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A Hybrid DE - HS Algorithm with Randomized Parameters

Year 2018, CMES 2018, 119 - 124, 30.11.2018
https://doi.org/10.17714/gumusfenbil.427696

Abstract

The evolutionary algorithms and their hybrid methods
are quite efficient and accurate in terms of solution quality of optimization.
In this study, a new hybrid algorithm is generated by merging Differential
Evolution (DE) and Harmony Search Optimization (HS) algorithms which is called
DES. The core steps of the algorithms are used without any modifications, but
the main control parameters which directly affect the performance are
randomized. The experimental study is done by comparing DE, HS and their hybrid
method DES. According to the results, it is found that DES algorithm has
improved the performances of original algorithms for the selected test
problems.

References

  • Chakraborty, P., Roy, G.G., Das, S., Jain, D. and Abraham, A., 2009. An Improved Harmony Search Algorithm with Differential Mutation Operator, Fundamenta Informaticae, 95, 401-426.
  • Chellaswamy, C. and Ramesh, R., 2016. Parameter Extraction of Solar Cell Models Based on Adaptive Differential Evolution Algorithm, Renewable Energy, 97, 823-837.
  • Chen, J., Pan, Q.K. and Li, J.Q., 2012. Harmony Search Algorithm with Dynamic Control Parameters, Applied Mathematics and Computation, 219, 592-604.
  • Delice, Y., Aydoğan, E.K., Özcan, U. and İlkay, M.S., 2017. A Modified Particle Swarm Optimization Algorithm to Mixed-Model Two-sided Assembly Line Balancing, Journal of Intelligent Manufacturing, 28, 23-36.
  • Gao, X.Z., Govindasamy, V., Xu, H., Wang, X. and Zenger, K., 2015. Harmony Search Method: Theory and Applications, Computational Intelligence and Neuroscience,1-10.
  • Geem, Z.W., Kim, J.H. and Loganathan, G.V, 2001. A New Heuristic Optimization Algorithm: Harmony search, Simulation, 76, 60-68.
  • Kukkonen, S.and Carlos, A.C., 2017. Generalized Differential Evolution for Numerical and Evolutionary Optimization, Springer International Publishing, 253-279.
  • Mahdavi, M., Fesanghary, M.and Damangir, E., 2007. An Improved Harmony Search Algorithm for Solving Optimization Problems, Applied mathematics and computation, 188, 1567-1579.
  • Qui, X., Xu, J.K., Tan, K.C. and Abbas, H.A., 2016. Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, 20, 232-244.
  • Roy, N., Ghosh, A.and Sanyal, K., 2016. Normal Boundary Intersection Based Multi-objective Harmony Search Algorithm for Environmental Economic Load Dispatch problem, IEEE International Conference on Power Systems, 1-6.
  • Sama, M., Pellegrini, I, P., D’ariana, A., Rodriguez, J. and Pacciarelli, D., 2016. Ant Colony Optimization for the Real-Time Train Routing Selection Problem, Transportation Research Part B: Methodological, 85, 89-108.
  • Storn, R. and Price, K., 1997. Differential Evolution–A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces”, Journal of global optimization, 11, 341-359.
  • Şimşek, B. and Şimşek E.H., 2017. Assessment and Optimization of Thermal and Fluidity Properties of High Strength Concrete via Genetic Algorithm, An international journal of optimization and Control: Theories&Applications (IJOCTA), 7 , 90-97.
  • Tvrdik, J., 2006. Competitive Differential Evolution and Genetic Algorithm in GA-DS Toolbox, Technical Computing Prague, Praha, Humusoft, 99-106.
  • Ülker, E.D., 2017. A PSO/HS Based Algorithm for Optimization Tasks, IEEE Xplore Computing Conference Proceedings 2017, London, 117-120.
  • Ülker, E.D., 2018. An Elitist Approach for Solving the Traveling Salesman Problem using an Animal Migration Optimization Algorithm, Turkish Journal of Electrical Engineering & Computer Sciences, 1, 605-617.
  • Wang, G.G., Gandomi, A.H., Zhao, X. and Chu, H.C.E., 2016. Hybridizing Harmony Search Algorithm with Cuckoo Search for Global Numerical Optimization, Soft Computing, 20, 273-285.
  • Wang, L. and Lingp-Po, L., 2013. An Effective Differential Harmony Search Algorithm for the Solving Non-convex Economic Load Dispatch Problems, International Journal of Electrical Power & Energy Systems, 44, 832-843.
  • Wolpert, D.H. and William, G. M., 1997. No Free Lunch Theorems for Optimization,1997, IEEE Transactions on Evolutionary Computation, 1, 67-87.

Rastgele Değişkenli Melez DG-HA Algoritması

Year 2018, CMES 2018, 119 - 124, 30.11.2018
https://doi.org/10.17714/gumusfenbil.427696

Abstract

Evrimsel algoritmalar ve onları
kullanarak yaratılan melez algoritmalar optimizasyon problemlerini çözmede
etkili ve doğru sonuçlar üretirler. Bu çalışmada, Diferansiyel Gelişim (DG)
algoritması ve Harmoni Arama (HA) algoritması birleştirilerek yeni bir melez
algoritma oluşturulmuştur. Birleştirilen algoritmaların ana basamakları
herhangi bir performans yükseltme yapılmadan kullanılmıştır, ancak performans
üzerinde doğrudan etkisi olduğu bilinen ana kontrol değişken değerleri için
rastgele seçim yapılmıştır. Deneysel çalışma, birleştirilen DG ve HA
algoritmaları ile onların oluşturduğu DES algoritması arasında yapılmıştır.
Elde edilen sonuçlara göre melez algoritma DES, diğer iki algoritmaya göre daha
iyi bir performans göstermiştir.

References

  • Chakraborty, P., Roy, G.G., Das, S., Jain, D. and Abraham, A., 2009. An Improved Harmony Search Algorithm with Differential Mutation Operator, Fundamenta Informaticae, 95, 401-426.
  • Chellaswamy, C. and Ramesh, R., 2016. Parameter Extraction of Solar Cell Models Based on Adaptive Differential Evolution Algorithm, Renewable Energy, 97, 823-837.
  • Chen, J., Pan, Q.K. and Li, J.Q., 2012. Harmony Search Algorithm with Dynamic Control Parameters, Applied Mathematics and Computation, 219, 592-604.
  • Delice, Y., Aydoğan, E.K., Özcan, U. and İlkay, M.S., 2017. A Modified Particle Swarm Optimization Algorithm to Mixed-Model Two-sided Assembly Line Balancing, Journal of Intelligent Manufacturing, 28, 23-36.
  • Gao, X.Z., Govindasamy, V., Xu, H., Wang, X. and Zenger, K., 2015. Harmony Search Method: Theory and Applications, Computational Intelligence and Neuroscience,1-10.
  • Geem, Z.W., Kim, J.H. and Loganathan, G.V, 2001. A New Heuristic Optimization Algorithm: Harmony search, Simulation, 76, 60-68.
  • Kukkonen, S.and Carlos, A.C., 2017. Generalized Differential Evolution for Numerical and Evolutionary Optimization, Springer International Publishing, 253-279.
  • Mahdavi, M., Fesanghary, M.and Damangir, E., 2007. An Improved Harmony Search Algorithm for Solving Optimization Problems, Applied mathematics and computation, 188, 1567-1579.
  • Qui, X., Xu, J.K., Tan, K.C. and Abbas, H.A., 2016. Adaptive Cross-Generation Differential Evolution Operators for Multiobjective Optimization”, IEEE Transactions on Evolutionary Computation, 20, 232-244.
  • Roy, N., Ghosh, A.and Sanyal, K., 2016. Normal Boundary Intersection Based Multi-objective Harmony Search Algorithm for Environmental Economic Load Dispatch problem, IEEE International Conference on Power Systems, 1-6.
  • Sama, M., Pellegrini, I, P., D’ariana, A., Rodriguez, J. and Pacciarelli, D., 2016. Ant Colony Optimization for the Real-Time Train Routing Selection Problem, Transportation Research Part B: Methodological, 85, 89-108.
  • Storn, R. and Price, K., 1997. Differential Evolution–A Simple and Efficient Heuristic for Global Optimization Over Continuous Spaces”, Journal of global optimization, 11, 341-359.
  • Şimşek, B. and Şimşek E.H., 2017. Assessment and Optimization of Thermal and Fluidity Properties of High Strength Concrete via Genetic Algorithm, An international journal of optimization and Control: Theories&Applications (IJOCTA), 7 , 90-97.
  • Tvrdik, J., 2006. Competitive Differential Evolution and Genetic Algorithm in GA-DS Toolbox, Technical Computing Prague, Praha, Humusoft, 99-106.
  • Ülker, E.D., 2017. A PSO/HS Based Algorithm for Optimization Tasks, IEEE Xplore Computing Conference Proceedings 2017, London, 117-120.
  • Ülker, E.D., 2018. An Elitist Approach for Solving the Traveling Salesman Problem using an Animal Migration Optimization Algorithm, Turkish Journal of Electrical Engineering & Computer Sciences, 1, 605-617.
  • Wang, G.G., Gandomi, A.H., Zhao, X. and Chu, H.C.E., 2016. Hybridizing Harmony Search Algorithm with Cuckoo Search for Global Numerical Optimization, Soft Computing, 20, 273-285.
  • Wang, L. and Lingp-Po, L., 2013. An Effective Differential Harmony Search Algorithm for the Solving Non-convex Economic Load Dispatch Problems, International Journal of Electrical Power & Energy Systems, 44, 832-843.
  • Wolpert, D.H. and William, G. M., 1997. No Free Lunch Theorems for Optimization,1997, IEEE Transactions on Evolutionary Computation, 1, 67-87.
There are 19 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ezgi Deniz Ülker 0000-0002-9642-7310

Publication Date November 30, 2018
Submission Date May 28, 2018
Acceptance Date November 30, 2018
Published in Issue Year 2018 CMES 2018

Cite

APA Deniz Ülker, E. (2018). Rastgele Değişkenli Melez DG-HA Algoritması. Gümüşhane Üniversitesi Fen Bilimleri Dergisi119-124. https://doi.org/10.17714/gumusfenbil.427696