Journal of Optimization Theory and Applications
A D.C. biobjective location model
Journal of Global Optimization
Stochastic method for the solution of unconstrained vector optimization problems
Journal of Optimization Theory and Applications
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Approximating the Ɛ-efficient set of an MOP with stochastic search algorithms
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Approximate Solutions in Space Mission Design
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
Computing gap free pareto front approximations with stochastic search algorithms
Evolutionary Computation
Robust design of embedded systems
Proceedings of the Conference on Design, Automation and Test in Europe
Information Sciences: an International Journal
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In this work we study the convergence of generic stochastic search algorithms toward the entire set of approximate solutions of continuous multi-objective optimization problems. Since the dimension of the set of interest is typically equal to the dimension of the parameter space, we focus on obtaining a finite and tight approximation, measured by the Hausdorff distance. Under mild assumptions about the process to generate new candidate solutions, the limit approximation set will be determined entirely by the archiving strategy. We propose and investigate a novel archiving strategy theoretically and empirically. For this, we analyze the convergence behavior of the algorithm, yielding bounds on the obtained approximation quality as well as on the cardinality of the resulting approximation, and present some numerical results.