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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, 107 p. :
        Number of Pages83
        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: 10MB.


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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


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. The two instances of the problem studied demonstrate the need for preference articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample effectively.

CiteSeerX — Evolutionary Algorithms for Multiobjective Optimization

Abstract Multiple, often conflicting objectives arise naturally in most real-world optimization scenarios. The focus is on the major issues such as fitness assignment, diversity preservation, and elitism in general rather than on particular algorithms. The paper concludes with the formulation of a multiob-jective genetic algorithm based on the proposed decision strategy.

Finally, two applications will presented and some recent trends in the field will be outlined. Meanwhile evolutionary multiobjective optimization has become established as a separate subdiscipline combining the fields of evolutionary computation and classical multiple criteria decision making.

In this paper, the basic principles of evolutionary multiobjective optimization are discussed from an algorithm design perspective. However, EAs are essen-tially unconstrained search techniques which require the as-signment of a scalar measure of quality, or fitness, to such candidate solutions. 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 multiple 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.as the designer would state them.

While such a designer-oriented formulation is very attractive, its practical usefulness depends heavily on the ability to search and optimize cost surfaces in a class much broader than usual, as already provided to a large extent by the Genetic Algorithm GA. 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.

As evolutionary algorithms possess several characteristics due to which they are well suited to this type of problem, evolution-based methods have been used for multiobjective optimization for more than a decade. The effect of preference changes on the cost surface seen by an EA is illustrated graphically for a simple problem.

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.

Requirements such as continuity and differentia-bility of the cost surface add yet another conflicting element to the decision process. Finally, the ranking of an arbitrary number of candidates is considered.