This paper proposes the hybrid Indicator-based Directional-biased Evolutionary Algorithm (hIDEA) and verifies its effectiveness through the simulations of the multi-objective 0/1 knapsackproblem. Although the convent...
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ISBN:
(纸本)9783642158704
This paper proposes the hybrid Indicator-based Directional-biased Evolutionary Algorithm (hIDEA) and verifies its effectiveness through the simulations of the multi-objective 0/1 knapsackproblem. Although the conventional multi-objective Optimization Evolutionary Algorithms (MOEA5) regard the weights of all objective functions as equally, hIDEA biases the weights of the objective functions in order to search not only the center of true Pareto optimal solutions but also near the edges of them. Intensive simulations have revealed that hIDEA is able to search the Pareto optimal solutions widely and accurately including the edge of true ones in comparison with the conventional methods.
New techniques are presented to reduce the number of feasible alternatives in certain multiple criteria subset selection problems, thereby making it less difficult to find a good subset. The class of m-best alternativ...
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New techniques are presented to reduce the number of feasible alternatives in certain multiple criteria subset selection problems, thereby making it less difficult to find a good subset. The class of m-best alternatives problems is defined and the relation between dominance and potential optimality explored in the context of this class. A program is proposed to identify whether an individually dominated alternative can belong to an optimal subset satisfying certain pre-specified constraints. The extension of the proposed method to multi-objective knapsack problems is considered. Two examples illustrate the screening procedure for m-best alternatives problems and multi-objective knapsack problems.
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