Archive management for dynamic multi-objective optimisation problems using vector evaluated particle swarm optimisation

2011 IEEE Congress of Evolutionary Computation, CEC 2011

Volumn , Issue , 2011, Pages 2047-2054

  Helbig, Marde ^a,b   Engelbrecht, Andries P ^a  

^a UNIVERSITY OF PRETORIA   (South Africa)

^b CSIR MERAKA INSTITUTE   (South Africa)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK FUNCTIONS; MULTI-OBJECTIVE PROBLEM; NONDOMINATED SOLUTIONS; OPTIMISATIONS; PARETO-OPTIMAL FRONT; PARTICLE SWARM OPTIMISATION; VECTOR EVALUATED;

ALGORITHMS; MULTIOBJECTIVE OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO);

PROBLEM SOLVING;

EID: 80052007460     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CEC.2011.5949867     Document Type: Conference Paper

References (21)

1. J. Kennedy and R. C. Eberhart. Particle Swarm Optimization. In Proc. of IEEE International Conference on Neural Networks, Volume IV, pages 1942-1948, 1995.
2. A. Carlisle and G. Dozier. Adapting Particle Swarm Optimization to Dynamic Environments. In Proc. of International Conference on Artificial Intelligence (ICAI 2000), pages 429-434, 2000.
3. Tim Blackwell, Jürgen Branke, and Xiaodong Li. Particle swarms for dynamic optimization problems. In Christian Blum and Daniel Merkle, editors, Swarm Intelligence, Natural Computing Series, pages 193-217. Springer Berlin Heidelberg, 2008. 10.1007/978-3-540-74089-6 6.
4. Kalyanmoy Deb, Udaya Bhaskara Rao N., and S. Karthik. Dynamic multi-objective optimization and decision-making using modied nsgaii: A case study on hydro-thermal power scheduling. In In Proc. of International Conference on Evolutionary Multi-criterion optimization (EMO), pages 803-817, Matsushima, Japan, 2007. (Pubitemid 350270927)
5. M. Cámara, J. Ortega, and J. Toro. Parallel Processing for Multiobjective Optimization in Dynamic Environments. In Proc. of IEEE International Parallel and Distributed Processing Symposium, page 243, 2007.
6. C-K. Goh and K. C. Tan. A competitive-cooperative coevolutionary paradigm for dynamic multiobjective optimization. IEEE Transactions on Evolutionary Computation, 13(1):103-127, February 2009.
7. M. Greeff and A. P. Engelbrecht. Multi-objective Swarm Intelligent Systems, volume 261 of Studies in Computational Intelligence, chapter Dynamic Multi-objective Optimisation using PSO, pages 105-123. Springer Verlag Berlin/Heidelberg, 2010.
8. B. Zheng. A New Dynamic Multi-Objective Optimization Evolutionary Algorithm. In Proc. of third International Conference on Natural Computation (ICNC 2007), Volume V, pages 565-570, 2007.
9. Y. Jin and B. Sendhof. Constructing Dynamic Optimization Test Problems using the Multi-objective Optimization Concept. In Proc. of Applications of Evolutionary Algorithms: LNCS, Volume 3005, pages 525-536, 2004.
10. S-U. Guan, Q. Chen, and W. Mo. Evolving Dynamic Multi-Objective Optimization Problems with Objective Replacement. Artificial Intelligence Review, 23(3):267-293, 2005. (Pubitemid 40512298)
11. M. Farina, K. Deb, and P. Amato. Dynamic multiobjective optimization problems: test cases, approximations, and applications. IEEE Transactions on Evolutionary Computation, 8(5):425-442, October 2004.
12. E. Zitzler, K. Deb, and L. Thiele. Comparison of Multiobjective Evolutionary Algorithms: Emperical Results. Evolutionary Computation, 8(2):173-195, 2000.
13. K. Deb et al. Scalable multi-objective optimization test problems. In Proc. of Congress on Evolutionary Computation (CEC 2002), Volume 1, pages 825-830, 2002.
14. J. Mehnen, T. Wagner, and G. Rudolph. Evolutionary Optimization of Dynamic Muli-Objective Test Functions. In Proc. of 2nd Italian Workshop on Evolutionary Computation and 3rd Italian Workshop on Artificial Life, 2006.
15. X. Li, J. Branke, and M. Kirley. On Performance Metrics and Particle Swarm Methods for Dynamic Multiobjective Optimization Problems. In Proc. of Congress of Evolutionary Computation (CEC 2007), pages 1635-1643, 2007.
16. K. E. Parsopoulos and M. N. Vrahatis. Recent Approaches to Global Optimization Problems through Particle Swarm Optimization. Natural Computing, 1(2-3):235-306, 2002.
17. K. E. Parsopoulos, D. K. Tasoulis, and M. N. Vrahatis. Multiobjective Optimization using Parallel Vector Evaluated Particle Swarm Optimization. In Proc. of IASTED International Conference on Artificial Intelligence and Applications, 2004.
18. G. Pampara, A. P. Engelbrecht, and T. Cloete. Cilib: A collaborative framework for computational intelligence algorithms - part i. In Proc. of IEEE World Congress on Computational Intelligence (WCCI), pages 1750-1757, Hong Kong, 1-8 June 2008. Source code available at: http://www.cilib.net. Last accessed on: 6 March 2011.
19. C. K. Goh and K. C. Tan. An Investigation on Noisy Environments in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 11(3):354-381, June 2007. (Pubitemid 46895413)
20. X. Li, J. Branke, and T. Blackwell. Particle Swarm with Speciation and Adaptation in a Dynamic Environment. In Proc. of 8th Conference on Genetic and Evolutionary Computation (GECCO 2006), pages 51-58, 2006. (Pubitemid 44611294)
21. K. Deb. Kanpur Genetic Algorithms Laboratory. http://www.iitk.ac.in/ kangal/codes.shtml. Accessed online on: 6 March 2011.