Abstract
Constrained optimization is a well-known research topic in the evolutionary computation field. In these problems, the selected solution must be feasible. In evolutionary constrained optimization, the search space is usually much bigger than the feasible space of the problem. There is a general view that the presence or absence of any feasible individuals in the initial population substantially influences the performance of the algorithm. Therefore, the aim of this research is to analyze the effect of the number of feasible individuals, in the initial population, on the algorithm’s performance. For experimentation, we solve a good number of well-known bench-mark problems using a Differential Evolution algorithm. The results show that the algorithm performs slightly better, for the test problems solved, when the initial population contains about 5% feasible individuals.
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References
Elsayed, S.M., Sarker, R.A., Essam, D.L.: Multi-operator based evolutionary algorithms for solving constrained optimization Problems. Computers and Operations Research 38, 1877–1896 (2011)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential evolution: a practical approach to global optimization. Springer, Berlin (2005)
Elsayed, S.M., Sarker, R.A., Essam, D.L.: GA with a new multi-parent crossover for constrained optimization. In: IEEE Congress on Evolutionary Computation, pp. 857–864 (2011)
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley, MA (1989)
Powell, M.: A fast algorithm for nonlinearly constrained optimization calculations. In: Watson, G. (ed.) Numerical Analysis, pp. 144–157. Springer, Heidelberg (1978)
Singh, H.K., Ray, T., Smith, W.: Performance of infeasibility empowered memetic algorithm for CEC 2010 constrained optimization problems. In: IEEE Congress on Evolutionary Computation, pp. 1–8 (2010)
Mezura-Montes, E., Velázquez-Reyes, J., Coello, C.A.C.: Promising infeasibility and multiple offspring incorporated to differential evolution for constrained optimization. In: The 2005 Conference on Genetic and Evolutionary Computation, pp. 225–232. ACM, Washington DC (2005)
Mezura-Montes, E., Coello, C.A.C.: A simple multimembered evolution strategy to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation 9, 1–17 (2005)
Vieira, D.A.G., Adriano, R.L.S., Vasconcelos, J.A., Krahenbuhl, L.: Treating constraints as objectives in multiobjective optimization problems using niched Pareto genetic algorithm. IEEE Transactions on Magnetics 40, 1188–1191 (2004)
Ray, T., Singh, H.K., Isaacs, A., Smith, W.: Infeasibility Driven Evolutionary Algorithm for Constrained Optimization. In: Mezura-Montes, E. (ed.) Constraint-Handling in Evolutionary Optimization. SCI, vol. 198, pp. 145–165. Springer, Heidelberg (2009)
Barkat-Ullah, A.S.S.M., Sarker, R., Cornforth, D.: Search space reduction technique for constrained optimization with tiny feasible space. In: The 10th Annual Conference on Genetic and Evolutionary Computation, Atlanta, GA, USA, pp. 881–888 (2008)
Barkat-Ullah, A.S.S.M., Elfeky, E.Z., Cornforth, D., Essam, D.L., Sarker, R.: Improved evolutionary algorithms for solving constrained optimization problems with tiny feasible space. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1426–1433 (2008)
Mallipeddi, R., Suganthan, P.N.: Problem definitions and evaluation criteria for the CEC 2010 competition and special session on single objective constrained real-parameter optimization. Technical Report, Nangyang Technological University, Singapore (2010)
Lampinen, J., Zelinka, I.: On stagnation of the differential evolution algorithm. In: 6th Int. Mendel Conference on Soft Computing, Brno, Czech Republic, pp. 76–83 (2000)
Vesterstrom, J., Thomsen, R.: A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: IEEE Congress on Evolutionary Computation, pp. 980–1987 (2004)
Elsayed, S.M., Sarker, R.A., Ray, T.: Parameters Adaptation in Differential Evolution. In: IEEE Congress on Evolutionary Computation (accepted, 2012)
Liang, J.J., Runarsson, T.P., Mezura-Montes, E., Clerc, M., Suganthan, P.N., Coello, C.A.C., Deb, K.: Problem Definitions and Evaluation Criteria for the CEC 2006 Special Session on Constrained Real-Parameter Optimization. Technical Report, Nanyang Technological University, Singapore (2005)
Deb, K.: An Efficient Constraint Handling Method for Genetic Algorithms. Computer Methods in Applied Mechanics and Engineering 186, 311–338 (2000)
Corder, G.W., Foreman, D.I.: Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. John Wiley, Hoboken (2009)
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Elsayed, S.M., Sarker, R.A., Essam, D.L. (2012). The Influence of the Number of Initial Feasible Solutions on the Performance of an Evolutionary Optimization Algorithm. In: Bui, L.T., Ong, Y.S., Hoai, N.X., Ishibuchi, H., Suganthan, P.N. (eds) Simulated Evolution and Learning. SEAL 2012. Lecture Notes in Computer Science, vol 7673. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34859-4_1
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DOI: https://doi.org/10.1007/978-3-642-34859-4_1
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