Biologically inspired optimization: A review

Transactions of the Institute of Measurement & Control

Volumn 31, Issue 6, 2009, Pages 495-515

  Tang, W J ^a   wu, Q H ^a  

^a UNIVERSITY OF LIVERPOOL   (United Kingdom)

Author keywords

applications; biologically inspired algorithms; optimization; survey

Indexed keywords

APPLICATIONS; BIOINFORMATICS; BIOMIMETICS; OPTIMIZATION; SURVEYING;

BACTERIAL FORAGING ALGORITHM; BIOLOGICALLY INSPIRED; BIOLOGICALLY INSPIRED ALGORITHMS; COMMON FEATURES;

EVOLUTIONARY ALGORITHMS;

EID: 72149088751     PISSN: 01423312     EISSN: None     Source Type: Journal    
DOI: 10.1177/0142331208094044     Document Type: Review

References (70)

1. Bäck, T. 1996: Evolutionary algorithms in theory and practice. Oxford University Press.
2. Barnard, C.J. and Sibly, R.M. 1981: Producers and scroungers: a general model and its application to captive flocks of house sparrows. Animal Behaviour 29, 543-50.
3. Bell, J.W. 1990: Searching behaviour - the behavioural ecology of finding resources. Chapman and Hall Animal Behaviour Series. Chapman and Hall.
4. Berg, H.C. and Brown, D.A. 1972: Chemotaxis in Escherichia coli analyzed by three-dimensional tracking. Nature 239, 500-504.
5. Bergener, T., Bruckhoff, C. and Igel, C. 2001: Parameter optimization for visual obstacle detection using a derandomized evolution strategy. In Blanc-Talon, J. and Popesc, D., editors. Imaging and vision systems: theory, assessment and applications, volume 9 of Advances in Computation: Theory and Practice. Nova Science Books, 265-79chapter 13.
6. Beyer, H.G. and Schwefel, H.P. 2002: Evolution strategies. Natural Computing 1, 3-52.
7. Bonabeau, E., Dorigo, M. and Theraulaz, G. 1999: Swarm intelligence. Oxford University Press.
8. Bremermann, H. 1958: The evolution of intelligence. The nervous system as a model if its environment. Contract No. 477 (17) 1, Department of Mathematics, University of Washington.
9. Cao, Y.J. and Wu, Q.H. 1999: Optimisation of control parameters in genetic algorithms: a stochastic approach. International Journal of Systems Science 30, 551-59. Lawrence Erlbaum Associates.
10. Caraco, T. 1986: Time budgeting and group size: a test of theory. Ecology 60, 618-27.
11. Clerc, M. and Kennedy, J. 2002: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6, 58-73.
12. Costa, D. and Hertz, A. 1997: Ants can colour graphs. Journal of the Operational Research Society 48, 295-305.
13. Dixon, A.F.G. 1959: An experimental study of the searching behaviour of the predatory coccinellid beetle. Adalia decempunctata. Journal of Animal Ecology 28, 259-81.
14. Dorigo, M. and Gambardella, L.M. 1997: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1, 53-66.
15. Dorigo, M., Maniezzo, V. and Colorni, A. 1996: Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 26, 29-41.
16. Dusenbery, D.B. 1988: Ranging strategies. Journal of Theoretical Biology 136, 309-16.
17. Fei, Y.N., Lu, Z., Tang, W.H. and Wu, Q.H. 2007: Harmonic estimation using a global search optimizer. Lecture Notes in Computer Science 4448, 261-70.
18. Fogel, L., Owens, A. and Walsh, M. 1966: Artificial intelligence through simulated evolution. Wiley.
19. Franken, N. and Engelbrechet, A.P. 2005: Particle swarm optimization approaches to coevolve strategies for the iterated prisoner 's dilemma. IEEE Transactions on Evolutionary Computation 9, 562-79.
20. Gambardella, L.M., Taillard, E.D. and Agazzi, G. 1997: MACS-VRPTW: A multiple ant colony system for vehicle routing problems with time windows. New Ideas in Optimization. McGraw Hill, 63-76.
21. Giraldeau, L.-A. and Lefebvre, L. 1986: Exchangeable producer and scrounger roles in a captive flock of feral pigeons - a case for the skill pool effect. Animal Behaviour 34, 797-803.
22. Goldberg, D.E. 1989: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley.
23. Goldberg, D.E. 1991: Real-coded genetic algorithms, virtual alphabets, and blocking. Complex Systems 5, 139-67.
24. Gonzalez, A.I., Gra, M., Ruiz Cabello, J., D’Anjou, A. and Albizuri, F.X. 2001: Experimental results of an evolution-based adaptation strategy for VQ image filtering. Information Sciences - Informatics and Computer Science 133, 249-66.
25. Gregory, R., Paton, R.C., Saunders, J.R. and Wu, Q.H. 2004: Parallelising a model of bacterial interaction and evolution. BioSystems 76, 121-31.
26. Guo, H. and Nandi, A.K. 2006: Breast cancer diagnosis using genetic programming generated feature. Pattern Recognition 39, 980-87.
27. Gupta, A.K. and Greenwood, G.W. 1996: Applications of evolutionary strategies to fine-grained task scheduling. Parallel Processing Letters 6, 551-61.
28. Harper, D.G.C. 1988: Competitive foraging in mallards: ‘ideal free’ ducks. Animal Behaviour 30, 575-84.
29. Harpham, C., Dawson, C.W. and Brown, M.R. 2004: A review of genetic algorithms applied to training radial basis function networks. Neural Computation and Application 13, 193-201.
30. Hatanaka, T., Uosaki, K., Tanaka, H. and Yamada, Y. 1996: System parameter estimation by evolutionary strategy. SICE ‘96. Proceedings of the 35th SICE Annual Conference. 1045-48.
31. He, S., Wu, Q.H. and Saunders, J.R. 2006: A novel group search optimizer inspired by animal behavioural ecology. Proceedings of the IEEE Congress on Evolutionary Computation. Vancouver, 4415-21.
32. He, S., Wu, Q.H., Wen, J.Y., Saunders, J.R. and Paton, R.C. 2004: A particle swarm optimizer with passive congregation. BioSystems 78, 135-47.
33. Higgins, C.L. and Strauss, R.E. 2003: Discrimination and classification of foraging paths produced by search-tactic models. Behavioural Ecology 15, 248-54.
34. Holland, J. 1975: Adaptation in natural and artificial systems. The University of Michigan Press.
35. Horn, J., Nafpliotis, N. and Goldberg, D.E. 1994: A niched pareto genetic algorithm for multiobjective optimization. Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Piscataway, NJ. IEEE Service Center, 1, 82-87.
36. Kennedy, J. and Eberhart, R.C. 1995: Particle swarm optimization. In. Proceedings IEEE International Conference on Neural Networks 4, 1942-48.
37. Koppen, M., Franke, K. and Vicente-Garcia, R. 2006: Tiny gas for image processing applications. IEEE Computational Intelligence Magazine 1, 17-26.
38. Koumousis, V.K. and Katsaras, C.P. 2006: A saw-tooth genetic algorithm combing the effects of variable population size and reinitialization to enhance performance. IEEE Transactions on Evolutionary Computation 10, 19-28.
39. Koza, J.R. 1992: Genetic programming: on the programming of computers by means of natural selection. MIT Press.
40. Koza, J.R., Bennett, III, F.H., Andre, D., Keane, M.A. and Dunlap, F. 1997: Automated synthesis of analog electrical circuits by means of genetic programming. IEEE Transactions on Evolutionary Computation 1, 109-28.
41. Li, M.S., Tang, W.J., Tang, W.H., Wu, Q.H. and Saunders, J.R. 2007: Bacterial foraging algorithm with varying population for optimal power flow. Lecture Notes in Computer Science 4448, 32-41.
42. Liang, J.J., Qin, A.K., Suganthan, P.N. and Baskar, S. 2006: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transactions on Evolutionary Computation 10, 281-95.
43. Meuleau, N. and Dorigo, M. 2002: Ant colony optimization and stochastic gradient descent. Artificial Life 8, 103-21.
44. Michalewicz, Z. 1994: Genetic algorithms + data structures = evolution programs. second extended edition. Springer.
45. Miller, M.B. and Bassler, B.L. 2001: Quorum sensing in bacteria. Annual Review of Microbiology 55, 165-99.
46. Nam, D., Seo, Y.D., Park, L.-J., Park, C.H. and Kim, B. 2001: Parameter optimization of an on-chip voltage reference circuit using evolutionary programming. IEEE Transactions on Evolutionary Computation 5, 414-21.
47. O’Brien, W.J., Evans, B.I. and Howick, G.L. 1986: A new view of the predation cycle of a planktivorous fish, white crappie (Pomoxis annularis). Canadian Journal Fisheries and Aquatic Sciences 43, 1894-99.
48. Passino, K.M. 2002: Biomimicry of bacterial foraging for distributed optimization and control. IEEE Control Systems Magazine 22, 53-67.
49. Paton, R., Gregory, R., Vlachos, C., Palmer, J.W., Suanders, J. and Wu, Q.H. 2004: Evolvable social agents for bacterial systems modelling. Special Issue on Molecular and Cellular Systems Biology. IEEE Transactions on Nanobioscience 3, 208-16.
50. Rechenberg, I. 1973: Evolutionstrategie: Optimieriung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog.
51. Rodriguez-Vazquez, K., Fonseca, C.M. and Fleming, P.J. 2004: Identifying the structure of nonlinear dynamic systems using multiobjective genetic programming. IEEE Transactions on Systems Man and Cybernetics Part A-Systems And Humans 34, 531-45.
52. Rudolph, G. 1998: Finite Markov chain results in evolutionary computation: a tour d'horizon. Fundamenta Informaticae 35, 67-89.
53. Salomon, R. 1998: Evolutionary algorithms and gradient search: similarities and differences. IEEE Transactions on Evolutionary Computation 2, 45-55.
54. Schaffer, J.D. 1985: Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms. Lawrence Erlbaum Associates, Mahwah, NJ, 93-100.
55. Schwefel, H.P. 1981: Numerical optimization of computer models. Wiley.
56. Shintemirov, A., Tang, W.H. and Wu, Q.H. 2007: Power transformer fault classification based on dissolved gas analysis by integrating bootstrap and genetic programming. IEEE Transactions on Systems, Man, and Cybernetics-Part C, submitted.
57. Shymygelska, A. and Hoos, H.H. 2005: An ant colony optimisation algorithm for the 2D and 3D hydrophobic polar protein folding problem. BMC Bioinformatics 6(30): 1-22.
58. Socha, K., Sampels, M. and Manfrin, M. 2003: Ant algorithm for the university course timetabling problem with regard to the state-of-the-art. Applications of Evolutionary Computing, Proceedings of the EvoWorkshops 2003, LNCS 2611, 334-45. Springer.
59. Tahk, M.J., Woo, H.W. and Park, M.S. 2006: A hybrid optimization method of evolutionary and gradient search. Engineering Optimization 39, 87-104.
60. Tang, W.J., Wu, Q.H. and Saunders, J.R. 2006: A novel model for bacterial foraging in varying environments. Lecture Notes in Computer Science 3980, 556-65.
61. Tang, W.J., Wu, Q.H. and Saunders, J.R. 2007: A bacterial swarming algorithm for global optimization. Proceedings of the IEEE Congress on Evolutionary Computation. Singapore.
62. Theraulaz, G. and Bonabeau, E. 1999: A brief history of stigmergy. Artificial Life 5, 97-116.
63. Venkatraman, S. and Yen, G.G. 2005: A generic framework for constrained optimization using genetic algorithms. IEEE Transactions on Evolutionary Computation 9, 424-35.
64. Viswanathan, G.M., Buldyrev, S.V., Havlin, S., da Luz, M.G., Raposo, E. and Stanley, H.E. 1999: Optimizing the success of random searches. Nature 401, 911-14.
65. Vlachos, C., Paton, R.C., Saunders, J.R. and Wu, Q.H. 2005: A rule-based approach to the modelling of bacterial ecosystems. BioSystems 84, 49-72.
66. Wachowiak, M.P., Smolikova, R. and Zheng, Y. 2004: An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Transactions on Evolutionary Computation 8, 289-301.
67. Wu, Q.H. and Ma, J.T. 1995: Power system optimal reactive dispatch using evolutionary programming. IEEE Transactions on Power Systems 10, 1243-49.
68. Wu, Q.H., Cao, Y.J. and Wen, J.Y. 1998: Optimal reactive power dispatch using an adaptive genetic algorithm. International Journal of Electrical Power and Energy Systems 20, 563-69.
69. Yamada, T. and Nakano, R. 1997: Genetic algorithms for job-shop scheduling problems. UNICOM seminar, Proceedings of Modern Heuristic for Decision Support, 67-81.
70. Yao, X., Liu, Y. and Lin, G. 1999: Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation 3, 82-102.