SWAF: Swarm Algorithm Framework for numerical optimization

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3102, Issue , 2004, Pages 238-250

  Xie, Xiao Feng ^a   Zhang, Wen Jun ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; AUTONOMOUS AGENTS; BENCHMARKING; COMPUTATIONAL METHODS; PROBLEM SOLVING;

AGENT-BASED MODEL; BENCH-MARK PROBLEMS; COGNITIVE ARCHITECTURES; NUMERICAL OPTIMIZATIONS; PERFORMANCE COMPARISON; PROBLEM FORMULATION; SUB-SYMBOLIC; SWARM ALGORITHMS;

OPTIMIZATION;

EID: 35048900573     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24854-5_21     Document Type: Article

References (37)

1. Anderson, J.R.: A theory of the origins of human knowledge. Artificial Intelligence, 40 (1989)313-351
2. Anderson, J.R.: ACT: A simple theory of complex cognition. American Psychologist, 51 (1996) 355-365
3. Bäck, T., Hammel, U., Schwefel, H.-P.: Evolutionary computation: comments on the history and current state. IEEE Trans. Evolutionary Computation, 1 (1997) 3-17
4. Bak, P., Chialvo, D.R.: Adaptive learning by extremal dynamics and negative feedback. Physical Review E, 63, 031912 (2001) 1-12
5. Bandura, A.: Social Learning Theory. Prentice Hall, Englewood Cliffs, NJ (1977)
6. Beyer, H.-G., Schwefel, H.-P.: Evolution strategies: a comprehensive introduction. Natural Computing, 1 (2002) 35-52
7. Bonabeau, E.: Agent-based modeling: methods and techniques for simulating human systems. Proc. Natl. Acad. Sci. USA, 99 (2002) 7280-7287
8. Boyd, R., Richerson, P.J.: The Origin and Evolution of Cultures. Oxford Univ. Press (2004)
9. Chase, V.M., Hertwig, R., Gigerenzer, G.: Visions of rationality. Trends in Cognitive Sciences, 2 (1998) 206-214
10. Chialvo, D.R., Bak, P.: Learning from mistakes. Neuroscience, 90 (1999) 1137-1148
11. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evolutionary Computation, 6 (2002) 58-73
12. Cvijovi , D., Klinowski, J.: Taboo search: an approach to the multiple minima problem. Science, 267 (1995) 664-666
13. Deb, K.: An efficient constraint handling method for genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 186 (2000) 311-338
14. Dekkers, A., Aarts, E.: Global optimization and simulated annealing. Mathematical Programming, 50 (1991) 367-393
15. Dietterich, T.G.: Learning at the knowledge level. Machine Learning, 1 (1986) 287-316
16. Dorigo, M., Maniezzo, V., Colorni, A.: The ant system: optimization by a colony of cooperating agents. IEEE Trans. Systems, Man, and Cybernetics - Part B, 26 (1996) 1-13
17. Farmani, R., Wright, J.A.: Self-adaptive fitness formulation for constrained optimization. IEEE Trans. Evolutionary Computation, 7 (2003) 445-455
18. Flinn, M.V.: Culture and the evolution of social learning. Evolution and Human Behavior, 18(1997)23-67
19. Galef, B.G.: Why behaviour patterns that animals leam socially are locally adaptive. Animal Behaviour, 49 (1995) 1325-1334
20. Heyes, C.M.: Social learning in animals: categories and mechanisms. Biological Reviews, 69(1994)207-231
21. Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor, MI (1975)
22. Kennedy, J., Eberhart, R.C.: Particle swarm optimization, IEEE Int. Conf. on Neural Networks, Perth, Australia (1995) 1942-1948
23. Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science, 220(1983)671-680
24. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)
25. Laird, J.E., Newell, A., Rosenbloom, P.S.: SOAR - an architecture for general intelligence. Artificial Intelligence, 33 (1987) 1-64
26. Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evolutionary Computation, 4 (1996) 1-32
27. Newell, A.: The knowledge level. Artificial Intelligence, 18 (1982) 87-127
28. Okano, H., Hirano, T., Balaban, E.: Learning and memory. Proc. Natl. Acad. Sci. USA, 97 (2000) 12403-12404
29. Runarsson, T.P., Yao, X.: Stochastic ranking for constrained evolutionary optimization. IEEE Trans. Evolutionary Computation, 4 (2000) 284-294
30. Salomon, R.: Re-evaluating genetic algorithm performance under coordinate rotation of benchmark functions. Biosystems, 39 (1996) 263-278
31. Simon, H.A.: Invariants of human behavior. Annual Review of Psychology, 41 (1990) 1-19
32. Storn, R., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization, 11 (1997) 341-359
33. Thorndike, E.L.: Animal intelligence: An experimental study of the associative processes in animals. Psychological Review, Monograph Supplement, 2 (1898)
34. Todd, P.M., Gigerenzer, G.: Simple heuristics that make us smart. Behavioral and Brain Sciences, 23 (1999) 727-741
35. Tsui, K.C., Liu, J.M.: Multiagent diffusion and distributed optimization, Int. Joint Conf. on Autonomous Agents and Multiagent Systems, Melbourne, Australia (2003) 169-176
36. Wolpert, D.H., Macready, W.G.: No free lunch theorems for optimization. IEEE Trans. Evolutionary Computation, 1 (1997) 67-82
37. Zhang, W.J., Xie, X.F.: DEPSO: hybrid particle swarm with differential evolution operator, IEEE Int. Conf. on Systems, Man & Cybernetics, Washington D C, USA (2003) 3816-3821