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1 Foundations of Computing and Decision Sciences

1 2002

1 Suman B.

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Multiobjective simulated annealing - a metaheuristic technique for multiobjective optimization of a constrained problem

100%

Suman B.

2002

tom Vol. 27, No. 3

171-191

The paper presents a multiobjective metaheuristic procedure - Weight based Multiobjective Simulated Annealing (WMOSA). The aim is to produce a set of potentially Pareto-optimal solutions of a constrained multiobjective optimization problem in a short time. In this method, the weight vector depends on the number of constraints to be satisfied by the solution vector and by the objective function vector, and the number of constraints of the problem. The weight vector is used in the acceptance criterion to handle constraints. Solution explores its neighborhood in a way similar to that of Classical Simulated Annealing. A computational experiment shows that WMOSA algorithm produces Pareto-optimal solutions of better quality than Suppapitanrm Multiobjective Simulated Annealing (SMOSA) with a penalty function approach at a lower computational cost.