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Proceedings of GO 2005, pp. 115 – 120.Global multiobjective optimization using evolutionarymethods: An experimental analysisC. Gil, 1 R. Baños, 1 M. G. Montoya, 1 A. Márquez, 1 and J. Ortega 21 Departamento de Arquitectura de Computadores y Electrónica, Universidad de Almería, La Cañada de San Urbano s/n,04120 Almería, Spain, {cgil,rbanos,mari,amarquez}@ace.ual.es2 Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus de Fuentenueva s/n,Granada, Spain, julio@atc.ugr.esAbstractKeywords:The objective of this work is to compare the performance of several recent multiobjective optimizationalgorithms (MOEAs) with a new hybrid algorithm. The main attraction of these algorithms isthe integration of selection and diversity maintenance. Since it is very difficult to exactly describewhat a good approximation is in terms of a number of criteria, the performance is quantified withspecific metrics and is based on two main aspects: the proximity of solutions to the global Paretofont and the suitable distribution of the located front.multiobjective evolutionary optimization, global Pareto-optimal front.1. IntroductionThe aim of Global Optimization (GO) is to find the best solution of decision models, in presenceof the multiple local solutions. Having several objective functions, the notion of optimumchanges, because in Multiobjective Optimization Problems (MOPs) we are really trying to findgood compromises (or trade-offs) rather than a single solution as in global optimization. Sincemost of the real design problems involve the achievement of several objectives, then the presenceof multiple objectives in a problem, in principle, gives rise to a set of optimal solutions(largely known as Pareto-optimal solutions), instead of a single optimal solution [2]. In theabsence of any further information, one of these Pareto-optimal solutions cannot be said to bebetter than the other. This demands a user to find as many Pareto-optimal solutions as possible.Classical optimization methods (including the multicriterion decision-making methods)suggest converting the multiobjective optimization problem to a single-objective optimizationproblem by emphasizing one particular Pareto-optimal solution at a time. When such amethod is to be used for finding multiple solutions, it has to be applied many times, hopefullyfinding a different solution at each simulation run.Generating the Pareto set can be computationally expensive and is often infeasible, becausethe complexity of the underlying application prevents exact methods from being applicable.For this reason, a number of stochastic search strategies such as evolutionary algorithms, tabusearch, simulated annealing, and ant colony optimization have been developed. Over the pastdecade, a number of multiobjective evolutionary algorithms (MOEAs) have been suggested[3–6, 9]. The primary reason for this is their ability to find multiple Pareto-optimal solutionsin one single simulation run. Since evolutionary algorithms (EAs) work with a population ofsolutions, a simple EA can be extended to maintain a diverse set of solutions. With an emphasisfor moving toward the global Pareto-optimal region, an EA can be used to find multiple