Abstract
Various scientific and technological fields, such as design, engineering, physics, chemistry, economics, business, and finance often face multidimensional optimisation problems. Although substantial research efforts have been directed in this area, key questions are still waiting for answers, such as: What limits computer aided design systems on optimisation tasks with high variables number? How to improve capabilities of modern search methods applied to multidimensional problems? What are software and hardware constraints? Approaching multidimensional optimisation problems raises in addition new research questions, which cannot be seen or identified on low dimensional tasks, such as: What time is required to resolve multidimensional task with acceptable level of precision? How dimensionality reflects on the search space complexity? How to establish search process orientation, within multidimensional space? How task specific landscapes embarrass orientation? This article presents an investigation on 300 dimensional heterogeneous real-value numerical tests. The study aims to evaluate relation between tasks’ dimensions’ number and required for achieving acceptable solution with non-zero probability number of objective function evaluations. Experimental results are presented, analysed and compared to other publications.
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References
Ackley, D.H.: A Connectionist Machine for Genetic Hillclimbing. Kluwer, Boston (1987)
Censor, Y.: Optimisation Methods, Encyclopedia of Computer Science. Nature Publishing Group, London (2000). pp. 1339–1341
De Jung, K.A.: An analysis of the behaviour of a class of genetic adaptive systems. Ph.D thesis, University of Michigan, USA (1975)
Griewank, A.O.: Generalized decent for global optimization. J. Optim. Theor. Appl. 34, 11–31 (1981)
Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1992)
Penev, K.: Free search of real value or how to make computers think. St. Qu, UK (2008). ISBN 978-0-9558948-0-0
Penev, K.: Free search – comparative analysis 100. Int. J. Metaheuristics 3(2), 118–132 (2014)
Penev, K.: Free search in multidimensional space II. In: Dimov, I., Fidanova, S., Lirkov, I. (eds.) NMA 2014. LNCS, vol. 8962, pp. 103–111. Springer, Heidelberg (2015)
Rosenbrock, H.H.: An automate method for finding the greatest or least value of a function. Comput. J 3, 175–184 (1960)
Acknowledgements
I would like to thank to my students Adel Al Hamadan, Asim Al Nashwan, Dimitrios Kalfas, Georgius Haritonidis, and Michael Borg for the design, implementation and overclocking of desktop PC used for completion of the experiments presented in this article.
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Penev, K. (2015). Free Search in Multidimensional Space III. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds) Large-Scale Scientific Computing. LSSC 2015. Lecture Notes in Computer Science(), vol 9374. Springer, Cham. https://doi.org/10.1007/978-3-319-26520-9_27
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DOI: https://doi.org/10.1007/978-3-319-26520-9_27
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