Abstract
A particle swarm optimization for solving constrained multi-objective optimization problem was proposed (CMPSO). In this paper, the main idea is the use of penalty function to handle the constraints. CMPSO employs particle swarm optimization algorithm and Pareto neighborhood crossover operation to generate new population. Numerical experiments are compared with NSGA-II and MOPSO on three benchmark problems. The numerical results show the effectiveness of the proposed CMPSO algorithm.
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References
Gen, M., Cheng, R.W.: Genetic Algorithms & Engineering Design. John Wiley & Sons, Inc., New York (1997)
Glover, F.: Heuristics for Integer Programming Using Surrogate Constraints. Decision Sciences 8(1), 156–166 (1977)
Goldberg, D.E.: Genetic in Search, Optimization and Machine Learning. Addison Wesley, Reading (1989)
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)
Shi, Y., Eberhart, R.: A Modified Particle Swarm Optimizer. In: Proceeging of the IEEE World Congress on Computational Intelligence, pp. 69–73 (1998)
Harada, K., Sakuma, J., Ono, I., Kobayashi, S.: Constraint-handling. Method for Multi-Objective Function Optimization: Pareto Descent Repair Operator. In: Int. Conf. Evol. Multi-Criterion Opt., Matshushima, Japan, pp. 156–170 (2007)
Yang, C.H., Mo, Z.X., Li, Y.G.: Constrained Multi-Objective Optimization Based on Improved Particle. Swarm Optimization Algorithm 36(20), 203–205 (2010)
Pei, S.Y.: Using Hybrid Particle Swarm Algorithm for Solving Constrained Multi-Objective Optimization Problem. Computer Engineering and Applications 47(15), 49–52 (2011)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A Fast And Elitist Multi-Objective Genetic Algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6(2), 182–197 (2002)
Jiao, L.Q., Liu, J., Zhong, W.C.: Co-evolutionary Algorithms and Multi-Agent System, pp. 22–25. Science Press, Beijing (2007)
Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming, Theory and Algorithm. Academic Press, New York (1979)
Liu, D., Tan, K.C., Coh, C.K., et al.: A Multi-Objective Memetic Algorithm Based On Particle Swarm Optimization. IEEE Transactions on Systems, Man and Cybernetics, Part B 37(1), 42–50 (2007)
Wang, X.S., Hao, M.L., Cheng, Y.H., et al.: A Multi-Objective Optimization Problems with a Hybrid Algorithms. Journal of System Simulation 21(16), 4980–4985 (2009)
Van, V., David, A., Lamont, G.B.: Evolutionary Computation and Convergence to a Pareto Front. In: Koza, J.R. (ed.) Late Breaking Papers at the Genetic Programming 1998 Conference, pp. 221–228. Stanford Bookstore, Stanford University, California (1998)
Zhou, A., Jin, Y., Zhang, Q., et al.: Combing Model-Based And Generics-Based Offspring Generation For Multi-Objective Optimization Using A Convergence Criterion. In: 2006 Congress on Evolutionary Computation, pp. 3234–3241 (2006)
Coello, C.A., Pulido, G.T., Lechuga, M.S.: Handling Multiple Objectives with Particle Swarm Optimization. IEEE Trans. on Evolutionary Computations 8(3), 256–279 (2004)
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Gao, Yl., Qu, M. (2012). Constrained Multi-objective Particle Swarm Optimization Algorithm. In: Huang, DS., Gupta, P., Zhang, X., Premaratne, P. (eds) Emerging Intelligent Computing Technology and Applications. ICIC 2012. Communications in Computer and Information Science, vol 304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31837-5_7
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DOI: https://doi.org/10.1007/978-3-642-31837-5_7
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