INFORMATION CHANGE THE WORLD

International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.4, No.10, Aug. 2017

Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non-Linear Optimization Problems

Full Text (PDF, 1544KB), PP.50-57


Views:188   Downloads:13

Author(s)

Saibal K. Pal, C.S Rai, Amrit Pal Singh

Index Terms

Metaheuristic Algorithm;Firefly Algorithm;PSO;Noisy Non-Linear Optimization

Abstract

There are various noisy non-linear mathematical optimization problems that can be effectively solved by Metaheuristic Algorithms. These are iterative search processes that efficiently perform the exploration and exploitation in the solution space, aiming to efficiently find near optimal solutions. Considering the solution space in a specified region, some models contain global optimum and multiple local optima. In this context, two types of meta-heuristics called Particle Swarm Optimization (PSO) and Firefly algorithms were devised to find optimal solutions of noisy non-linear continuous mathematical models. Firefly Algorithm is one of the recent evolutionary computing models which is inspired by fireflies behavior in nature. PSO is population based optimization technique inspired by social behavior of bird flocking or fish schooling. A series of computational experiments using each algorithm were conducted. The results of this experiment were analyzed and compared to the best solutions found so far on the basis of mean of execution time to converge to the optimum. The Firefly algorithm seems to perform better for higher levels of noise.

Cite This Paper

Saibal K. Pal, C.S Rai, Amrit Pal Singh,"Comparative Study of Firefly Algorithm and Particle Swarm Optimization for Noisy Non-Linear Optimization Problems", International Journal of Intelligent Systems and Applications(IJISA), vol.4, no.10, pp.50-57, 2012. DOI: 10.5815/ijisa.2012.10.06

Reference

[1]X. S. Yang, “Nature-Inspired Metaheuristic Algorithms”, Luniver Press, 2008.

[2]Christian Blum, Maria Jos´e Blesa Aguilera, Andrea Roli, Michael Sampels, Hybrid Metaheuristics, An Emerging Approach to Optimization, Springer, 2008 .

[3]WengKee Wong, Nature-Inspired Metaheuristic Algorithms for Generating Optimal Experimental Designs, 2011.

[4]Sh. M. Farahani, A. A. Abshouri, B. Nasiri, and M. R. Meybodi, “A Gaussian Firefly Algorithm”, International Journal of Machine Learning and Computing, Vol. 1, No. December 2011.

[5]N. Chai-ead, P. Aungkulanon, and P. Luangpai- boon, Member, IAENG, “Bees and Firefly Algorithms for NoisyNon-Linear Optimization Problems” Inter- national Multiconference of Engineers and Computer Scientists, 2011. 

[6]Hajo Broersma “Application of the Firefly Algorithm for Solving theEconomic Emissions Load Dispatch Problem, ”Hindawi Publishing Corporation, International Journal of Combinatorics, Volume 2011.

[7]Rania Hassan, Babak Cohanim, Olivier de Weck, A Comparison of the Particle Swarm Algorithm and the Genetic Algorithm, published by AIAA, 2004.

[8]Bajeh, A. O., Abolarinwa, K. O., A Comparative Study of Genetic and Tabu Search Algorithms, International Journal of Computer Applications, 2011.

[9]Xin-She Yang, Chaos-Enhanced Firefly Algorithm with Automatic Parameter Tuning, International Journal of Swarm Intelligence Research, December 2011.

[10]Xiang-yin Meng, Yu-long Hu, Yuan-hang Hou, Wen-quan Wang, The Analysis of Chaotic Particle Swarm Optimization and the Application in Preliminary Design of Ship”, International Conference on Mechatronics and Automation, August, 2010.

[11]J. Kennedy, R. C. Eberhart, “Particle swarm optimization”, IEEE International Conference on Neural Networks, Piscataway, NJ., pp.942-1948, 1995.