Abstract
In this chapter, we propose a new approach to solve the most general form of global optimization problems, namely, non-convex Mixed-Integer Nonlinear Programming (MINLP) and non-convex Nonlinear Programming (NLP) problems. The target is to solve MINLP/NLP problems from different domains of research in fewer fitness evaluations as compared to other state-of-the-art stochastic algorithms in the area. The algorithm is GPU compatible and shows remarkable speedups over nVidia’s CUDA-compatible GPUs. MINLP problems involve both discrete and continuous variables with several active nonlinear equality and inequality constraints making them extremely difficult to solve. The proposed algorithm is named the adaptive resolution Genetic Algorithm (arGA) as it exploits the concept of controlling the search space size and resolution in an adaptive manner. Using entropy measures, the proposed algorithm adaptively controls the intensity of the genetic search in a given sub-solution space, i.e., promising regions are searched more intensively as compared to other regions. The algorithm is equipped with an asynchronous adaptive local search operator to further improve the performance. Niching is incorporated by using a technique inspired from the Tabu search. The algorithm reduces the chances of convergence to local optima by maintaining a list of already visited optima and penalizing their neighborhoods. The proposed technique was able to find the best-known solutions to extremely difficult MINLP/NLP problems in fewer fitness evaluations and in a competitive amount of time. The results section discusses the performance of the algorithm and the effect of different operators by using a variety of MINLP/NLPs from different problem domains. GPU implementation shows a speedup of up to 42× for single precision and 20× for double precision implementation over the nVidia C2050 GPU architecture.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Agrawal, R.B., Deb, K.: Simulated binary crossover for continuous search space. Department of Mechanical Engineering, Indian Institute of Technology, Kanpur, UP, India (1994)
Danish, M., Kumar, S., Qamareen, A., Kumar, S.: Optimal solution of MINLP problems using modified genetic algorithm. Chem. Prod. Process Model. 1(1) (2006)
El-mihoub, T.A., Hopgood, A.A., Nolle, L., Battersby, A.: Hybrid genetic algorithms: A review. Eng. Lett. 13(12), 124–137 (2006)
French, A.P., Robinson, A.C., Wilson, J.M.: Using a hybrid genetic-algorithm/branch and bound approach to solve feasibility and optimization integer programming problems. J. Heuristics 7(6), 551–564 (2001)
Gantovnik, V.B., Gurdal, Z., Watson, L.T., Anderson-Cook, C.M.: A genetic algorithm for mixed integer nonlinear programming problems using separate constraint approximations. Departmental Technical Report TR-03-22, Computer Science, Virginia Polytechnic Institute and State University (2005)
GENO: General evolutionary numerical optimizer. http://tomopt.com/tomlab/products/geno/
Goldberg, D.: Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley Professional, Upper Saddle River (1989)
Goldberg, D., Deb, K., Clark, J.: Genetic algorithms, noise, and the sizing of populations. Complex Syst. 6, 333–362 (1991)
Grossmann, I.E.: Review of nonlinear mixed-integer and disjunctive programming techniques. Optim. Eng. 3, 227–252 (2002)
Holland, J.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)
Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. SPIE Intell. Control Adapt. Syst. 1196, 289–296 (1989)
Lemonge, A.C., Barbosa, H.J.: An adaptive penalty scheme for genetic algorithms in structural optimization. Int. J. Numer. Methods Eng. 59(5), 703–736 (2004)
Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. 8(1), 3–30 (1998)
Matsumoto, M., Nishimura, T.: Dynamic creation of pseudorandom number generators. In: Monte Carlo and Quasi-Monte Carlo Methods 1998. Springer, Berlin (2000)
Munawar, A., Wahib, M., Munetomo, M., Akama, K.: Hybrid of genetic algorithm and local search to solve MAX-SAT problem using NVIDIA CUDA framework. Genet. Program. Evol. Mach. 10(4), 391–415 (2009)
Podlozhnyuk, V.: Parallel mersenne twister, CUDA 2.1 SDK Documentation (June 2007)
Ponsich, A., Azzaro-Pantel, C., Domenech, S., Pibouleau, L.: Some guidelines for genetic algorithm implementation in MINLP batch plant design problems. In: Advances in Metaheuristics for Hard Optimization. Natural Computing Series [ISSN 1619-7127]. Springer, Berlin (2008)
Schlueter, M.: Midaco: Global optimization software for mixed integer nonlinear programming. http://www.midaco-solver.com (2009)
Schlueter, M., Gerdts, M.: The oracle penalty method. J. Glob. Optim. 47(2), 293–325 (2010)
Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27, 3–55 (2001)
Young, C., Zheng, Y., Yeh, C., Jang, S.: Information-guided genetic algorithm approach to the solution of MINLP problems. Ind. Eng. Chem. Res. 46(5), 1527–1537 (2007)
Acknowledgement
This work is supported by Grant-in-Aid for Scientific Research (C) No. 22500196 by MEXT, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Munawar, A., Wahib, M., Munetomo, M., Akama, K. (2013). arGA: Adaptive Resolution Micro-genetic Algorithm with Tabu Search to Solve MINLP Problems Using GPU. In: Tsutsui, S., Collet, P. (eds) Massively Parallel Evolutionary Computation on GPGPUs. Natural Computing Series. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37959-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-37959-8_5
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37958-1
Online ISBN: 978-3-642-37959-8
eBook Packages: Computer ScienceComputer Science (R0)