INFORMATION CHANGE THE WORLD

International Journal of Information Engineering and Electronic Business(IJIEEB)

ISSN: 2074-9023 (Print), ISSN: 2074-9031 (Online)

Published By: MECS Press

IJIEEB Vol.10, No.5, Sep. 2018

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

Full Text (PDF, 505KB), PP.40-45


Views:21   Downloads:0

Author(s)

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

Index Terms

Dual Population Genetic Algorithm;DPGA;Genetic Algorithm;GA;Evolutionary Algorithm;EA;Function Optimization;CEC’2013;k-Point Crossover

Abstract

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.

Cite This Paper

A. J. Umbarkar, L. R. Moon, P. D. Sheth," Comparative Study of CEC’2013 Problem Using Dual Population Genetic Algorithm", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.5, pp. 40-45, 2018. DOI: 10.5815/ijieeb.2018.05.06

Reference

[1]T. Park, K. Ruy, “A Dual-Population Genetic Algorithm for Adaptive Diversity Control”, IEEE transactions on evolutionary computation, vol. 14, no. 6, 2010 DOI:10.1109/TEVC.2010.2043362. 

[2]A.J. Umbarkar, M.S. Joshi, P.D. Sheth, “Diversity-Based Dual-Population Genetic Algorithm (DPGA): A Review” In Proceedings of Fourth International Conference on Soft Computing for Problem Solving, (AISC, vol 335). 2015. DOI https://doi.org/10.1007/978-81-322-2217-0_19

[3]T. Park and K. Ruy, “A dual population genetic algorithm with evolving diversity”, in Proc. IEEE Congr. Evol. Comput., pp. 3516–3522, 2007. DOI: 10.1109/CEC.2007.4424928.

[4]T. Park and K. Ruy, “Adjusting population distance for dual-population genetic algorithm,” in Proc. of Aust. Joint Conf. Artif. Intell., pp. 171–180, 2007.  DOI: 10.1109/TEVC.2010.2043362.

[5]T. Park, R. Choe, K. Ruy, “Dual-population Genetic Algorithm for Nonstationary Optimization”, in Proc. GECCO’08 ACM, 2008, pp.1025-1032.  DOI: 10.1145/1389095.1389286.

[6]A. Umbarkar, M. Joshi, “Dual Population Genetic Algorithm (GA) versus OpenMP GA for Multimodal Function Optimization”, International Journal of Computer Applications, 64(19):29-36, February 2013. DOI: 10.5120/10744-5516.

[7]M. Zambrano-Bigiarini, M. Clerc and R. Rojas, "Standard Particle Swarm Optimisation 2011 at CEC-2013: A baseline for future PSO improvements," Evolutionary Computation (CEC), 2013 IEEE Congress on , vol., no., pp.2337,2344, 20-23 June 2013. doi: 10.1109/CEC.2013.6557848.

[8]M. Mehra, M. Jayalal, A. John Arul, S. Rajeswari, K. Kuriakose, S. Murty, “Study on Different Crossover Mechanisms of Genetic Algorithm for Test Interval Optimization for Nuclear Power Plants”, IJISA, vol.6, no.1, 2014, pp.20-28. DOI: 10.5815/ijisa.2014.01.03.

[9]J. Liang, B. Qu, P. Suganthan, A. Hernández-Díaz, “Problem definitions and evaluation criteria for the CEC 2013 special session on real-parameter optimization”, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore, Technical Report 2013, vol. 201212.

[10]S. Elsayed, R. Sarker and D. Essam, "A genetic algorithm for solving the CEC'2013 competition problems on real-parameter optimization," Evolutionary Computation (CEC), 2013 IEEE Congress on , vol., no., pp.356,360, 20-23 June 2013. DOI: 10.1109/CEC.2013.6557591.

[11]C. Worasucheep, “A Particle Swarm Optimization with  Diversity-Guided Convergence Acceleration and Stagnation Avoidance”, in Proc. of 8th International Conference on Natural Computation (ICNC 2012), pp.733-738, 2012. DOI: 10.1109/ICNC.2012.6234647.

[12]B. Qu, P. Suganthan, “Constrained Multi-Objective Optimization Algorithm with Diversity Enhanced Differential Evolution”, IEEE conference on evolutionary computation (CEC), 2010. DOI: 10.1109/CEC.2010.5585947.