L. R. Moon

Work place: Department of Information Technology, Walchand College of Engineering Sangli, MS, India

E-mail: latikmoon@gmail.com

Website:

Research Interests: Evolutionary Computation, Parallel Computing, Data Structures and Algorithms, Programming Language Theory

Biography

Latik R. Moon has completed Bachelor of Technology (B. Tech. 2013) in Computer Science and Engineering from SGGSIE&T, Nanded, MS, India; and Master of Technology (M. Tech. 2015) in Information Technology from Walchand College of Engineering, Sangli, MS, India. His research interests are Parallel Programming, High Performance Computing, GPU Programming and Evolutionary Algorithms.

Author Articles
Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm

By A. J. Umbarkar L. R. Moon P. D. Sheth

DOI: https://doi.org/10.5815/ijieeb.2018.05.06, Pub. Date: 8 Sep. 2018

Evolutionary Algorithms (EAs) are found to be effective for solving a large variety of optimization problems. In this Paper Dual Population Genetic Algorithm (DPGA) is used for solving the test functions of Congress on Evolutionary Computation 2013 (CEC’2013), by using two different crossovers. Dual Population Genetic Algorithm is found to be better in performance than traditional Genetic Algorithm. It is also able to solve the problem of premature convergence and diversity of the population in genetic algorithm. This paper proposes Dual Population Genetic Algorithm for solving the problem regarding unconstrained optimization. Dual Population Genetic Algorithm is used as meta-heuristic which is verified against 28 functions from Problem Definitions and Evaluation Criteria for the Congress on Evolutionary Computation 2013 on unconstrained set of benchmark functions using two different crossovers. The results of both the crossovers are compared with each other. The results of both the crossovers are also compared with the existing results of Standard Particle Swarm Optimization algorithm. The Experimental results showed that the algorithm found to be better for finding the solution of multimodal functions of the problem set.

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