An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization

Pierro, Francesco Di; Khu, Soon‐Thiam; Savić, Dragan

doi:10.1109/tevc.2006.876362

Cited by 265 publications

(124 citation statements)

References 20 publications

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“…Note that the edge points identified as the maximal mutually non-dominating points after projection onto subsets of the criteria have also been considered by di Pierro et al in their k-preference ordering [13], in which a many-objective point is considered important if it remains non-dominated when projected onto criterion subsets. Although somewhat counter-intuitive we have shown that points which remain non-dominated under maximisation after projection onto criterion subsets are also important, because they too are on the edges of the set.…”

Section: Resultsmentioning

confidence: 99%

Edges of mutually non-dominating sets

Everson

Walker

Fieldsend

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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Multi-objective optimisation yields an estimated Pareto front of mutually non-dominating solutions, but with more than three objectives understanding the relationships between solutions is challenging. Natural solutions to use as landmarks are those lying near to the edges of the mutually nondominating set. We propose four definitions of edge points for many-objective mutually non-dominating sets and examine the relations between them.The first defines edge points to be those that extend the range of the attainment surface. This is shown to be equivalent to finding points which are not dominated on projection onto subsets of the objectives. If the objectives are to be minimised, a further definition considers points which are not dominated under maximisation when projected onto objective subsets. A final definition looks for edges via alternative projections of the set.We examine the relations between these definitions and their efficacy for synthetic concave-and convex-shaped sets, and on solutions to a prototypical many-objective optimisation problem, showing how they can reveal information about the structure of the estimated Pareto front.

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Section: Resultsmentioning

confidence: 99%

Edges of mutually non-dominating sets

Everson

Walker

Fieldsend

2013

Proceedings of the 15th Annual Conference on Genetic and Evolutionary Computation

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“…The consideration of multiple objectives poses the potential for broader understanding of performance trade-offs, but requires commensurate effort in advancing the value of these results for aiding decision makers (Balling, 1999;Castelletti et al, 2010a;di Pierro et al, 2007;Kasprzyk et al, 2013;Kollat and Reed, 2007b;Lotov and Miettinen, 2008;Woodruff et al, 2013). For example, for two-or three-dimensional problems, Pareto optimal trade-offs can be visualised relatively easily.…”

Section: Overviewmentioning

confidence: 99%

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Maier

Kapelan

Kasprzyk

et al. 2014

Environmental Modelling & Software

530

317

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In all cases accepted manuscripts should:  link to the formal publication via its DOI  bear a CC-BY-NC-ND license -this is easy to do, click here to find out how  if aggregated with other manuscripts, for example in a repository or other site, be shared in alignment with our hosting policy  not be added to or enhanced in any way to appear more like, or to substitute for, the published journal article AbstractThe development and application of evolutionary algorithms (EAs) and other metaheuristics for the optimisation of water resources systems has been an active research field for over two decades. Research to date has emphasized algorithmic improvements and individual applications in specific areas (e.g., model calibration, water distribution systems, groundwater management, river-basin planning and management, etc.). However, there has been limited synthesis between shared problem traits, common EA challenges, and needed advances across major applications. This paper clarifies the current status and future research directions for better solving key water resources problems using EAs. Advances in understanding fitness landscape properties and their effects on algorithm performance are critical. Future EA-based applications to real-world problems require a fundamental shift of focus towards improving problem formulations, understanding general theoretic frameworks for problem decompositions, major advances in EA computational efficiency, and most importantly aiding real decision-making in complex, uncertain application contexts.

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“…Some of the most successful EMOAs [16] rely on Pareto dominance classification as a fitness measure to guide selection of the new population. The work [17] indicates that resorting to Pareto dominance classification to assign fitness becomes ineffective for increasing number of objectives and proposes a refined preference ordering based on the notion of order of efficiency [18].…”

Section: From Evolutionary Algorithms To Brain-computer Optimizationmentioning

confidence: 99%

Decision-Making in Complicated Geometrical Problems

Mosavi¹

2014

IJCA

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Due to increasing the number of decision-making criteria in today's ever complicated geometrical optimization problems, the traditional multiobjective optimization approaches, whether a priori, a posteriori or interactive's, found to be insufficient and ineffective. In this paper the drawbacks of the current algorithms are reviewed and the urgent need for inserting a learning component in the optimization loop is discussed. In the following the methodology of reactive optimization for evolutionary interactive multiobjective optimization for solving complicated geometrical decisionmaking problems is adopted. The proposed brain-computer optimization follows to the paradigm of learning while optimizing, through the use of online machine learning techniques as an integral part of a self-tuning optimization scheme. At the end the effectiveness of the approach to geometrical problems is emphasized by providing the study case of optimal design problem of curves and surfaces.

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An Investigation on Preference Order Ranking Scheme for Multiobjective Evolutionary Optimization

Cited by 265 publications

References 20 publications

Edges of mutually non-dominating sets

Edges of mutually non-dominating sets

Evolutionary algorithms and other metaheuristics in water resources: Current status, research challenges and future directions

Decision-Making in Complicated Geometrical Problems

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