Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design...

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Bibliographic Details
Published in:Journal of Applied Mathematics 2015, Vol.2015 (2015), p.44-55-004
Main Authors: Sabarinath, P., Saravanan, R., Thansekhar, M. R.
Format: Article
Language:English
Subjects:
Adaptive algorithms
Algorithms
Design engineering
Design optimization
Mathematical analysis
Mathematical models
Mathematical optimization
Mathematical research
Methods
Nonlinearity
Optimization
Search algorithms
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Summary:The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.
ISSN:1110-757X
1687-0042
DOI:10.1155/2015/165601