Multiobjective cuckoo search for design optimization

Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may...

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Bibliographic Details
Published inComputers & operations research Vol. 40; no. 6; pp. 1616 - 1624
Main Authors Yang, Xin-She, Deb, Suash
Format Journal Article
LanguageEnglish
Published New York Elsevier Ltd 01.06.2013
Elsevier
Pergamon Press Inc
Subjects
Algorithms
Cuckoo search
Dealing
Design engineering
Design optimization
Heuristic
Mathematical analysis
Mathematical functions
Mathematical models
Mathematical problems
Metaheuristic
Multiobjective
Operations research
Optimization
Optimization algorithms
Searching
Studies
Multiobjective
Cuckoo search
Metaheuristic
Optimization
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Summary:Many design problems in engineering are typically multiobjective, under complex nonlinear constraints. The algorithms needed to solve multiobjective problems can be significantly different from the methods for single objective optimization. Computing effort and the number of function evaluations may often increase significantly for multiobjective problems. Metaheuristic algorithms start to show their advantages in dealing with multiobjective optimization. In this paper, we formulate a new cuckoo search for multiobjective optimization. We validate it against a set of multiobjective test functions, and then apply it to solve structural design problems such as beam design and disc brake design. In addition, we also analyze the main characteristics of the algorithm and their implications.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2011.09.026