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Frontiers in Computing Technologies for Manufacturing ApplicationsMulti-objective Optimization Through Soft Computing Approaches

Frontiers in Computing Technologies for Manufacturing Applications: Multi-objective Optimization... Multi-objective Optimization Through Soft Computing Approaches 3.1 Introduction Recently, agile and flexible manufacturing has been required to deal with diversified customer demands and global competition. The multi-objective optimization has been gaining interest as a decision aid sutable for those challenges. Accordingly, its importance might be intensified especially for real world problems in many fields. In this section, new methods for a multi- objective optimization problem (MOP) will be presented associated with the metaheuristic methods and the soft computing techniques. Generally, we can describe the MOP as a triplet like (x, f, x), similar to the usual single-objective optimization. However, it should be noticed that the objective function in this case is not a scalar but a vector. Consequently, the MOP is written, in general, by [Problem]min f (x)= {f (x),f (x),...,f (x)} 1 2 N subject to x ∈ X, where x denotes an n-dimensional decision variable vector, X a feasible region defined by a set of constraints, and f an N -dimensional objective function vector, some elements of which conflict and are incommensurable with each other. The conflicts occur when if one tries to improve a certain objective func- tion, at least one of the other objective functions http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Frontiers in Computing Technologies for Manufacturing ApplicationsMulti-objective Optimization Through Soft Computing Approaches

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Publisher
Springer London
Copyright
© Springer-Verlag London Limited 2007
ISBN
978-1-84628-954-5
Pages
77 –124
DOI
10.1007/978-1-84628-955-2_3
Publisher site
See Chapter on Publisher Site

Abstract

Multi-objective Optimization Through Soft Computing Approaches 3.1 Introduction Recently, agile and flexible manufacturing has been required to deal with diversified customer demands and global competition. The multi-objective optimization has been gaining interest as a decision aid sutable for those challenges. Accordingly, its importance might be intensified especially for real world problems in many fields. In this section, new methods for a multi- objective optimization problem (MOP) will be presented associated with the metaheuristic methods and the soft computing techniques. Generally, we can describe the MOP as a triplet like (x, f, x), similar to the usual single-objective optimization. However, it should be noticed that the objective function in this case is not a scalar but a vector. Consequently, the MOP is written, in general, by [Problem]min f (x)= {f (x),f (x),...,f (x)} 1 2 N subject to x ∈ X, where x denotes an n-dimensional decision variable vector, X a feasible region defined by a set of constraints, and f an N -dimensional objective function vector, some elements of which conflict and are incommensurable with each other. The conflicts occur when if one tries to improve a certain objective func- tion, at least one of the other objective functions

Published: Jan 1, 2007

Keywords: Pareto Front; Multiobjective Optimization; Soft Computing; Pareto Optimal Solution; Common Gateway Interface

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