Abstract
The article presents an exploration of a novel optimisation method, called Free Search. Free Search is population-based and can be classified as an evolutionary computational method.
Free Search is examined by using a hard non-linear constrained optimisation problem. The experimental results of twenty and fifty dimensional variants of the test problem are presented and discussed.
The algorithm is also applied to a traffic management optimisation model. It explores how adaptive computing can support air traffic dispatchers, who attempt to satisfy requirements for safety and efficiency constrained by the environmental impacts. The results suggest that the Free Search can provide decision-making with optimised traffic information.
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References
Angeline P., (1998), Evolutionary Optimisation versus Particle Swarm Optimisation: Philosophy and Performance Difference, The 7-th Annual Conference on Evolutionary Programming, San Diego, USA
Bilchev G., I. Parmee, (1996), Constrained Optimisation With an Ant Colony Search Model, Proceedings of ACED’96, PEDC, University of Plymouth, UK.
Bilchev G., I. Parmee, (1995), The Ant Colony Metaphor for Searching Continuous Design Space, Proceedings of the AISB Workshop on Evolutionary Computation, University of Sheffield, UK, April 3-4.
Corne D., M. Dorigo, and F. Glover, (1999), New Ideas in Optimization. ISBN 007 7095065, McGraw-Hill Internationa
Goldberg D., (2001), Genetic Algorithms in Search, Optimisation, and Machine Learning, ISBN 0-201-15767-5, Addison-Wesley.
Dorigo M., G. Agazzi, G. Di Caro, L. Gambardella, R. Michel, M. Middendorf, T. Stutzle, E. Taillard, (1999) Part One, Ant Colony Optimization, in Editors Corne D., M. Dorigo, and F. Glover, New Ideas in Optimization. ISBN 007 7095065, McGraw-Hill International. (pp. 9–76).
Dorigo M., G. Di Caro, L. Gambardella, (1998), Ant Algorithms for Discrete Optimisation, TR 98-10, IRIDIA, University Libre de Bruxelles.
Eberhart R. and J. Kennedy, (1995), Particle Swarm Optimisation, Proceedings of the IEEE International Conference on Neural Networks, vol.4, 1942–1948.
Eiben, A. E., and J. E. Smith, 2003, Introduction to Evolutionary Computing, Springer, ISBN 3-540-40184-9, (pp 15–35).
EI-Beltagy M. A., and A. I. Keane, (1998), Optimisation for Multilevel Problems: A Comparison of Various Algorithms, In I.C. Parmee editor, Adaptive computing in design and manufacture, ISBN 3-540-76254-X Springer — Verlag London Limited. (pp. 111–120).
Eshelman, L. J., & Schaffer, J. D., (1993), Real-coded genetic algorithms and interval-schemata, Foundations of Genetic Algorithms 2, Morgan Kaufman Publishers, San Mateo, pp. 187–202.
Fogel G., (2000), Evolutionary Computation: Towards a New Philosophy of Machine Inteligence, Second Edition, IEEE Press, ISBN: 0-7803-5379-X
Ghasemi M.R., E. Hinton and S. Bulman, (1998), Performance of Genetic Algorithms for Optimization of Frame Structures, In I.C. Parmee editor, Adaptive computing in design and manufacture, ISBN 3-540-76254-X Springer-Verlag London Limited. (pp. 287–299).
Holland J., (1975), Adaptation In Natural and Artificial Systems, University of Michigan Press.
Keane A. J., (1995), Genetic algorithm optimization of multi-peak problems: studies in convergence and robustness, Artificial Intelligence in Engineering 9(2) (pp. 75–83).
Keane A. J., (1996), A Brief Comparison of Some Evolutionary Optimization Methods, In V. Rayward-Smith, I. Osman, C. Reeves and G.D. Smith, J. Wiley (Editors), Modern Heuristic Search Methods, ISBN 0471962805 pp 255–272.
Michalewicz, Z. and Schoenauer, M., (1996), Evolutionary Algorithms for Constrained Parameter Optimization Problems, Evolutionary Computation, Vol.4, No.1, (pp.1–32).
Michalewicz, Z. and Fogel, D., (2002), How to Solve It: Modern Heuristics, ISBN 3-540-66061-5 Springer-Verlag, Berlin, Heidelberg, New York.
Penev, K., (2001), GIS in Support of Traffic Management, MPhil thesis submitted in partial fulfilment of the requirements of The Nottingham Trent University, UK, August, (pp 5–23).
Penev, K., and Littlefair, G., (2003), Free Search — a Novel Heuristic Method, Proceedings of the PREP 2003, 14-16 April, Exeter, UK, (pp 133–134).
Penev, K., and Littlefair, G., (2003), Free Search — A Comprative Analysis, Submitted to Information Sciences, Special Issue on Genetic and Evolutionary Computing, Elsevier.
Price K., and R. Storn, (1997), Differential Evolution, Dr, Dobb’s Journal 22(4), (April), (pp. 18–24).
Price K., K. Chisholm, J. Lampinen, R. Storn,, I. Zelinka, (1999), Part Two Differential Evolution, in Editors Corne D., M. Dorigo, and F. Glover, New Ideas in Optimisation. ISBN 007 7095065, McGraw-Hill International (pp 77–158).
Schoenauer, M. and Michalewicz, Z., (1996), Evolutionary Computation at the Edge of Feasibility, Proceedings of the 4th Parallel Problem Solving from Nature, H. M. Voigt, W. Ebeling, I. Rechenberg, and H. P. Schwefel (Editors), Springer-Verlag, Lecture Notes in Computer Science, Vol.1141 (pp.245–254).
Smith K., (2001), Incompatible goals, uncertain information and conflicting incentives: the dispatch dilemma, Human Factor and Aerospace Safety, Ashgate Publishing 1(4), (pp. 361–380).
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Penev, K. (2004). Adaptive Computing in Support of Traffic Management. In: Parmee, I.C. (eds) Adaptive Computing in Design and Manufacture VI. Springer, London. https://doi.org/10.1007/978-0-85729-338-1_25
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DOI: https://doi.org/10.1007/978-0-85729-338-1_25
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