Evolutionary many-objective optimisation: an exploratory analysis

Purshouse, Robin C.; Fleming, Peter J.

doi:10.1109/cec.2003.1299927

Cited by 153 publications

(84 citation statements)

References 11 publications

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“…The bad performance of early Pareto-based methods like NSGA-II and SPEA2 observed by Hughes [3] and Purshouse and Fleming [2] is confirmed. They show a rapid degradation with increasing number of objectives.…”

Section: Discussionsupporting

confidence: 52%

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Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Wagner¹,

Beume

Naujoks

Lecture Notes in Computer Science

336

223

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Abstract. Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two-and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-ofthe-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with highdimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.

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Section: Discussionsupporting

confidence: 52%

“…Few previous studies on many-objective optimization by Purshouse and Fleming [2] and Hughes [3] focus to demonstrate the bad performance of NSGA-II by Deb et al [4]. Hughes observed a simple single-objective restart strategy outperforming NSGA-II on a six-objective function in a two-dimensional decision space.…”

Section: Introductionmentioning

confidence: 99%

Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Wagner¹,

Beume

Naujoks

Lecture Notes in Computer Science

336

223

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show abstract

“…MOEAs are being used to solve many-objective problems in a rapidly growing body of literature as their population-based search enables the direct approximation of problems' Pareto frontiers in a single optimization run (Purshouse and Fleming 2003, Aguirre et al 2013, Lygoe et al 2013, Morino and Obayashi 2013. For example, while solving a five-objective problem, the MOEAs simultaneously solve the five single-objective problems, ten two-objective problems, ten three-objective problems, and five four-objective problems.…”

Section: Evolutionary Multiobjective Optimizationmentioning

confidence: 99%

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Singh¹,

Reed²,

Keller³

2015

E&S

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ABSTRACT. Managing ecosystems with deeply uncertain threshold responses and multiple decision makers poses nontrivial decision analytical challenges. The problem is imbued with deep uncertainties because decision makers do not know or cannot converge on a single probability density function for each key parameter, a perfect model structure, or a single adequate objective. The existing literature on managing multistate ecosystems has generally followed a normative decision-making approach based on expected utility maximization (MEU). This approach has simple and intuitive axiomatic foundations, but faces at least two limitations. First, a prespecified utility function is often unable to capture the preferences of diverse decision makers. Second, decision makers' preferences depart from MEU in the presence of deep uncertainty. Here, we introduce a framework that allows decision makers to pose multiple objectives, explore the trade-offs between potentially conflicting preferences of diverse decision makers, and to identify strategies that are robust to deep uncertainties. The framework, referred to as many-objective robust decision making (MORDM), employs multiobjective evolutionary search to identify trade-offs between strategies, re-evaluates their performance under deep uncertainty, and uses interactive visual analytics to support the selection of robust management strategies. We demonstrate MORDM on a stylized decision problem posed by the management of a lake in which surpassing a pollution threshold causes eutrophication. Our results illustrate how framing the lake problem in terms of MEU can fail to represent key trade-offs between phosphorus levels in the lake and expected economic benefits. Moreover, the MEU strategy deteriorates severely in performance for all objectives under deep uncertainties. Alternatively, the MORDM framework enables the discovery of strategies that balance multiple preferences and perform well under deep uncertainty. This decision analytic framework allows the decision makers to select strategies with a better understanding of their expected trade-offs (traditional uncertainty) as well as their robustness (deep uncertainty).

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“…[30,31,32,33]). According to [32], other important concerns are the so-called Dominance Resistant Solutions (e.g.…”

Section: A Brief Outline and Some Criticisms Of Previous Approachesmentioning

confidence: 99%

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

Cruz¹,

Fernández²,

Gómez³

et al. 2014

Appl. Math. Inf. Sci.

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Project portfolio selection is one of the most important problems faced by any organization. The decision process involves multiple conflicting criteria, and has been commonly addressed by implementing a two-phase procedure. The first step identifies the efficient solution set; the second step supports the decision maker in selecting only one portfolio solution from the efficient set. However, several recent studies show the advantages gained by optimizing towards a region of interest (according to the decision maker's preferences) instead of approximating the complete Pareto set. However, these works have not faced synergism and its variants, such as cannibalization and redundancy. In this paper we introduce a new approach called Non-Outranked Ant Colony Optimization, which optimizes interdependent project portfolios with a priori articulation of decision-maker preferences based on an outranking model. Several experimental tests show the advantages of our proposal over the two-phase approach, providing reasonable evidence of its potential for solving real-world high-scale problems with many objectives.

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Evolutionary many-objective optimisation: an exploratory analysis

Cited by 153 publications

References 11 publications

Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization

Many-objective robust decision making for managing an ecosystem with a deeply uncertain threshold response

Many-Objective Portfolio Optimization of Interdependent Projects with ‘a priori’ Incorporation of Decision-Maker Preferences

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