system, and other traditional methods are developed in the system as well, to facilitated results comparisons. Four brand-new evaluation factors for different solving methods are proposed and defined in this thesis. Five numerical examples are testes against our method and other methods. Results show that our method in general can obtain more and better non-dominated solutions than others.
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