Dual Population Genetic Algorithm for Solving Constrained Optimization Problems

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A. J. Umbarkar 1,* M. S. Joshi 2 P. D. Sheth 1

1. Department of Information Technology, Walchand College of Engineering, Sangli, 416-416, India

2. Department of Computer Engineering, Jawaharlal Nehru Engineering College, Aurangabad, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijisa.2015.02.05

Received: 27 Jun. 2014 / Revised: 1 Oct. 2014 / Accepted: 10 Nov. 2014 / Published: 8 Jan. 2015

Index Terms

Dual Population Genetic Algorithm, DPGA, Population Diversity, Metaheuristic Algorithms, Function Optimization, Constrained Optimization Problems, COPs


Dual Population Genetic Algorithm is an effective optimization algorithm that provides additional diversity to the main population. It addresses the premature convergence problem as well as the diversity problem associated with Genetic Algorithm. Thus it restricts their individuals to be trapped in the local optima. This paper proposes Dual Population Genetic Algorithm for solving Constrained Optimization Problems. A novel method based on maximum constrains satisfaction is applied as constrains handling technique and Dual Population Genetic Algorithm is used as meta-heuristic. This method is verified against 9 problems from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2006 Special Session on Constrained Real-Parameter Optimization problem set. The results are compared with existing algorithms such as Ant Bee Colony Algorithm, Differential Evolution Algorithm and Genetic Algorithm that have been used for solving same problem set. Analysis shows that this technique gives results close to optimum value but fails to obtain exact optimum solution. In future Dual Population Genetic Algorithm can produce more efficient solutions using alternative constrains handling technique.

Cite This Paper

A. J. Umbarkar, M. S. Joshi, P. D. Sheth, "Dual Population Genetic Algorithm for Solving Constrained Optimization Problems", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.2, pp.34-40, 2015. DOI:10.5815/ijisa.2015.02.05


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