Hypervolume-based multiobjective optimization: Theoretical foundations and practical implications
TL;DR: General investigations for finite @m are presented, a limit result for @m going to infinity is derived in terms of a density of points and lower bounds for placing the reference point to guarantee the Pareto front's extreme points in an optimal @m-distribution are derived.
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About: This article is published in Theoretical Computer Science. The article was published on 01 Mar 2012. and is currently open access. The article focuses on the topics: Multi-objective optimization & Optimization problem.
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Citations
Hype: An algorithm for fast hypervolume-based many-objective optimization
Johannes Bader,Eckart Zitzler +1 more
TL;DR: This paper presents HypE, a hypervolume estimation algorithm for multi-objective optimization, by which the accuracy of the estimates and the available computing resources can be traded off; thereby, not only do many-Objective problems become feasible with hypervolume-based search, but also the runtime can be flexibly adapted.
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Performance indicators in multiobjective optimization
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TL;DR: A review of a total of 63 performance indicators partitioned into four groups according to their properties: cardinality, convergence, distribution and spread is proposed.
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Coevolutionary Particle Swarm Optimization With Bottleneck Objective Learning Strategy for Many-Objective Optimization
TL;DR: This work proposes a coevolutionary particle swarm optimization with a bottleneck objective learning (BOL) strategy for many-objective optimization, and develops a solution reproduction procedure with both an elitist learning strategy and a juncture learning strategy to improve the quality of archived solutions.
A Survey on the Hypervolume Indicator in Evolutionary Multiobjective Optimization
TL;DR: This article aims to fill the gap and provide a comprehensive survey on the hypervolume indicator and help EMO researchers to understand thehypervolume indicator more deeply and thoroughly, and promote further utilization of the hyper volume indicator in the EMO field.
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•Book
Evolutionary optimization algorithms : biologically-Inspired and population-based approaches to computer intelligence
Dan Simon
- 01 Jan 2013
TL;DR: This paper presents a meta-anatomy of evolutionary algorithms and some examples of successful and unsuccessful attempts at optimization in the context of discrete-time programming.
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References
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Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.
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Evolutionary algorithms for solving multi-objective problems
Gary B. Lamont,David A. Van Veldhuizen +1 more
- 30 Jun 2002
TL;DR: This paper presents a meta-anatomy of the multi-Criteria Decision Making process, which aims to provide a scaffolding for the future development of multi-criteria decision-making systems.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
TL;DR: This paper provides a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions and shows that elitism is shown to be an important factor for improving evolutionary multiobjectives search.
Performance assessment of multiobjective optimizers: an analysis and review
TL;DR: This study provides a rigorous analysis of the limitations underlying this type of quality assessment in multiobjective evolutionary algorithms and develops a mathematical framework which allows one to classify and discuss existing techniques.
Comparison of multiobjective evolutionary algorithms: empirical results
Eckart Zitzler,Kalyanmoy Deb,Lothar Thiele +2 more
- 01 Jan 1999
TL;DR: In this article, the authors provide a systematic comparison of various evolutionary approaches to multiobjective optimization using six carefully chosen test functions, each test function involves a particular feature that is known to cause difficulty in the evolutionary optimization process, mainly in converging to the Pareto-optimal front.
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