Convergence of set-based multi-objective optimization, indicators and deteriorative cycles

DOI10.1016/J.TCS.2012.05.036zbMath1254.90202OpenAlexW2025784084MaRDI QIDQ714857

Rudolf Berghammer, Tobias Friedrich, Frank Neumann

Publication date: 11 October 2012

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2012.05.036

zbMATH Keywords

convergencemulti-objective optimizationperformance measuresevolutionary algorithmshypervolume indicatorset-based optimization

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (3)

A note on the \(\epsilon\)-indicator subset selection ⋮ Runtime analysis of a multi-objective evolutionary algorithm for obtaining finite approximations of Pareto fronts ⋮ Population size matters: rigorous runtime results for maximizing the hypervolume indicator

Uses Software

• SPEA2
• SMS-EMOA
• weightedHypervolume

Cites Work

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• Bioinspired computation in combinatorial optimization. Algorithms and their computational complexity
• Plateaus can be harder in multi-objective optimization
• SMS-EMOA: multiobjective selection based on dominated hypervolume
• Convergence of stochastic search algorithms to finite size Pareto set approximations
• Approximating the volume of unions and intersections of high-dimensional geometric objects
• On the convergence of multiobjective evolutionary algorithms
• Tight Bounds for the Approximation Ratio of the Hypervolume Indicator
• Monotonicity versus performance in co-optimization

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