Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
A new approach for online multiobjective optimization of mechatronic systems
International Journal on Software Tools for Technology Transfer (STTT)
Properties of an adaptive archiving algorithm for storing nondominated vectors
IEEE Transactions on Evolutionary Computation
Convergence of stochastic search algorithms to finite size pareto set approximations
Journal of Global Optimization
A new memetic strategy for the numerical treatment of multi-objective optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Computing gap free pareto front approximations with stochastic search algorithms
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
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
A fast steady-state ε-dominance multi-objective evolutionary algorithm
Computational Optimization and Applications
Information Sciences: an International Journal
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Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of ε-dominance. Though bounds on the quality of the limit approximation -- which are entirely determined by the archiving strategy and the value of ε -- have been obtained, the strategies do not guarantee to obtain a gap-free Pareto front approximation. Since such approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included into the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs we give somenumerical results to visualize the behavior of the different archiving strategies.