Roulette Electromagnetic Field Optimization (R-EFO) Algorithm

Year 2020, Volume: 8 Issue: 1, 69 - 80, 31.01.2020

Hamdi Tolga Kahraman

https://doi.org/10.29130/dubited.589259

Cited By: 3

Abstract

Two key elements that influence the local search performance of meta-heuristic optimization algorithms are selection methods and search operators. In this article, the effect of roulette wheel, which is a probabilistic selection method, on local search performance of EFO (electromagnetic field optimization) algorithm, which is a current meta-heuristic search technique, is researched. In the EFO, the group of solution candidates are divided into positive, neutral and negative fields depending on their fitness values. The solution candidates selected from these three fields guide the search process. In this process, solution candidates are determined by greedy and random selection methods. In this study, the roulette technique is used for selection of solution candidates from negative field. In the experimental studies, the continuous valued and unconstrained problems CEC17 benchmark suite are used to test the performance of proposed Roulette- Electromagnetic Field Optimization (R-EFO). The results of the experimental study are statistically analyzed by Wilcoxon runk sum test used in comparison with standard EFO algorithm. According to the analysis results, proposed R-EFO algorithm with roulette selection method significantly improves the search performance of the EFO algorithm.

Keywords

Electromagnetic field optimization, Roulette selection, CEC17 benchmark suite, Meta-heuristic optimization

Rulet Elektromanyetik Alan Optimizasyon (R-EFO) Algoritması

Abstract

Meta-sezgisel
optimizasyon algoritmalarının yerel arama performansları üzerinde etkili olan
iki temel öğe seçim yöntemleri ve arama operatörleridir. Bu makale çalışmasında
olasılıksal bir seçim yöntemi olan rulet tekerleğinin güncel bir meta-sezgisel
arama tekniği olan elektromanyetik alan optimizasyon (electromagnetic field
optimization, EFO) algoritmasının yerel arama performansı üzerindeki etkisi
araştırılmaktadır. Elektromanyetik optimizasyon algoritmasında çözüm adayları
topluluğu uygunluk değerlerine bağlı olarak pozitif, nötr ve negatif alanlara
ayrılmaktadır. Bu üç alandan seçilen çözüm adayları ise arama sürecine
rehberlik etmektedirler. Bu süreçte çözüm adayları açgözlü ve rastgele seçim
yöntemleri ile belirlenmektedir. Bu makale çalışmasında ise negatif alandan
çözüm adaylarının seçimi için rulet tekniği kullanılmaktadır. Deneysel
çalışmalarda literatürdeki en güncel sürekli değer problemleri olan CEC17 test
seti kullanılmıştır. Deneysel çalışma sonuçları istatistiksel olarak ikili
karşılaştırmalarda kullanılan wilcoxon runk sum test ile analiz edilmiştir.
Analiz sonuçlarına göre rulet seçim yöntemi EFO algoritmasının arama
performansını kayda değer şekilde artırmaktadır.

Keywords

Elektromanyetik alan optimizasyon algoritması, rulet seçim yöntemi, CEC17 karşılaştırma problemleri, meta-sezgisel optimizasyon

References

• [1] Abedinpourshotorban, H., Shamsuddin, S. M., Beheshti, Z., & Jawawi, D. N. “Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm”, Swarm and Evolutionary Computation, no. 26, pp. 8-22, 2016.
• [2] Al-Bahrani, L. T., & Patra, J. C. "A novel orthogonal PSO algorithm based on orthogonal diagonalization", Swarm and Evolutionary Computation, no. 40, pp. 1-23,2018
• [3] Ali, A. F., Tawhid, M. A. 2017. "A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems", Ain Shams Engineering Journal, 8, (2), 191-206.
• [4] Awad, N. H., Ali, M. Z., Mallipeddi, R., & Suganthan, P. N. 2018. "An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization". Information Sciences, 451, 326-347.
• [5] Aydilek, İ. B. 2018. "A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems", Applied Soft Computing, 66, 232-249.
• [6] Caraveo, C., Valdez, F., & Castillo, O. 2018. "A new optimization meta-heuristic algorithm based on self-defense mechanism of the plants with three reproduction operators", Soft Computing, 1-14.
• [7] Chen, K., Zhou, F., Yin, L., Wang, S., Wang, Y., Wan, F. 2018. "A hybrid particle swarm optimizer with sine cosine acceleration coefficients", Information Sciences, 422, 218-241.
• [8] Cheng, M. Y., & Prayogo, D. 2014. "Symbiotic organisms search: a new metaheuristic optimization algorithm". Computers & Structures, 139, 98-112.
• [9] Cheraghalipour, A., Hajiaghaei-Keshteli, M., & Paydar, M. M. 2018. "Tree Growth Algorithm (TGA): A novel approach for solving optimization problems", Engineering Applications of Artificial Intelligence, 72, 393-414.
• [10] Holland, J.H., 1975. "Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence". Q. Rev. Biol. 1, 211. http://dx.doi.org/10.1086/418447.
• [11] Hooker, J. N. 1995. "Testing heuristics: We have it all wrong". Journal of heuristics, 1, (1), 33-42.
• [12] Kahraman, H. T., Aras, S., Guvenc, U., & Sonmez, Y. 2017. "Exploring the effect of distribution methods on meta-heuristic searching process". Iternational Conference on Computer Science and Engineering (UBMK), Antalya, 5-8 Ekim 2017, IEEE, 371-376.
• [13] Kahraman, H. T., Aras, S., Gedikli, E. 2018. "Meta-Sezgisel Optimizasyon Çalışmalarında Benchmark Problemlerinde Karşılaşılan Standartsızlıklar ve Çözüm Önerileri", IV. INES Internatıonal Academic Research Congress (INES-2018), Antalya, 30 Ekim-3 Kasım 2018.
• [14] Kahraman, H. T., Aras, S., Gedikli, E., 2018. "Meta-Sezgisel Algoritmaların Deneysel Çalışmalarındaki Standartsızlıklar ve Çözüm Önerileri", , IV. INES Internatıonal Academic Research Congress (INES - 2018), Antalya, 30 Ekim-3 Kasım 2018.
• [15] Cui, L., Li, G., Zhu, Z., Lin, Q., Wong, K. C., Chen, J., Lu, J. 2018. "Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism", Information Sciences, 422, 122-143.
• [16] Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., & Suganthan, P. N. (2016). “Problem definitions and evaluation criteria for the CEC 2017 special session and competition on single objective real-parameter numerical optimization. Tech. Rep.”, (son erişim tarihi: 1.07.2019).
• [17] 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", Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou, China and Nanyang Technological University, Singapore.
• [18] Le, D. T., Bui, D. K., Ngo, T. D., Nguyen, Q. H., & Nguyen-Xuan, H. (2019). “A novel hybrid method combining electromagnetism-like mechanism and firefly algorithms for constrained design optimization of discrete truss structures”, Computers & Structures, 212, 20-42.
• [19] Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). “Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems”, Advances in Engineering Software, 114, 163-191.
• [20] Wang, L., Yang, B., & Orchard, J. (2016). “Particle swarm optimization using dynamic tournament topology. Applied Soft Computing”, 48, 584-596.
• [21] Qin, Q., Cheng, S., Zhang, Q., Li, L., & Shi, Y. (2016). “Particle swarm optimization with interswarm interactive learning strategy”, IEEE transactions on cybernetics, 46(10), 2238-2251.
• [22] Al-Bahrani, L. T., & Patra, J. C. (2018). “A novel orthogonal PSO algorithm based on orthogonal diagonalization”, Swarm and Evolutionary Computation", 40, 1-23.
• [23] Lin, Q., Zhu, M., Li, G., Wang, W., Cui, L., Chen, J., & Lu, J. 2018. "A novel artificial bee colony algorithm with local and global information interaction", Applied Soft Computing, 62, 702-735.
• [24] Long, W., Jiao, J., Liang, X., Tang, M. 2018. "An exploration-enhanced grey wolf optimizer to solve high-dimensional numerical optimization", Engineering Applications of Artificial Intelligence, 68, 63-80.
• [25] Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. 2017. "Salp Swarm Algorithm: A bio-inspired optimizer for engineering design problems". Advances in Engineering Software, 114, 163-191.
• [26] Salgotra, R., Singh, U., & Saha, S. 2018. "New cuckoo search algorithms with enhanced exploration and exploitation properties", Expert Systems with Applications, 95, 384-420.
• [27] Sun, G., Ma, P., Ren, J., Zhang, A., & Jia, X. 2018. "A stability constrained adaptive alpha for gravitational search algorithm". Knowledge-Based Systems, 139, 200-213.
• [28] Torabi, S., & Safi-Esfahani, F. 2018. "Improved Raven Roosting Optimization algorithm (IRRO)", Swarm and Evolutionary Computation, 40, 144-154.
• [29] Veçek, N., Mernik, M., & ?repinšek, M. 2014. "A chess rating system for evolutionary algorithms: A new method for the comparison and ranking of evolutionary algorithms". Information Sciences, 277, 656-679.
• [30] Wu, G., Shen, X., Li, H., Chen, H., Lin, A., Suganthan, P. N. 2018. "Ensemble of differential evolution variants", Information Sciences, 423, 172-186.