Improved Simulated Annealing With Cobweb Spreading In Continuous Space
[Full Text]
AUTHOR(S)
Mahdi Imani, Seyede Fatemeh Ghoreishi, Masoud ShariatPanahi
KEYWORDS
Keywords : Simulated Annealing, continuous space, Cobweb Simulated Annealing (CSA), swarm search, adaptive step length and temperature
ABSTRACT
ABSTRACT: An innovative approach based on Simulated Annealing method to optimize the problems in continuous space is introduced in this paper. The Simulated Annealing is a popular method which finds the optimum value of functions based on temperature changes during its search. Specifying the trend of temperature change and step length is the most critical issue in the original SA method. The SA algorithm with large or small initial step length cannot reach the optimum value of the function efficiently. The initial temperature and its trend of decrease affect the results of the search in the space. In addition, swarm algorithms like PSO and GA are the powerful methods in optimization. In this paper, a new method called Cobweb Simulated Annealing (CSA) with swarm search in the continuous space is presented. The number of population, temperature and step length are adaptive during the search in this algorithm. The searching points spread to explore the entire search space especially in the first stages. This method is applied to several benchmark functions and the results have shown its reliability and efficiency to find the optimum value of functions in comparison with some powerful modifications of SA. CSA is able to search more extensive area of the whole space with less computational cost. The other significant capability of CSA algorithm is finding more local minimums than other modified algorithms.
REFERENCES
[1] Kirkpatrick, S., Vecchi, M.: Optimization by simmulated annealing. science 220(4598), 671680 (1983).
[2] Ishibuchi, H., Misaki, S., Tanaka, H.: Modified simulated annealing algorithms for the flow shop sequencing problem. European Journal of Operational Research 81(2), 388398 (1995).
[3] Misevicius, A.: A modified simulated annealing algorithm for the quadratic assignment problem. Informatica 14(4), 497514 (2003).
[4] RodriguezTello, E., Hao, J.K., TorresJimenez, J.: An improved simulated annealing algorithm for bandwidth minimization. European Journal of Operational Research 185(3), 13191335 (2008).
[5] Locatelli, M.: Convergence of a simulated annealing algorithm for continuous global optimization. Journal of Global Optimization 18(3), 219233 (2000).
[6] Carnevali, P., Coletti, L., Patarnello, S.: Image processing by simulated annealing. IBM Journal of Research and Development 29(6), 569579 (1985).
[7] SalcedoSanz, S., SantiagoMozos, R., BousoņoCalzón, C.: A hybrid Hopfield networksimulated annealing approach for frequency assignment in satellite communications systems. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on 34(2), 11081116 (2004).
[8] Xinchao, Z.: Simulated annealing algorithm with adaptive neighborhood. Applied Soft Computing 11(2), 18271836 (2011).
[9] Zhao, X., Gao, X.S., Hu, Z.C.: Evolutionary programming based on nonuniform mutation. Applied Mathematics and Computation 192(1), 111 (2007).
[10] Paik, C., Soni, S.: A simulated annealing based solution approach for the twolayered location registration and paging areas partitioning problem in cellular mobile networks. European journal of operational research 178(2), 579594 (2007).
[11] Černý, V.: Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm. Journal of optimization theory and applications 45(1), 4151 (1985).
[12] Lin, S., Kernighan, B.W.: An effective heuristic algorithm for the travelingsalesman problem. Operations research 21(2), 498516 (1973).
[13] Low, C.: Simulated annealing heuristic for flow shop scheduling problems with unrelated parallel machines. Computers & Operations Research 32(8), 20132025 (2005).
[14] Tajeddini, M. A., Kebriaei. H, Imani, M.: Bidding Strategy in pay as bid markets by MultiAgent Reinforcement Learning. The 28th International Power System Conference (PSC2013)
