Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization
Hrvoje Markovic, Fangyan Dong, and Kaoru Hirota
Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
-  R. Chelouah and P. Siarry, “A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions,” J. of Heuristics, Vol.6, pp. 191-213, 2000.
-  S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi, “Optimization by Simulated Annealing,” Science, New Series, Vol.220, No.4598, pp. 671-680, 1983.
-  J. Kennedy and R. C. Eberhart, “Swarm Intelligence,” Morgan Kaufmann, San Francisco, 2001.
-  K. V. Price, R. M. Storn, and J. A. Lampinen, “Differential Evolution: A practical approach to global optimization,” Springer, 2005.
-  H. Y. K. Lau and W. W. P. Tsang, “A Parallel Immune Optimization Algorithm for Numeric Function Optimization,” Evolutionary Intelligence, Vol.1, pp. 171-185, 2008.
-  C. Zang, J. Ning, S. Lu, D. Ouyang, and T. Ding, “A Novel Hybrid Differential Evolution and Particle Swarm Optimization Algorithm for Unconstrained Optimization,” Operations Research Letters, Vol.37, No.2, pp. 117-122, 2009.
-  A. H. Wright and J. N. Richter, “Strong Recombination, Weak Selection, and Mutation,” in Proc. 2006 Genetic and Evolutionary Computation Conf., 2006, pp. 1369-1376.
-  T. T. Nguyen and X. Yao, “An Experimental Study of Hybridizing Cultural Algorithms and Local Search,” Int. J. of Neural Systems, Vol.18, No.1, pp. 1-18, 2008.
-  J. J. Liang and P. N. Suganthan, “Dynamic Multi-Swarm Particle Swarm Optimizer with Local Search,” in Proc. 2005 IEEE Congress on Evolutionary Computation, 2005, pp. 522-528.
-  A. A. Guinta and L.T. Watson, “A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models,” in Proc. 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization, 1998, pp. 1-13.
-  Z. Zhou, Y. S. Ong, P. B. Nair, A. J. Keane, and K. Y. Lum, “Combining Global and Local Surrogate Models to Accelerate Evolutionary Optimization,” IEEE Trans. on Systems, Man, and Cybernetics Part C, Vol.37, No.1, pp. 66-76, 2007.
-  A. Ratle, “Kriging as a Surrogate Fitness Landscape in Evolutionary Optimization,” Artificial Intelligence for Engineering Design Analysis and Manufacturing, Vol.15, No.1, pp. 37-49, 2001.
-  Y. Jin, M. Olhofer and B. Sendhoff, “A Framework for Evolutionary Optimization with Approximate Fitness Functions,” IEEE Trans. on Evolutionary Computation, Vol.6, No.5, pp. 481-494, 2002.
-  Y. Jin, “A Comprehensive Survey of Fitness Approximation in Evolutionary Computation,” Soft Computing, Vol.9, No.1, pp. 3-12, 2005.
-  Y. S. Ong, Z. Zhou, and D. Lim, “Curse and Blessing of Uncertainty in Evolutionary Algorithm Using Approximation,” in. Proc. 2006 IEEE Congress on Evolutionary Computation, 2006, pp. 2928-2935.
-  D. Eby, R. C. Averill, R. F. Punch, and E. D. Goodman, “Optimal Design of Flywheels Using an Injection Island Genetic Algorithm,” Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol.13, No.5, pp. 327-340, 1999.
-  M. Sefrioui and J. Periaux, “A Hierarchical Genetic Algorithm Using Multiple Models for Optimization,” in Parallel Problem Solving from Nature, Lecture Notes in Computer Science, Vol.1917, pp. 879-888, 2000.
-  K. Socha and M. Dorigo, “Ant Colony Optimization for Continuous Domains,” European J. of Operational Research, Vol.185, No.3, pp. 1155-1173, 2008.
-  I. G. Tsoulosa and I. E. Lagaris, “MinFinder: Locating all the local minima of a function,” Computer Physics Communications, Vol.174, No.2, pp. 166-179, 2006.
This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.