Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems

This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic...

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Bibliographic Details
Published in:IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 37; no. 3; pp. 560 - 575
Main Authors: Wang, Yong, Cai, Zixing, Guo, Guanqi, Zhou, Yuren
Format: Journal Article
Language:English
Published: United States IEEE 01.06.2007
Subjects:
Acceleration
Algorithms
Artificial Intelligence
Benchmark testing
Biological Evolution
Clustering algorithms
Computer Simulation
Constrained optimization
Constraint optimization
Convergence
evolutionary algorithm (EA)
Evolutionary computation
Genetic algorithms
global search
local search
Mathematical Computing
Measurement
Models, Genetic
Models, Theoretical
multiobjective optimization
Partitioning algorithms
Robustness
ISSN:1083-4419
Online Access:Get full text
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Summary:This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In performing the global search, a niching genetic algorithm based on tournament selection is proposed. Also, HCOEA has adopted a parallel local search operator that implements a clustering partition of the population and multiparent crossover to generate the offspring population. Then, nondominated individuals in the offspring population are used to replace the dominated individuals in the parent population. Meanwhile, the best infeasible individual replacement scheme is devised for the purpose of rapidly guiding the population toward the feasible region of the search space. During the evolutionary process, the global search model effectively promotes high population diversity, and the local search model remarkably accelerates the convergence speed. HCOEA is tested on 13 well-known benchmark functions, and the experimental results suggest that it is more robust and efficient than other state-of-the-art algorithms from the literature in terms of the selected performance metrics, such as the best, median, mean, and worst objective function values and the standard deviations
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ISSN:1083-4419
DOI:10.1109/TSMCB.2006.886164