An Algorithm for Many-Objective Optimization With Reduced Objective Computations: A Study in Differential Evolution

In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict status and selects a subset of conflicting object...

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Bibliographic Details
Published in:IEEE transactions on evolutionary computation Vol. 19; no. 3; pp. 400 - 413
Main Authors: Bandyopadhyay, Sanghamitra, Mukherjee, Arpan
Format: Journal Article
Language:English
Published: New York IEEE 01.06.2015
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Convergence
Correlation
Correlation based ordering
Differential Evolution
Linear programming
Many-Objective Optimization
Objectives
Optimization
Sociology
Vectors
differential evolution
Correlation based ordering
many-objective optimization
structural optimization
elitism
ISSN:1089-778X, 1941-0026
Online Access:Get full text
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Summary:In this paper we have developed an algorithm for many-objective optimization problems, which will work more quickly than existing ones, while offering competitive performance. The algorithm periodically reorders the objectives based on their conflict status and selects a subset of conflicting objectives for further processing. We have taken differential evolution multiobjective optimization (DEMO) as the underlying metaheuristic evolutionary algorithm, and implemented the technique of selecting a subset of conflicting objectives using a correlation-based ordering of objectives. The resultant method is called α-DEMO, where α is a parameter determining the number of conflicting objectives to be selected. We have also proposed a new form of elitism so as to restrict the number of higher ranked solutions that are selected in the next population. The α-DEMO with the revised elitism is referred to as α-DEMO-revised. Extensive results of the five DTLZ functions show that the number of objective computations required in the proposed algorithm is much less compared to the existing algorithms, while the convergence measures are competitive or often better. Statistical significance testing is also performed. A real-life application on structural optimization of factory shed truss is demonstrated.
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ISSN:1089-778X
1941-0026
DOI:10.1109/TEVC.2014.2332878