[1] Deb K. An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 2000, 186(2-4): 311-338
[2] Farmani R, Wright J A. Self-adaptive fitness formulation for constrained optimization. IEEE Transactions on Evolutionary Computation, 2003, 7(5): 445-455
[3] Nema S, Goulermas J Y, Sparrow G, Helman P. A hybrid cooperative search algorithm for constrained optimization. Structural and Multidisciplinary Optimization, 2011, 43(1): 107-119
[4] He S, Prempain E, Wu Q H. An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 2004, 36(5): 585-605
[5] Yang B, Chen Y P, Zhao Z L, Han Q Y. A master-slave particle swarm optimization algorithm for solving constrained optimization problems. In: Proceedings of the 6th World Congress on Intelligent Control and Automation. Dalian, China: IEEE, 2006. 3208-3212
[6] Huang Xiao-Ling, Chai Tian-You. Particle swarm optimization for raw material purchasing plan in large scale ore dressing plant. Acta Automatica Sinica, 2009, 35(5): 632-636 (in Chinese)
[7] Wang Chun-Sheng, Wu Min, Cao Wei-Hua, He Yong. Intelligent integrated modeling and synthetic optimization for blending process in lead-zinc sintering. Acta Automatica Sinica, 2009, 35(5): 605-612 (in Chinese)
[8] Liu Gang, Lao Song-Yang, Yuan Can, Hou Lv-Lin, Tan Dong-Feng. OACRR-PSO algorithm for anti-ship missile path planning. Acta Automatica Sinica, 2012, 38(9): 1528-1537 (in Chinese)
[9] Wu Q, Law R, Wu E, Lin J X. A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization. Information Sciences, 2013, 238: 96-110
[10] Wu Q, Law R. Complex system fault diagnosis based on a fuzzy robust wavelet support vector classifier and an adaptive gaussian particle swarm optimization. Information Sciences, 2010, 180(23): 4514-4528
[11] Vural R A, Yildirim T, Kadioglu T, Basargan A. Performance evaluation of evolutionary algorithms for optimal filter design. IEEE Transactions on Evolutionary Computation, 2012, 16(1): 135-147
[12] Li X D, Yao X. Cooperatively coevolving particle swarms for large scale optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(2): 210-224
[13] Blackwell T. A study of collapse in bare bones particle swarm optimization. IEEE Transactions on Evolutionary Computation, 2012, 16(3): 354-372
[14] Pehlivanoglu Y V. A new particle swarm optimization method enhanced with a periodic mutation strategy and neural networks. IEEE Transactions on Evolutionary Computation, 2013, 17(3): 436-452
[15] Chen W N, Zhang J, Lin Y, Chen N, Zhan Z H, Chung H, Li Y, Shi Y H. Particle swarm optimization with an aging leader and challengers. IEEE Transactions on Evolutionary Computation, 2013, 17(2): 241-258
[16] Naznin F, Sarker R, Essam D. Progressive alignment method using genetic algorithm for multiple sequence alignment. IEEE Transactions on Evolutionary Computation, 2012, 16(5): 615-631
[17] Pan Feng, Zhou Qian, Li Wei-Xing, Gao Qi. Analysis of standard particle swarm optimization algorithm based on Markov chain. Acta Automatica Sinica, 2013, 39(4): 381-389 (in Chinese)
[18] Liu Jian-Hua, Liu Guo-Mai, Yang Rong-Hua, Hu Wen-Yu. Analysis of interactivity and randomness in particle swarm optimization. Acta Automatica Sinica, 2012, 38(9): 1471-1484 (in Chinese)
[19] Wang Y, Cai Z X. Combining multiobjective optimization with differential evolution to solve constrained optimization problems. IEEE Transactions on Evolutionary Computation, 2012, 16(1): 117-134
[20] Wang Y, Cai Z X, Zhou Y R, Zeng W. An adaptive tradeoff model for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2008, 12(1): 80-92
[21] Cai Z X, Wang Y. A multiobjective optimization-based evolutionary algorithm for constrained optimization. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 658-675
[22] Krohling R A, dos Santos-Coelho L. Coevolutionary particle swarm optimization using Gaussian distribution for solving constrained optimization problems. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2006, 36(6): 1407-1416
[23] Tessema B, Yen G G. An adaptive penalty formulation for constrained evolutionary optimization. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2009, 39(3): 565-578
[24] Daneshyari M, Yen G G. Constrained multiple-swarm particle swarm optimization within a cultural framework. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 2012, 42(2): 475-490
[25] Venkatraman S, Yen G G. A generic framework for constrained optimization using genetic algorithms. IEEE Transactions on Evolutionary Computation, 2005, 9(4): 424-435
[26] Wang Y, Jiao Y C, Li H. An evolutionary algorithm for solving nonlinear bilevel programming based on a new constraint-handling scheme. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2005, 35(2): 221-232
[27] Grefenstette J J. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 1986, 16(1): 122-128
[28] Whitley D, Starkweather T, Bogart C. Genetic algorithms and neural networks: optimizing connections and connectivity. Parallel Computing, 1990, 14(3): 347-361
[29] Weile D S, Michielssen E. Genetic algorithm optimization applied to electromagnetics: a review. IEEE Transactions on Antennas and Propagation, 1997, 45(3): 343-353
[30] Kennedy J, Eberhart R. Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks. New York, USA: IEEE, 1995. 1942-1948
[31] Eberhart R, Kennedy J. A new optimizer using particle swarm theory. In: Proceedings of the 6th International Symposium on Micro Machine and Human Science. Nagoya, USA: IEEE, 1995. 39-43
[32] Settles M, Soule T. Breeding swarms: a GA/PSO hybrid. In: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation. Washington, DC: ACM, 2005. 161-168
[33] Robinson J, Sinton S, Rahmat-Samii Y. Particle swarm, genetic algorithm, and their hybrids: optimization of a profiled corrugated horn antenna. In: Proceedings of Antennas and Propagation Society International Symposium, 2002. New York, USA: IEEE, 2002. 314-317
[34] Rahmat-Samii Y. Genetic algorithm (GA) and particle swarm optimization (PSO) in engineering electromagnetics. In: Proceedings of the 17th International Conference on Applied Electromagnetics and Communications, 2003. Dubrovnik, Croatia: IEEE, 2003. 1-5
[35] Gao Wei-Shang, Shao Cheng, Gao Qin. Pseudo-collision in swarm optimization algorithm and solution: rain forest algorithm. Acta Physica Sinica, 2013, 62(19): 20-35 (in Chinese)
[36] Himmelblau D M, Clark B J, Eichberg M. Applied Nonlinear Programming. New York: McGraw-Hill, 1972.
[37] Runarsson T P, Yao X. Stochastic ranking for constrained evolutionary optimization. IEEE Transactions on Evolutionary Computation, 2000, 4(3): 284-294
[38] Gen M, Cheng R. Genetic Algorithms and Engineering design. New York: John Wily and Sons, 1997.
[39] Belegundu A D. A Study of Mathematical Programming Methods for Structural Optimization [Ph.D. dissertation], University of Iowa, Iowa, 1982.
[40] Arora J S. Introduction to optimum design. New York: McGraw-Hill, 1989.
[41] Coello C A C, Montes E M. Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Advanced Engineering Informatics, 2002, 16(3): 193-203
[42] Sandgren E. Nonlinear integer and discrete programming in mechanical design optimization. Journal of Mechanical Design, 1990, 112(2): 223-229
[43] Deb K. Geneas: a robust optimal design technique for mechanical component design. Evolutionary Algorithms in Engineering Applications. New York: Springer 1997. 497-514
[44] Coello C A C, Montes E M. Use of dominance-based tournament selection to handle constraints in genetic algorithms. Intelligent Engineering Systems through Artificial Neural Networks (ANNIE2001), 2001, 11: 177-182
[45] Ragsdell K M, Phillips D T. Optimal design of a class of welded structures using geometric programming. Journal of Manufacturing Science and Engineering, 1976, 98(3): 1021-1025
[46] Ray T, Liew K M. Society and civilization: an optimization algorithm based on the simulation of social behavior. IEEE Transactions on Evolutionary Computation, 2003, 7(4): 386-396