Abstract
A Pareto optimal set, which is obtained from solving multi-objective optimisation problems, usually contain a large number of optimal solutions. This situation poses a challenge for decision makers in choosing a suitable solution from a large number of overlapping and complex Pareto solutions. This paper proposes a new procedure for solving multi-objective optimisation problems by reducing the size of the Pareto set. The procedure is divided into two major stages. In the first stage, the multi-objective simulated annealing algorithm is used to solve a multi-objective optimisation problem by constructing the Pareto optimal set. In the second stage, the automatic clustering algorithm is used to prune the Pareto set. This procedure is implemented to solve two multi-objective optimisation problems, namely, the 0/1 multi-objective multi-dimensional knapsack problem and the multi-objective inventory system. The procedure enables the decision maker to select an appropriate solution efficiently.
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