IJISA Vol. 7, No. 12, 8 Nov. 2015

Cover page and Table of Contents: PDF (size: 1191KB)

Full Text (PDF, 1191KB), PP.1-22

Views: 0 Downloads: 0

Gravitational Search Algorithm, Real Coded Genetic Algorithm operators, Laplace Crossover, Power Mutation, continuous optimization

The objective of this paper is to propose three modified versions of the Gravitational Search Algorithm for continuous optimization problems. Although the Gravitational Search Algorithm is a recently introduced promising memory-less heuristic but its performance is not so satisfactory in multimodal problems particularly during the later iterations. With a view to improve the exploration and exploitation capabilities of GSA, it is hybridized with well-known real coded genetic algorithm operators. The first version is the hybridization of GSA with Laplace Crossover which was initially designed for real coded genetic algorithms. The second version is the hybridization of GSA with Power Mutation which also was initially designed for real coded genetic algorithms. The third version hybridizes the GSA with both the Laplace Crossover and the Power mutation. The performance of the original GSA and the three proposed variants is investigated over a set of 23 benchmark problems considered in the original paper of GSA. Next, all the four variants are implemented on 30 rotated and shifted benchmark problems of CEC 2014. The extensive numerical, graphical and statistical analysis of the results show that the third version incorporating the Laplace Crossover and Power mutation is a definite improvement over the other variants.

Amarjeet Singh, Kusum Deep, "Real Coded Genetic Algorithm Operators Embedded in Gravitational Search Algorithm for Continuous Optimization", International Journal of Intelligent Systems and Applications(IJISA), vol.7, no.12, pp.1-22, 2015. DOI:10.5815/ijisa.2015.12.01

[1]M. Dorigo, G.D. Caro, “Ant colony optimization: a new meta-heuristic,” in proceeding of the 1999 Congress on Evolutionary Computation, Washington, DC, USA, 1999, pp. 1470-1478.

[2]D. Karaboga, B. Basturk, “Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems,” Foundations of Fuzzy Logic and Soft Computing, Springer Berlin Heidelberg, 2007, pp. 789-798.

[3]M. Basu, “Artificial immune system for dynamic economic dispatch,” International Journal of Electrical Power & Energy Systems, 2011, 33(1) pp. 131-136.

[4]S. Das, A. Biswas, S. Dasgupta, A. Abraham, “Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications,” In Foundations of Computational Intelligence, Springer Berlin Heidelberg, vol. 3, 2009, pp. 23-55.

[5]R. Storn, K. Price, “Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces,” Journal of global optimization, 11(4), 1997, pp. 341-359.

[6]T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford Univ. Press, New York, USA 1996.

[7]K. N. Krishnanand, D. Ghose, “Glowworm swarm optimisation: a new method for optimising multi-modal functions,” International Journal of Computational Intelligence Studies, 1(1), 2009, pp. 93-119.

[8]A. Singh, K. Deep, “How Improvements in Glowworm Swarm Optimization Can Solve Real-Life Problems,” In Proceedings of Fourth International Conference on Soft Computing for Problem Solving, Springer India, 2015, pp. 275-287.

[9]J. Kennedy, Particle swarm optimization, In Encyclopedia of Machine Learning, Springer US, 2010, pp. 760-766.

[10]S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, Optimization by Simulated annealing, Science 220 (4598), 1983, pp. 671-680.

[11]Y. Wang, J. Zeng, Z. Cui, X. He, “A novel constraint multi-objective artificial physics optimization algorithm and its convergence,” Int. J. Innovat. Comput. Appl. 3(2), 2011, pp. 61-70.

[12]L. Xie, Y. Tan, J. Zeng, Z. Cui, “The convergence analysis of artificial physics optimization algorithm,” Int. J. Intell. Inform. Database Syst. 5 (6), 2011, pp. 536-555.

[13]R. A. Formato, “Central force optimization: a new nature inspired computational framework for multidimensional search and optimization,” Nature Inspired Cooperative Strategies for Optimization (NICSO). Stud. Computa. Intell., 129, 2008, pp. 221-238.

[14]Z. W. Geem, J. H. Kim, G. V. Loganathan, “A new heuristic optimization algorithm: harmony search,” Simulation 76 (2), 2001, pp. 60-68.

[15]Y. T. Hsiao, C. L. Chuang, J. A. Jiang, C. C. Chien, “A novel optimization algorithm: space gravitational optimization,” In Systems, Man and Cybernetics, 2005 IEEE International Conference on 3, 2005, pp. 2323-2328.

[16]A. Biswas, K. K. Mishra, S. Tiwari, A. K. Misra, “Physics-inspired optimization algorithms: A survey,” Journal of Optimization, 2013 < http://www.hindawi.com/journals/jopti/2013/438152/ >.

[17]E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, “GSA: a gravitational search algorithm,” Information sciences, 179(13), 2009, pp. 2232-2248.

[18]S. Sarafrazi, H. Nezamabadi-pour, S. Saryazdi, “Disruption: A new operator in gravitational search algorithm,” Scientia Iranica, 18 (3), 2011, pp. 539-548.

[19]M. Doraghinejad, H. Nezamabadi-pour, “Black Hole: A New Operator for Gravitational Search Algorithm,” International Journal of Computational Intelligence Systems, 7(5), 2014, pp. 809-826.

[20]M. S. Moghadam, H. Nezamabadi-Pour, M. M. Farsangi, “A Quantum Behaved Gravitational Search Algorithm,” Intelligent Information Management 4, 2012, pp. 390-395.

[21]M. S. Moghadam, H. Nezamabadi-pour, “An improved quantum behaved gravitational search algorithm,” 20th Iranian Conference on Electrical Engineering (ICEE2012), 2012, pp. 711-715.

[22]N. M. Sabri, M. Puteh, M. R. Mahmood, A review of gravitational search algorithm. Int. J. Advance. Soft Comput. Appl. 5, (3), 2013, pp. 1-39.

[23]R. E. Precup, R. C. David, E. M. Petriu, S. Preitl, M. B. R?dac, “Gravitational search algorithms in fuzzy control systems tuning,” In Preprints of the 18th IFAC World Congress, 2011, pp. 13624-13629.

[24]G. Sahoo, “A Review on Gravitational Search Algorithm and its Applications to Data Clustering & Classification,” I.J. Intelligent Systems and Applications, 06, 2014, pp. 79-93.

[25]S. Gao, C. Vairappan, Y. Wang, Q. Cao, Z. Tang, “Gravitational search algorithm combined with chaos for unconstrained numerical optimization,” Applied Mathematics and Computation, 231, 2014, pp. 48-62.

[26]E. Rashedi, H. Nezamabadi-Pour, S. Saryazdi, “BGSA: binary gravitational search algorithm,” Natural Computing, 9 (3), 2010, pp. 727-745.

[27]S. Mirjalili, S. Z. M. Hashim, “A new hybrid PSOGSA algorithm for function optimization,” International conference on Computer and information application (ICCIA2010), 2010, pp. 374-377.

[28]T. O. N. G. Chengyi, “Gravitational Search Algorithm Based on Simulated Annealing,” Journal of Convergence Information Technology (JCIT) 9 (2) 2014, pp. 231-237.

[29]B. C. Xu, Y. Y. Zhang, “An improved gravitational search algorithm for dynamic neural network identification,” International Journal of Automation and Computing, 11(4), 2014, pp. 434-440.

[30]B. Gu, F. Pan, “Modified Gravitational Search Algorithm with Particle Memory Ability and its Application,” International Journal of Innovative Computing, Information and Control 9 (11), 2013, pp. 4531-4544.

[31]S. Jiang, Y. Wang, Z. Ji, “Convergence analysis and performance of an improved gravitational search algorithm,” Applied Soft Computing 24, 2014, pp. 363-384.

[32]A. Yadav, K. Deep, “Constrained Optimization Using Gravitational Search Algorithm,” National Academy Science Letters 36 (5), 2013, pp. 527-534.

[33]A. Yadav, K. Deep, “A Novel Co-swarm Gravitational Search Algorithm for Constrained Optimization,” Proceedings of the Third International Conference on Soft Computing for Problem Solving, Springer India, 2014, pp. 629-640.

[34]H. Nobahari, M. Nikusokhan, P. Siarry, “Non-dominated sorting gravitational search algorithm,” In Proc. of the 2011 International Conference on Swarm Intelligence, 2011, pp. 1-10.

[35]H. R. Hassanzadeh, M. Rouhani, “A multi-objective gravitational search algorithm,” Second International Conference on Computational Intelligence, Communication Systems and Networks (CICSyN), 2010, pp. 7-12.

[36]K. Deep, M. Thakur, “A new crossover operator for real coded genetic algorithms,” Applied Mathematics and Computation, 188(1), 2007, pp. 895-911.

[37]K. Deep, M. Thakur, “A new mutation operator for real coded genetic algorithms,” Applied mathematics and Computation, 193(1), 2007, pp. 211-230.

[38]J.J. Liang, B.Y.Qu, P.N Suganthan, “Problem Definitions and Evaluation Criteria for the CEC 2014. Special Session and Competition on Single Objective Real-Parameter Numerical Optimization,” Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, December 2013