### 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

#### Tam Metin:

PDF (English)#### Referanslar

Ala’raj M, Abbod MF, Classifiers consensus system approach for credit scoring, Knowl-Based Syst. 2016, doi:10.1016/j.knosys.2016.04.013

Al-Hassan W., Fayek MB, and Shaheen SI, “Psosa: An optimized particle swarm technique for solving the urban planning problem”, In Computer Engineering and Systems, The 2006 International Conference on, pages 401–405. IEEE, 2007.

Arasomwan MA, Adewumi AO. On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization. Sci World J. 2013.

Armano G, Farmani MR. Multiobjective clustering analysis using particle swarm optimization, Expert Systems with Applications, 2016;55: 184–193, doi:10.1016/j.eswa.2016.02.009

Arumugam M.S. and Rao MVC., “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, 2006, 2006.

Awad N. H., Ali M. Z., Liang J. J., Qu B. Y. and 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 JC, Singh PK, Saraswat M, Verma A, Jadon SS, Abraham A (2011) Inertia weight strategies in particle swarm optimization. In: Proceedings of third world congress on nature and biologically inspired computing (NaBIC-2011), pp 633–640

Bharti KK, Singh PK. Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering. Appl Soft Comput. 2016;43:20-34.

Çavdar T, PSO tuned ANFIS equalizer based on fuzzy C-means clustering algorithm, ,AEU - International Journal of Electronics and Communications, 2016;70(6): 799–807, doi:10.1016/j.aeue.2016.03.006.

Eberhart R.C. and Shi Y., “Tracking and optimizing dynamic systems with particle swarms”, In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, volume 1, pages 94–100. IEEE.

Feng Y., Teng G.F., Wang A.X., and Yao Y.M., “Chaotic Inertia Weight in Particle Swarm Optimization”, In Innovative Computing, Information and Control, 2007. ICICIC’07. Second International Conference on, page 475. IEEE, 2008.

Gheisari S, Meybodi MR. BNC-PSO: structure learning of Bayesian networks by Particle Swarm Optimization. Inform Sciences. 2016;348:272-89.

Ho, Tin Kam (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8): 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 JK, Rezvanian A, Meybodi MR. An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments. J Exp Theor Artif In. 2016;28(1-2):137-49.

Liang Y, Leung KS. Genetic Algorithm with adaptive elitist-population strategies for multimodal function optimization. Appl Soft Comput. 2011;11(2):2017-34.

Lim WH, Isa NAM. An adaptive two-layer particle swarm optimization with elitist learning strategy. Inform Sciences. 2014;273:49-72.

Maca P, Pech P. The Inertia Weight Updating Strategies in Particle Swarm Optimisation Based on the Beta Distribution. Math Probl Eng. 2015.

Nickabadi A, Ebadzadeh MM, Safabakhsh R. A novel particle swarm optimization algorithm with adaptive inertia weight. Appl Soft Comput. 2011;11(4):3658-70.

Pluhacek M, Senkerik R, Davendra D, Oplatkova ZK, Zelinka I. On the behavior and performance of chaos driven PSO algorithm with inertia weight. Comput Math Appl. 2013;66(2):122-34.

Rokach L., Ensemble-based classifiers, Artif. Intell. Rev. 33 (1–2) (2010) 1–39.

Shi Y. and Eberhart R., A modified particle swarm optimizer, In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on, pages 69–73. IEEE.

Shi Y.H., Eberhart R.C., Fuzzy adaptive particle swarm optimization, Proc. of the IEEE Congress on Evolutionary Computation, Seoul Korea, vol. 1 (2001), pp. 101–106

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., A novel stability-based adaptive inertia weight for particle swarm optimization. Appl Soft Comput. 2016;38:281-95.]

Uymaz SA, Tezel G, Yel E. Artificial algae algorithm (AAA) for nonlinear global optimization. Appl Soft Comput. 2015;31:153-71.

Whitley D, A genetic algorithm tutorial, Statistics and Computing (1994) 4, 65-85.

Xiang Y, Zhou YR, Liu HL. An elitism based multi-objective artificial bee colony algorithm. European Journal of Operational Research. 2015;245(1):168-93.

Xin J., Chen G., and Hai Y., “A Particle Swarm Optimizer with Multistage Linearly-Decreasing Inertia Weight”, In Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on, volume 1, pages 505–508. IEEE, 2009.

Xu GL, Wan SP, Wang F, Dong JY, Zeng YF. Mathematical programming methods for consistency and consensus in group decision making with intuitionistic fuzzy preference relations. Knowl-Based Syst. 2016;98:30-43.

Zang W., Zhang P., Zhou C., Guo L., Comparative study between incremental and ensemble learning on data streams: Case study, J. Big Data 1 (1) (2014) 1–16.

Zhang LM, Tang YG, Hua CC, Guan XP. A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Appl Soft Comput. 2015;28:138-49

#### Madde Ölçümleri

_{Metrics powered by PLOS ALM}

### Refback'ler

- Şu halde refbacks yoktur.

Telif Hakkı (c) 2018 Selçuk Üniversitesi Mühendislik, Bilim ve Teknoloji Dergisi

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

**Tarayan Veri Tabanları**