In recent years, evolutionary algorithms based on the concept of “decomposition” have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems.
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April 2017
Research-Article
A Novel Decomposition-Based Evolutionary Algorithm for Engineering Design Optimization
Kalyan Shankar Bhattacharjee,
Kalyan Shankar Bhattacharjee
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: k.bhattacharjee@student.adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: k.bhattacharjee@student.adfa.edu.au
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Hemant Kumar Singh,
Hemant Kumar Singh
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: h.singh@adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: h.singh@adfa.edu.au
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Tapabrata Ray
Tapabrata Ray
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: t.ray@adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: t.ray@adfa.edu.au
Search for other works by this author on:
Kalyan Shankar Bhattacharjee
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: k.bhattacharjee@student.adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: k.bhattacharjee@student.adfa.edu.au
Hemant Kumar Singh
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: h.singh@adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: h.singh@adfa.edu.au
Tapabrata Ray
School of Engineering and Information
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: t.ray@adfa.edu.au
Technology,
The University of New South Wales,
Canberra 2600, Australia
e-mail: t.ray@adfa.edu.au
1Corresponding author.
Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received July 25, 2016; final manuscript received January 16, 2017; published online February 23, 2017. Assoc. Editor: Harrison M. Kim.
J. Mech. Des. Apr 2017, 139(4): 041403 (11 pages)
Published Online: February 23, 2017
Article history
Received:
July 25, 2016
Revised:
January 16, 2017
Citation
Shankar Bhattacharjee, K., Kumar Singh, H., and Ray, T. (February 23, 2017). "A Novel Decomposition-Based Evolutionary Algorithm for Engineering Design Optimization." ASME. J. Mech. Des. April 2017; 139(4): 041403. https://doi.org/10.1115/1.4035862
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