Convergence of stochastic search algorithms to finite size Pareto set approximations

DOI10.1007/s10898-007-9265-7zbMath1152.90598OpenAlexW2098593811MaRDI QIDQ934805

El-Ghazali Talbi, Oliver Schütze, Carlos A. Coello Coello, Michael Dellnitz, Marco Laumanns

Publication date: 30 July 2008

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/inria-00119255/file/RR-6063.pdf

zbMATH Keywords

Multi-objective optimizationConvergenceStochastic search algorithms

Mathematics Subject Classification ID

Probabilistic models, generic numerical methods in probability and statistics (65C20) Multi-objective and goal programming (90C29) Management decision making, including multiple objectives (90B50)

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A study of Liu-Storey conjugate gradient methods for vector optimization ⋮ On the convergence of adaptive stochastic search methods for constrained and multi-objective black-box optimization ⋮ A quasi-Newton method with Wolfe line searches for multiobjective optimization ⋮ Spectral conjugate gradient methods for vector optimization problems ⋮ On the extension of the Hager-Zhang conjugate gradient method for vector optimization ⋮ Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts ⋮ Convergence of multi-objective evolutionary algorithms to a uniformly distributed representation of the Pareto front ⋮ Multicriteria optimization with a multiobjective golden section line search ⋮ A surrogate-based multiobjective metaheuristic and network degradation simulation model for robust toll pricing ⋮ Stochastic convergence of random search methods to fixed size Pareto front approximations ⋮ Convergence of set-based multi-objective optimization, indicators and deteriorative cycles ⋮ Conditional gradient method for multiobjective optimization ⋮ Globally convergent Newton-type methods for multiobjective optimization

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