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Multiobjective optimization and multiple constraint handling with evolutionary algorithms I

A unified formulation

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Published by Administrator in University of Sheffield, Dept. of Automatic Control and Systems Engineering

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        StatementUniversity of Sheffield, Dept. of Automatic Control and Systems Engineering
        PublishersUniversity of Sheffield, Dept. of Automatic Control and Systems Engineering
        Classifications
        LC Classifications1995
        The Physical Object
        Paginationxvi, 134 p. :
        Number of Pages51
        ID Numbers
        ISBN 10nodata
        Series
        1
        2Research report (University of Sheffield. Department of Automatic Control and Systems Engineering) -- no.564.
        3Research report / University of Sheffield. Department of Automatic Control and Systems Engineering -- no.564

        nodata File Size: 5MB.


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Multiobjective optimization and multiple constraint handling with evolutionary algorithms I by University of Sheffield, Dept. of Automatic Control and Systems Engineering Download PDF EPUB FB2


This study illustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The paper concludes with a discussion of the results. After reviewing current evolutionary approaches to multi-objective and constrained optimization, the paper proposes that fitness assignment be interpreted as, or at least related to, a multicriterion decision process.

Different techniques to implement these strongly related concepts will be discussed, and further important aspects such as constraint handling and preference articulation are treated as well. Several objective functions and associated goals express design concerns in direct form, i. Several objective functions and associated goals express design concerns in direct….

Abstract The evolutionary approach to Multiobjective optimization and multiple constraint handling with evolutionary algorithms I function optimization formulated in the first part of the paper [1] is applied to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine.

Niche formation techniques are used to promote diversity among preferable candidates, and progressive ar-ticulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Part A For part I see ibid. The evolutionary approach to multiple function optimization formulated in the first part of the paper is applied to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine.

A suitable decision making framework based on goals and priorities is subse-quently formulated in terms of a relational operator, char-acterized, and shown to encompass a number of simpler deci-sion strategies. This study illustrates how a technique such as the multiobjective genetic algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. Requirements such as continuity and differentia-bility of the cost surface add yet another conflicting element to the decision process.

Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making.

CiteSeerX — Evolutionary Algorithms for Multiobjective Optimization

In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective.as the designer would state them. Evolutionary algorithms EAswhich have found appli-cation in many areas not amenable to optimization by other methods, possess many characteristics desirable in a mul-tiobjective optimizer, most notably the concerted handling of multiple candidate solutions.

Abstract Abstract — In optimization, multiple objectives and con-straints cannot be handled independently of the underlying optimizer.