Unified Particle Swarm Optimization for Solving Constrained Engineering Optimization Problems
暂无分享,去创建一个
[1] David Mautner Himmelblau,et al. Applied Nonlinear Programming , 1972 .
[2] Michael N. Vrahatis,et al. Particle Swarm Optimization Method for Constrained Optimization Problems , 2002 .
[3] Maurice Clerc,et al. The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..
[4] Christopher R. Houck,et al. On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.
[5] Carlos A. Coello Coello,et al. Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .
[6] Panos M. Pardalos,et al. A Collection of Test Problems for Constrained Global Optimization Algorithms , 1990, Lecture Notes in Computer Science.
[7] Konstantinos E. Parsopoulos,et al. UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).
[8] Carlos A. Coello Coello,et al. Self-adaptive penalties for GA-based optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[9] Jasbir S. Arora,et al. Introduction to Optimum Design , 1988 .
[10] E. Sandgren,et al. Nonlinear Integer and Discrete Programming in Mechanical Design Optimization , 1990 .
[11] Michael N. Vrahatis,et al. Unified Particle Swarm Optimization in Dynamic Environments , 2005, EvoWorkshops.
[12] Singiresu S. Rao. Engineering Optimization : Theory and Practice , 2010 .
[13] Xiaohui Hu,et al. Engineering optimization with particle swarm , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).
[14] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.