An adaptive Multi-Point Random Search (AMPRS) for Global Minimum Optimization Value of Multimodal Functions
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Published:
Jan 30, 2019
Keywords:
Global Minimum Optimization Multimodal Functions Adaptive Multi-Point Random Search (AMPRS)
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Abstract
This research studied an adaptive Multi-Point Random Search (AMPRS) for global minimum value of multimodal functions consisted of main elements including population number, a search direction, step size, population operation and crossover rate of vectors. In which this method was performed by using 5 selected test functions with the following parameters: the variable number N = 2, 5, 10, 20 and 30; the crossover rate CR = 0.1, 0.3, 0.5, 0.7 and 0.9, and the population number NP= 10, 30, 50, 70 and 90. The study found that the crossover rate CR = 0.9 and the population number NP = 50 were the suitable global minimum optimization value of multimodal functions using an adaptive Multi-Point Random Search.
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References
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2. Holland, J.H. 1975. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, Michigan; re-issued by MIT Press.
3. Storn, R.N. and Price, K.N. 1997. Differential Evolution-A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization.
4. Mohamed, Ali W., Hegazy Z. Sabry and Motaz Khorshid. 2012. An alternative differential evolution algorithm for global optimization.
5. Zabinsky, Zelda B. 2009. Random Search Algorithms, Department ofIndustrial and Engineering, University of Washington. Seattle. April 5.
6. Price K., Storn R., and Lampinen J. 2005. Differential Evolution – A Practical Approach to Global Optimization, Springer. Berlin Heidelberg New York.
7. Sotiropoulos, D.G., Plagianakos, V.P. and Vrahatis, M.N. An Evolutionary Algorithm for Minimizing Multimodal Function, Department of Mathematics, Uiverity of Patras, GR-261.10 Patras, Greece. University of Patras Artificial Inteligence Research Center-UPAIRC.
8. Zelda B. Zabinsky. 2009. Random Search Algorithms, Department of Industrial and Systems Engineering, University of Washington, Seattle, Wa, 98195-2650, USA.
9. Francisco, J.Solis and Roger, J-B. Wets. 1981. Minimization By Random Search Techniques, Mathematics of Operations Research.
