Research Article
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Year 2020, Volume: 6 Issue: 1, 42 - 62, 31.03.2020

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References

  • [1] Spall, J. (1998). An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest, 19, 482-492. [2] Goldberg, D., E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison–Wesley Publishing Company. [3] Mukhopadhyay, A., Maulik, A., Bandyopadhyay, S., & Coello, C., A., C. (2014). A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II. IEEE Trans. On Evolutionary Computation, 18(1), 20-35. [4] Kennedy, J., & Eberhart R., C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann PublishersInc. [5] Storn, R., & Price K. (1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization, 11(4), 341–359. [6] Dorigo, M., Maniezzo V., & Colorni A. (1996). The ant system: optimization by a colony of cooperative agents. IEEE Transactions on System Man Cybernet, 26, 29-41. [7] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697. [8] Beyer, H. & Schwefel, H. (2002). Evolution strategies-Acomprehensive introduction.Natural Computing, 1, 3-52. [9] Ni, H., & Wang, Y. (2013). Stock index tracking by pareto efficient genetic algorithm. Applied Softcomputing, 13(12),4519-4535. [10] Dhiman, R., & Priyanka, J., S. (2014). Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures. Applied Softcomputing, 19, 8-17. [11] Chen, S., H., Chen, M., C., & Liou, Y., C. (2014). Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times. Applied Softcomputing, 17, 167-175. [12] Abiyev, R., H., & Menekay, M. (2007). Fuzzy Portfolio Selection Using Genetic Algorithm. Soft Computing- A Fusion of Foundations, Methodologies and Applications, Springer, Berlin/ Heidelberg,11(12), 1157-1163. [13] Abiyev, R. & Tunay, M. (2015). Optimization of High Dimensional Functions through Hypercube Evaluation. Computational Intelligence and Neuroscience, Volume 2015, 2015. [14] Eduardo, L., R., José Luis, C., V., Belén, M., B., & Marcos Moreno-Vega J. (2014). Biased random key genetic algorithm for the tactical berth allocation problem. Applied Softcomputing, 22, 60-76. [15] Vaisakh, K., Srinivas, L., R., & Meah, K. (2014). Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis. Applied Softcomputing, 13, 4579-4593. [16] Sörensen, K. (2013). Metaheuristics—the metaphor exposed. International Transactions in Operational Research,22(1), 3–18. [17] Hansen, N., & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2), 159-195. [18] Su, M-C., Su, S-Y., & Zhao, Y-X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42, 2764-2786. [19] Belal, M., A., & Haggag, M., H. (2013). A structured-population genetic-algorithm based on hierarchical hypercube of genes expressions. International Journal of Computer Applications, 64(22), 5-18. [20] Chen, C., C. (2011). Two-layer particle swarm optimization for unconstrained optimization problems. Applied Soft Computing, 11(1), 295-304. [21] Precup, R., E., David, R., C., Petriu, E., M., Preitl, S., & Radac, M., B. (2012). Fuzzy control systems with reduced parametric sensitivity based on simulated annealing. IEEE Transactions on Industrial Electronics, 59(8), 3049-3061. [22] Wang, Y., Li, B., & Weise, T. (2013). Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design. Information Sciences, 220, 408-424. [23] Yazdani, D., Nasiri B., Azizi R., Sepas-Moghaddam, A., & Meybodi, M., R. (2013). Optimization in dynamic environments utilizing a novel method based on particle swarm optimization. International Journal of Artificial Intelligence, 11(A13), 170-192. [24] Weise, T., Skubch, H., Zapf, M., and Geihs, K. (2008). Global optimization algorithms and their application to distributed systems, Fachbereich 16: Elektrotechnik/ Informatik, Univ. Kassel. [25] Holland, J. (1992). Adaptation in natural and artificial systems. Cambridge: University of Michigan Press, Extended new Edition, MIT Press. [26] Li, X., Yao, X. (2012). Cooperatively Coevolving Particle Swarms for Large Scale Optimization. IEEE Transaction on Evolutionary Computation, 16(2), 210-224. [27] Liang, J., J., Qin, A., K., Suganthan, P., N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transaction on Evolutionary Computation, 10(3), 281-295. [28] Zhang, J., & Sanderson, A., C. (2007). JADE: Self-Adaptive Differential Evoluation with Fast and Reliable Convergence Performance. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 2251-2258). Singapore. [29] Bose, D., Biswas, S., Vasilakos, A., V., & Laha, S. (2014). Optimal filter design using an improved artificial bee colony algorithm. Information Sciences 281, 443–461. [30] Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. In IEEE Transactions On Evolutionary Computation, 10(6), 646-657. [31] Davison, V. E., and E. G. Sullivan. 1963. Mourning doves' selection of foods. J. Wildl. Manage. 27:373-383. [32] Attracting Doves to Your Land. http://www.caes.uga.edu/extension/taylor/anr/documents/AttractingDovestoYourLand.pdf [33] Hansen, N., Auger, A., Ros, R., Finck, S., & Pošík, P. (2010). Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation (pp. 1689-1696). Prague: Czech Technical University. [34] Grosan,C. (2009). A. Abraham, A Novel Global Optimization Technique for High Dimensional Functions. Inter. Journal of Intelligent Systems 24, 421–440. [35] Dixon, L., C., W. and Szegö, G. (1978). “The global optimization problem: An introduction,” in Proc. Toward Global Optimization 2, Amsterdam, Netherlands: North-Holland, pp. 1–15. [36] Liang, J., J., Qu, B-Y., & Suganthan P., N. (2013). Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization. Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou: Zhengzhou University, and Technical Report. Singapore: Nanyang Technological University, China. [37] Erlich, I., Rueda, J., L., Wildenhues, S., & Shewarega, F. (2014). Evaluating the Mean-Variance Mapping Optimization on the IEEE-CEC 2014 Test Suite. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1625-1632). Beijing, China. [38] Chen, L., Zheng, Z., Liu, H., L., Xie, Shengli. (2014). An Evolutionary Algorithm Based on Covariance Matrix Leaning and Searching Preference for Solving CEC 2014 Benchmark Problems. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 2672-2677). Beijing, China. [39] Mallipeddi, R., Wu, G., Lee M., & Suganthan, P., N. (2014). Gaussian Adaptation based Parameter Adaptation for Differential Evolution. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1760-1767). Beijing, China. [40] Yashesh D., Deb K., and Bandaru, S. (2014). Non-Uniform Mapping in Real-Coded Genetic Algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2237-2244). Beijing, China. [41] Bujok, P., Tvrdık, J., & Polakova, R. (2014). Differential Evolution with Rotation-Invariant Mutation and Competing-Strategies Adaptation. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2253-2258). Beijing, China. [42] Elsayed, S., M., Sarker, R., A., Essam D., L., & Hamza N., M. (2014). Testing United Multi-Operator Evolutionary Algorithms on the CEC2014 Real-Parameter Numerical Optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1650-1657). Beijing, China. [43] Tanabe, R., & Fukunaga, A., S. (2014). Improving the Search Performance of SHADE Using Linear Population Size Reduction. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 1658-1665 ). Beijing, China. [44] Qu, B., Y., Liang, J., J., Xiao, J., M., & Shang, Z., G. (2014). Memetic Differential Evolution Based on Fitness Euclidean-Distance Ratio. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2266-2273). Beijing, China. [45] Hu Z., Bao Y., and Xiong T. (2014) Partial Opposition-Based Adaptive Differential Evolution Algorithms: Evaluation on the CEC 2014 Benchmark Set for Real-parameter Optimization. . In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2259-2265). Beijing, China. [46] Li, Z., Shang, Z., Qu, B., Y., & Liang J., J. (2014). Differential Evolution Strategy based on the Constraint of Fitness Values. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1454-1460). Beijing, China. [47] Polakov, R., Tvrdık J., Bujok, P. (2014). Controlled Restart in Differential Evolution Applied to CEC2014 Benchmark Functions. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2230-2236). Beijing, China. [48] Dourado Maia R., Nunes de Castro L., and Matos Caminhas, W. (2014). Real-Parameter Optimization with OptBees. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2649-2655). Beijing, China. [49] Xu, C., Huang, H., & Ye, S. (2014). A Differential Evolution with Replacement Strategy for Real-Parameter Numerical Optimization. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 1617-1624). Beijing, China. [50] Burke, E., K., Elliman, D., G., & Weare, R., F. (1995). A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems. In Proceedings of the 6th International Conference on Genetic Algorithms (pp. 15-19). Pittsburgh, USA. [51] Burke, E., K., Newall, J., P. (1999). A multistage evolutionary algorithm for the timetable problem. IEEE Transaction on Evolutionary Computation 3(1), 1085-1092.

Evolutionary Search Algorithm Based on Hypercube Optimization For High-Dimensional Functions

Year 2020, Volume: 6 Issue: 1, 42 - 62, 31.03.2020

Abstract

1347/5000
Bu çalışma, çok değişkenli sistemlerin optimizasyonunu çözmek için yeni bir evrimsel arama algoritması tasarlanmıştır. Günümüzde birçok gerçek dünya optimizasyon problemi için, yüksek optimizasyon doğruluğuna sahip evrimsel arama algoritmasının tasarımı, çok değişkenli sistemlerin önemli bir optimizasyonudur. Önerilen optimizasyon arama algoritması, hiperküp evrimine dayanan yeni bir yoğun stokastik arama yöntemidir. Bu algoritma, gerçek dünyada yem arama yerlerini keşfeden bir güvercin davranışından esinlenmiştir. Hiper küp, gerçek hayatta bir güvercin davranışı için yaşam alanını gösteren bir ifade olarak kullanılır.
Önerilen algoritmanın performansı, bazı Benchmark ve yeni yüksek boyutlu optimizasyon fonksiyonlarında test edilmiştir. Önerilen algoritmanın performansı EA'larda çok daha iyi deneysel sonuçlar elde edilmiştir. Buna ek olarak, önerilen algoritma yaklaşımı bitişik dönemlerde aynı anda iki sınav, sınav çatışması, bir günde iki veya daha fazla sınav vb. Gibi bir zaman çizelgesi problemini çözmek için uygulanır. karmaşıklıklarında önemli bir artış.

References

  • [1] Spall, J. (1998). An overview of the simultaneous perturbation method for efficient optimization. Johns Hopkins APL Technical Digest, 19, 482-492. [2] Goldberg, D., E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison–Wesley Publishing Company. [3] Mukhopadhyay, A., Maulik, A., Bandyopadhyay, S., & Coello, C., A., C. (2014). A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II. IEEE Trans. On Evolutionary Computation, 18(1), 20-35. [4] Kennedy, J., & Eberhart R., C. (2001). Swarm Intelligence. San Francisco: Morgan Kaufmann PublishersInc. [5] Storn, R., & Price K. (1997). Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optimization, 11(4), 341–359. [6] Dorigo, M., Maniezzo V., & Colorni A. (1996). The ant system: optimization by a colony of cooperative agents. IEEE Transactions on System Man Cybernet, 26, 29-41. [7] Karaboga, D., & Basturk, B. (2008). On the performance of artificial bee colony (ABC) algorithm. Applied Soft Computing, 8(1), 687-697. [8] Beyer, H. & Schwefel, H. (2002). Evolution strategies-Acomprehensive introduction.Natural Computing, 1, 3-52. [9] Ni, H., & Wang, Y. (2013). Stock index tracking by pareto efficient genetic algorithm. Applied Softcomputing, 13(12),4519-4535. [10] Dhiman, R., & Priyanka, J., S. (2014). Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures. Applied Softcomputing, 19, 8-17. [11] Chen, S., H., Chen, M., C., & Liou, Y., C. (2014). Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times. Applied Softcomputing, 17, 167-175. [12] Abiyev, R., H., & Menekay, M. (2007). Fuzzy Portfolio Selection Using Genetic Algorithm. Soft Computing- A Fusion of Foundations, Methodologies and Applications, Springer, Berlin/ Heidelberg,11(12), 1157-1163. [13] Abiyev, R. & Tunay, M. (2015). Optimization of High Dimensional Functions through Hypercube Evaluation. Computational Intelligence and Neuroscience, Volume 2015, 2015. [14] Eduardo, L., R., José Luis, C., V., Belén, M., B., & Marcos Moreno-Vega J. (2014). Biased random key genetic algorithm for the tactical berth allocation problem. Applied Softcomputing, 22, 60-76. [15] Vaisakh, K., Srinivas, L., R., & Meah, K. (2014). Genetic evolving ant direction particle swarm optimization algorithm for optimal power flow with non-smooth cost functions and statistical analysis. Applied Softcomputing, 13, 4579-4593. [16] Sörensen, K. (2013). Metaheuristics—the metaphor exposed. International Transactions in Operational Research,22(1), 3–18. [17] Hansen, N., & Ostermeier, A. (2001). Completely derandomized self-adaptation in evolution strategies. Evolutionary computation, 9(2), 159-195. [18] Su, M-C., Su, S-Y., & Zhao, Y-X. (2009). A swarm-inspired projection algorithm. Pattern Recognition, 42, 2764-2786. [19] Belal, M., A., & Haggag, M., H. (2013). A structured-population genetic-algorithm based on hierarchical hypercube of genes expressions. International Journal of Computer Applications, 64(22), 5-18. [20] Chen, C., C. (2011). Two-layer particle swarm optimization for unconstrained optimization problems. Applied Soft Computing, 11(1), 295-304. [21] Precup, R., E., David, R., C., Petriu, E., M., Preitl, S., & Radac, M., B. (2012). Fuzzy control systems with reduced parametric sensitivity based on simulated annealing. IEEE Transactions on Industrial Electronics, 59(8), 3049-3061. [22] Wang, Y., Li, B., & Weise, T. (2013). Two-stage ensemble memetic algorithm: Function optimization and digital IIR filter design. Information Sciences, 220, 408-424. [23] Yazdani, D., Nasiri B., Azizi R., Sepas-Moghaddam, A., & Meybodi, M., R. (2013). Optimization in dynamic environments utilizing a novel method based on particle swarm optimization. International Journal of Artificial Intelligence, 11(A13), 170-192. [24] Weise, T., Skubch, H., Zapf, M., and Geihs, K. (2008). Global optimization algorithms and their application to distributed systems, Fachbereich 16: Elektrotechnik/ Informatik, Univ. Kassel. [25] Holland, J. (1992). Adaptation in natural and artificial systems. Cambridge: University of Michigan Press, Extended new Edition, MIT Press. [26] Li, X., Yao, X. (2012). Cooperatively Coevolving Particle Swarms for Large Scale Optimization. IEEE Transaction on Evolutionary Computation, 16(2), 210-224. [27] Liang, J., J., Qin, A., K., Suganthan, P., N., & Baskar, S. (2006). Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Transaction on Evolutionary Computation, 10(3), 281-295. [28] Zhang, J., & Sanderson, A., C. (2007). JADE: Self-Adaptive Differential Evoluation with Fast and Reliable Convergence Performance. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 2251-2258). Singapore. [29] Bose, D., Biswas, S., Vasilakos, A., V., & Laha, S. (2014). Optimal filter design using an improved artificial bee colony algorithm. Information Sciences 281, 443–461. [30] Brest, J., Greiner, S., Boskovic, B., Mernik, M., & Zumer, V. (2006). Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems. In IEEE Transactions On Evolutionary Computation, 10(6), 646-657. [31] Davison, V. E., and E. G. Sullivan. 1963. Mourning doves' selection of foods. J. Wildl. Manage. 27:373-383. [32] Attracting Doves to Your Land. http://www.caes.uga.edu/extension/taylor/anr/documents/AttractingDovestoYourLand.pdf [33] Hansen, N., Auger, A., Ros, R., Finck, S., & Pošík, P. (2010). Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009. In Proceedings of the 12th annual conference companion on Genetic and evolutionary computation (pp. 1689-1696). Prague: Czech Technical University. [34] Grosan,C. (2009). A. Abraham, A Novel Global Optimization Technique for High Dimensional Functions. Inter. Journal of Intelligent Systems 24, 421–440. [35] Dixon, L., C., W. and Szegö, G. (1978). “The global optimization problem: An introduction,” in Proc. Toward Global Optimization 2, Amsterdam, Netherlands: North-Holland, pp. 1–15. [36] Liang, J., J., Qu, B-Y., & Suganthan P., N. (2013). Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization. Technical Report 201311, Computational Intelligence Laboratory, Zhengzhou: Zhengzhou University, and Technical Report. Singapore: Nanyang Technological University, China. [37] Erlich, I., Rueda, J., L., Wildenhues, S., & Shewarega, F. (2014). Evaluating the Mean-Variance Mapping Optimization on the IEEE-CEC 2014 Test Suite. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1625-1632). Beijing, China. [38] Chen, L., Zheng, Z., Liu, H., L., Xie, Shengli. (2014). An Evolutionary Algorithm Based on Covariance Matrix Leaning and Searching Preference for Solving CEC 2014 Benchmark Problems. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 2672-2677). Beijing, China. [39] Mallipeddi, R., Wu, G., Lee M., & Suganthan, P., N. (2014). Gaussian Adaptation based Parameter Adaptation for Differential Evolution. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1760-1767). Beijing, China. [40] Yashesh D., Deb K., and Bandaru, S. (2014). Non-Uniform Mapping in Real-Coded Genetic Algorithms. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2237-2244). Beijing, China. [41] Bujok, P., Tvrdık, J., & Polakova, R. (2014). Differential Evolution with Rotation-Invariant Mutation and Competing-Strategies Adaptation. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2253-2258). Beijing, China. [42] Elsayed, S., M., Sarker, R., A., Essam D., L., & Hamza N., M. (2014). Testing United Multi-Operator Evolutionary Algorithms on the CEC2014 Real-Parameter Numerical Optimization. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1650-1657). Beijing, China. [43] Tanabe, R., & Fukunaga, A., S. (2014). Improving the Search Performance of SHADE Using Linear Population Size Reduction. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 1658-1665 ). Beijing, China. [44] Qu, B., Y., Liang, J., J., Xiao, J., M., & Shang, Z., G. (2014). Memetic Differential Evolution Based on Fitness Euclidean-Distance Ratio. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2266-2273). Beijing, China. [45] Hu Z., Bao Y., and Xiong T. (2014) Partial Opposition-Based Adaptive Differential Evolution Algorithms: Evaluation on the CEC 2014 Benchmark Set for Real-parameter Optimization. . In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2259-2265). Beijing, China. [46] Li, Z., Shang, Z., Qu, B., Y., & Liang J., J. (2014). Differential Evolution Strategy based on the Constraint of Fitness Values. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 1454-1460). Beijing, China. [47] Polakov, R., Tvrdık J., Bujok, P. (2014). Controlled Restart in Differential Evolution Applied to CEC2014 Benchmark Functions. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2230-2236). Beijing, China. [48] Dourado Maia R., Nunes de Castro L., and Matos Caminhas, W. (2014). Real-Parameter Optimization with OptBees. In Proceedings of the IEEE Congress on Evolutionary Computation (pp. 2649-2655). Beijing, China. [49] Xu, C., Huang, H., & Ye, S. (2014). A Differential Evolution with Replacement Strategy for Real-Parameter Numerical Optimization. In Proceedings of theIEEE Congress on Evolutionary Computation (pp. 1617-1624). Beijing, China. [50] Burke, E., K., Elliman, D., G., & Weare, R., F. (1995). A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems. In Proceedings of the 6th International Conference on Genetic Algorithms (pp. 15-19). Pittsburgh, USA. [51] Burke, E., K., Newall, J., P. (1999). A multistage evolutionary algorithm for the timetable problem. IEEE Transaction on Evolutionary Computation 3(1), 1085-1092.
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Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Mustafa Tunay 0000-0001-8843-621X

Publication Date March 31, 2020
Submission Date February 4, 2020
Acceptance Date March 16, 2020
Published in Issue Year 2020 Volume: 6 Issue: 1

Cite

APA Tunay, M. (2020). Evolutionary Search Algorithm Based on Hypercube Optimization For High-Dimensional Functions. International Journal of Computational and Experimental Science and Engineering, 6(1), 42-62.