Parçacık Sürü Optimizasyon Algoritmasında Bir Topluluk Atalet Ağırlığı Hesaplama Stratejisi

Yıl 2018, Cilt: 6 Sayı: 4, 544 - 558, 01.12.2018

İbrahim Berkan Aydilek

https://doi.org/10.15317/Scitech.2018.151

Öz

Parçacık sürüsü optimizasyonunun nihai başarısı, önceki parçacıkların hız değerlerine bağlıdır. Hız,

atalet ağırlık katsayısı ile çarpılır ve parçacık sürüsü optimizasyonunun arama yeteneği üzerinde önemli

bir etkiye sahiptir. Bu katsayıyı hesaplamak için yapılan önceki çalışmalara bakıldığında atalet ağırlık

katsayısının çeşitli şekillerde ele alındığı görülmektedir. Bu makalede; diğer sabit, rasgele, doğrusal

azalan, küresel yerel en iyi, benzetimli tavlama ve kaotik atalet ağırlığı hesaplama yöntemlerini

kullanılan yeni bir topluluk atalet ağırlığı hesaplama stratejisi önerilmiştir. Önerilen yöntemde, diğer

yöntemlerin sonuçları uygun bir şekilde birleştirilerek nihai çıktı kararı üretmek için kullanılmaktadır.

Deneysel testlerde, bilinen 30 optimizasyon kıyaslama test problemi kullanılmaktadır. Önerilen topluluk

stratejisi istatistiksel testlerle kanıtlanmış ve tüm optimizasyon kıyaslama test problemlerinde başarılı

sonuçlar vermiştir.

Anahtar Kelimeler

Atalet ağırlığı, Parçacık sürü optimizasyonu

AN ENSEMBLE INERTIA WEIGHT CALCULATION STRATEGY IN PARTICLE SWARM OPTIMIZATION ALGORITHM

Öz

The ultimate success of particle swarm optimization depends on the velocity values of

previous particles. Velocity is multiplied with inertia weight coefficient, and has a significant effect on

search capability of the particle swarm optimization. When looking at previous studies that are carried

out to calculate this coefficient, it is seen that inertia weight coefficient has been handled in several ways.

In this article; a new ensemble inertia weight calculation strategy is proposed that uses other constant,

random, linear decreasing, global local best, simulated annealing and chaotic inertia weight calculation

methods. Other methods results are combined and used to make a final output decision in a proper way.

In experimental tests, 30 common optimization benchmark test problems are used. Proposed ensemble

strategy is proven by statistical tests and gives successful results in all optimization benchmark test

problems.

Anahtar Kelimeler

Inertia weight, Particle swarm optimization

Kaynakça

• Ala’raj, M., Abbod, M.F., 2016, “Classifiers Consensus System Approach for Credit Scoring”, Knowledge-Based Systems, Vol. 104, pp. 89-105, doi:10.1016/j.knosys.2016.04.013
• Al-Hassan, W., Fayek, M.B., Shaheen, S.I., “Psosa: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem”, In Computer Engineering and Systems, The 2006 International Conference on, Cairo, Egypt, pp. 401–405 5-7 Nov. 2006, IEEE, 2007.
• Arasomwan, M.A., Adewumi, A.O., 2013, “On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization”, The Scientific World Journal. 2013.
• Armano, G., Farmani, M.R., 2016, “Multiobjective Clustering Analysis Using Particle Swarm Optimization”, Expert Systems with Applications, Vol. 55, pp. 184–193, doi:10.1016/j.eswa.2016.02.009
• Arumugam, M.S., Rao, MVC., 2006, “On the Performance of the Particle Swarm Optimization Algorithm with Various Inertia Weight Variants for Computing Optimal Control of a Class of Hybrid Systems”, Discrete Dynamics in Nature and Society.
• Awad, N. H., Ali, M. Z., Liang, J. J., Qu, B. Y., Suganthan P. N., Problem Definitions and Evaluation Criteria for the CEC 2017 Special Session and Competition on Single Objective Bound Constrained Real-Parameter Numerical Optimization, Technical Report, Nanyang Technological University,Singapore, November 2016.
• Bansal, J.C., Singh, P.K., Saraswat, M., Verma, A., Jadon, S.S., Abraham, A., 2011, “Inertia Weight Strategies in Particle Swarm Optimization”, In: Proceedings of Third World Congress on Nature and Biologically Inspired Computing (NaBIC-2011), Salamanca, Spain, pp 633–640, 19-21 October. 2011.
• Bharti, K.K., Singh, P.K., 2016, “Opposition Chaotic Fitness Mutation Based Adaptive Inertia Weight BPSO for Feature Selection in Text Clustering”, Applied Soft Computing, Vol. 43, pp. 20-34.
• Çavdar, T., 2016, “PSO Tuned ANFIS Equalizer Based on Fuzzy C-means Clustering Algorithm”, AEU - International Journal of Electronics and Communications, Vol. 70(6), pp. 799–807, doi:10.1016/j.aeue.2016.03.006.
• Eberhart, R.C., Shi, Y., “Tracking and Optimizing Dynamic Systems with Particle Swarms”, In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, Seoul, South Korea, Vol. 1, pp. 94–100, 27-30 May 2001.
• Feng, Y., Teng, G.F., Wang, A.X., Yao, Y.M., “Chaotic Inertia Weight in Particle Swarm Optimization”, In Innovative Computing, Information and Control, Kumamoto, Japan, 2007. ICICIC’07. Second International Conference on, page 475, 5-7 September 2007. IEEE, 2008.
• Gheisari, S., Meybodi, M.R., 2016, “BNC-PSO: Structure Learning of Bayesian Networks by Particle Swarm Optimization”, Inform Sciences, Vol. 348, pp. 272-89.
• Ho, Tin Kam, 1998, “The Random Subspace Method for Constructing Decision Forests”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20 (8), pp. 832–844. doi:10.1109/34.709601
• Kennedy, J., Eberhart, R., “Particle Swarm Optimization”, Proc. IEEE Int. Conf. Neural Netw., 4 (1995), pp. 1942–1948.
• Kordestani, J.K., Rezvanian, A., Meybodi, M.R., 2016, “An Efficient Oscillating Inertia Weight of Particle Swarm Optimisation for Tracking Optima in Dynamic Environments”, Journal of Experimental & Theoretical Artificial Intelligence, 2016, Vol. 28(1-2), pp.137-49.
• Liang, Y., Leung, K.S, 2011, “Genetic Algorithm with Adaptive Elitist-Population Strategies for Multimodal Function Optimization”, Applied Soft Computing, Vol. 11(2), pp. 2017-34.
• Lim, W.H., Isa, NAM., 2014, “An Adaptive two-layer Particle Swarm Optimization with Elitist Learning Strategy”, Information Sciences, Vol. 273, pp. 49-72.
• Maca, P., Pech, P., 2015, “The Inertia Weight Updating Strategies in Particle Swarm Optimisation Based on the Beta Distribution”, Mathematical Problems in Engineering.
• Nickabadi, A., Ebadzadeh, M.M., Safabakhsh, R., 2011, “A Novel Particle Swarm Optimization Algorithm with Adaptive Inertia Weight” Applied Soft Computing, Vol. 11(4), pp.3658-70.
• Pluhacek, M., Senkerik, R., Davendra, D., Oplatkova, Z.K., Zelinka, I., 2013, “On the Behavior and Performance of Chaos Driven PSO Algorithm with Inertia Weight”, Computers & Mathematics with Applications, Vol. 66(2), pp.122-34.
• Rokach, L., 2010, “Ensemble-based Classifiers”, Artificial Intelligence Review, Vol. 33 (1–2), pp.1–39.
• Shi, Y., Eberhart, R., “A Modified Particle Swarm Optimizer”, In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, Anchorage, AK, USA, pp. 69–73, 4-9 May 1998.
• Shi, Y.H., Eberhart, R.C., “Fuzzy Adaptive Particle Swarm Optimization”, Proc. of the IEEE Congress on Evolutionary Computation, Seoul Korea, Vol. 1, pp. 101–106, 27-30 May 2001.
• Surjanovic, S., Bingham, D., 2013, Virtual Library of Simulation Experiments: Test Functions and Datasets, Retrieved May 13, 2016, from http://www.sfu.ca/~ssurjano.
• Taherkhani, M., Safabakhsh, R., 2016, “A Novel Stability-Based Adaptive Inertia Weight for Particle Swarm Optimization”, Applied Soft Computing, Vol. 38, pp. 281-95.
• Uymaz, S.A., Tezel, G., Yel, E., 2015, “Artificial Algae Algorithm (AAA) for Nonlinear Global Optimization”, Applied Soft Computing, Vol. 31, pp. 153-71.
• Whitley, D., 1994, “A Genetic Algorithm Tutorial”, Statistics and Computing, Vol. 4, pp. 65-85.
• Xiang, Y., Zhou, Y.R., Liu, H.L., 2015, “An Elitism Based Multi-Objective Artificial Bee Colony Algorithm”, European Journal of Operational Research, Vol. 245(1), pp. 168-93.
• Xin, J., Chen, G., Hai, Y., “A Particle Swarm Optimizer with Multistage Linearly-Decreasing Inertia Weight”, In Computational Sciences and Optimization, 2009, CSO 2009, International Joint Conference on, Sanya, Hainan, China, Vol. 1, pp. 505–508, 24-26 April 2009.
• Xu, G.L., Wan, S.P., Wang, F., Dong, J.Y., Zeng, Y.F., 2016, “Mathematical Programming Methods for Consistency and Consensus in Group Decision Making with Intuitionistic Fuzzy Preference Relations”, Knowledge-Based Systems, Vol. 98, pp.30-43.
• Zang, W., Zhang, P., Zhou, C., Guo, L., 2014, “Comparative Study Between Incremental and Ensemble Learning on Data Streams: Case Study”, Journal of Big Data, Vol. 1 (1), pp.1–16.
• Zhang, L.M., Tang. Y.G., Hua, C.C., Guan, X.P., 2015, “A New Particle Swarm Optimization Algorithm with Adaptive Inertia Weight based on Bayesian Techniques”, Applied Soft Computing, Vol. 28, pp. 138-49.