Dynamic constrained multi-objective model for solving constrained optimization problem

Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly,...

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Bibliographic Details
Published in:2011 IEEE Congress of Evolutionary Computation (CEC) pp. 2041 - 2046
Main Authors: Sanyou Zeng, Shizhong Chen, Jiang Zhao, Aimin Zhou, Zhengjun Li, Hongyong Jing
Format: Conference Proceeding
Language:English
Published: IEEE 01.06.2011
Subjects:
Algorithm design and analysis
Asynchronous transfer mode
Benchmark testing
Constrained optimization
Dynamic multi-objective optimization
Dynamic optimization
Evolutionary algorithm
Evolutionary computation
Heuristic algorithms
Multi-objective optimization
Optimization
Search problems
ISBN:1424478340, 9781424478347
ISSN:1089-778X
Online Access:Get full text
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Summary:Constrained optimization problem (COP) is skillfully converted into dynamic constrained multi-objective optimization problem (DCMOP) in this paper. Then dynamic constrained multi-objective evolutionary algorithms (DCMOEAs) can be used to solve the COP problem by solving the DCMOP problem. Seemingly, a complex DCMOEA algorithm is used to solve a relatively simple COP problem. However, the DCMOEA algorithm can adopt Pareto domination to achieve a good trade off between fast converging and global searching, and therefore a DCMOEA algorithm can effectively solve a COP problem by solving the DCMOP problem. An instance of DCMOEA was used to to solve 13 widely used constraint benchmark problems, The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper. The efficient performance of the DCMOEA algorithm shows, to some extend, the DCMOP model works well.
ISBN:1424478340
9781424478347
ISSN:1089-778X
DOI:10.1109/CEC.2011.5949866