Many-Objective Optimization: An Engineering Design Perspective

Fleming, Peter; Purshouse, Robin C.; Lygoe, Robert J.

doi:10.1007/978-3-540-31880-4_2

Cited by 267 publications

(157 citation statements)

References 18 publications

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“…8,9) EAs use a population to seek optimal solutions in parallel. This feature can be extended to seek Pareto solutions in parallel without specifying weights between the objective functions.…”

Section: Introductionmentioning

confidence: 99%

“…Due to these advantages, EAs have been Ó 2007 The Japan Society for Aeronautical and Space Sciences actively applied to MOPs. [6][7][8][9] EAs have been also applied to single-objective and multi-objective aerospace design optimization problems. 2,[10][11][12][13] This approach to finding many Pareto solutions works fine as it is, however, only when the number of objectives remains small (usually two, three at most, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…To reveal trade-off information from the resultant Pareto front for real-world problems with many objectives, visualization of the Pareto front becomes an issue. Several techniques have been considered, such as parallel coordinates, 8) box plot, 14) and self-organizing maps (SOM). 15) Because such visualization is a tool for data mining, data mining is found very important in this approach.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Obayashi

Jeong

Chiba

et al. 2007

Trans. Japan Soc. Aero. S Sci.

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A new approach, Multi-Objective Design Exploration (MODE), is presented to address multidisciplinary design optimization (MDO) problems using computational fluid dynamics-computational structural dynamics (CFD-CSD) coupling. MODE reveals the structure of the design space from the trade-off information and visualizes it as a panorama for Decision Maker. The present form of MODE consists of the Kriging Model, adaptive range multi objective genetic algorithms, analysis of variance and a self-organizing map. The main emphasis of this approach is visual data mining. An MDO system using high-fidelity simulation codes, a Navier-Stokes solver and NASTRAN has been developed and applied to a regional-jet wing design. Because the optimization system becomes very expensive computationally, only brief exploration of the design space has been performed. However, visual data mining results demonstrate that design knowledge can produce a good design even after brief design exploration.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Obayashi

Jeong

Chiba

et al. 2007

Trans. Japan Soc. Aero. S Sci.

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“…However, some theoretical work has shown that it is difficult or impossible to create an internally consistent aggregation function to achieve this (Franssen, 2005). At the other end of the spectrum, there is an emerging paradigm for "many" objective analysis of more than four objectives (Fleming et al, 2005;Woodruff et al, 2013). However, the inclusion of too many objectives could prove problematic.…”

Section: Current Statusmentioning

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

504

307

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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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“…(Fleming et al, 2005;Corne and Knowles, 2007;Ishibuchi et al, 2008;Brockhoff and Zitzler, 2009;Bader and Zitzler, 2011)). A promising approach in evolutionary many-objective optimization is objective reduction based on the Dominates relation ).…”

Section: Introductionmentioning

confidence: 99%

Incorporating user preferences in many-objective optimization using relation ε-preferred

Drechsler

Sülflow²,

Drechsler

2014

Nat Comput

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Abstract:During the last 10 years, many-objective optimization problems, i.e. optimization problems with more than three objectives, are getting more and more important in the area of multi-objective optimization. Many realworld optimization problems consist of more than three mutually dependent subproblems, that have to be considered in parallel. Furthermore, the objectives have different levels of importance. For this, priorities have to be assigned to the objectives. In this paper we present a new model for many-objective optimization called Prio-ε-Preferred, where the objectives can have different levels of priorities or user preferences. This relation is used for ranking a set of solutions such that an ordering of the solutions is determined. Prio-ε-Preferred is controlled by a parameter ε, that is problem specific and has to be adjusted experimentally by the designer. Therefore we also present an extension called Adapted-ε-Preferred (AEP), that determines the ε values automatically without any user interaction. To demonstrate the efficiency of our approach, experiments are performed.

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Many-Objective Optimization: An Engineering Design Perspective

Cited by 267 publications

References 18 publications

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

Multi-Objective Design Exploration and Its Application to Regional-Jet Wing Design

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

Incorporating user preferences in many-objective optimization using relation ε-preferred

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