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OOPSF: Object Oriented Particle Swarm Framework


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DOI: https://doi.org/10.15866/irecos.v13i2.16867

Abstract


This paper proposes an Object Oriented Particle Swarm Framework (OOPSF) that can be used to apply particle swarm intelligence on different optimization problems. The framework is designed using object oriented paradigm and implemented using Java programming language as an Application Programming Interface (API). Within a single framework, OOPSF attempts to abstract and encapsulate the basic concepts of particle swarm optimization (PSO) along with some modifications that were proposed by different researchers. The proposed API benefits from advantages of object oriented design including abstraction, encapsulation, inheritance and code reuse; finally the paper is concluded with a case study to solve a regression problem using the proposed framework.
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Keywords


Particle Swarm; Application Programming Interface; Java; Modeling; Soft Computing; Object Oriented

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References


L. A. Zadeh, Soft Computing and Fuzzy Logic. IEEE Software, vol. 11 n.6, 1994, pp. 48-56.

W. Gao, Study on genetic algorithm and evolutionary programming. The 2nd IEEE International Conference on Parallel Distributed and Grid Computing (PDGC), Dec 6-8, 2012, Coimbatore, India.

X. Yang, A New Metaheuristic Bat-Inspired Algorithm, In J. R. Gonzalez et al (Eds.), Studies in Computational Intelligence, (Berlin: Springer, 2010, 65-74).

X. Yang, S. Deb, Cuckoo search via Lévy flights. World Congress on Nature & Biologically Inspired Computing, Dec 9-11, 2009, Coimbatore, India

G. Fornarelli, Swarm Intelligence for Electric and Electronic Engineering (IGI Global,2012).

TIOBE Programming Community Index for March 2013 from http://www.tiobe.com/index.php/content/paperinfo/tpci /index.html. 2013.03.028.

M. Weisfeld, The Object-Oriented Thought Process (Addison-Wesley Professional, 2013).

H. Rania, C. Babak, A comparison of particle swarm optimization and the genetic algorithm. Structures, Structural Dynamics, and Materials Conference, April 18-21, 2005, Austin, Texas.

E. Chang, J. Singh, Prediction of Short Term Traffic Variables Using Intelligent Swarm-Based Neural Networks, IEEE Transactions on Control Systems Technology vol. 21, 2013, pp.1942-1945.

D. Rocha, J. Tome, A discrete evolutionary PSO based approach to the multiyear transmission expansion planning problem considering demand uncertainties. International Journal of Electrical Power & Energy Systems, vol. 45 n. 1, 2013, pp.427-442.

R. Bansal, P. Sehgal, Using PSO in a spatial domain based image hiding scheme with distortion tolerance. Computers & Electrical Engineering, 2 Feb 2013, from http://dx.doi.org/10.1016/ j.compeleceng. 2012.12.021

L. Yanbin, G. Youhua, Optimum design for beam-pumping unit based on chaos particle swarm optimization algorithm, Second International Conference on Mechanic Automation and Control Engineering (MACE), July 15-17 ,2011,pp. 59635965

C. Chun-Mei, Comparative study of financial distress prediction via optimized SVM. International Conference on Machine Learning and Cybernetics, Jul 15- 17, 2012, Shaanxi, China.

M. Qian, G. Tan, Z.Ma, H.Zhang. Improved Chaotic Particle Swarm Optimization Algorithm and Its Application in the Design of Robust Controller. International Journal on Information Technology, vol .7 n. 1 (Part A), 2012, pp. 23-30

Q. Dong, J.Lu, Y.Gui. Integrated Production Planning and Scheduling Model and Algorithm for Permutation Flowshop, International Journal on Information Technology, vol .7 n. 1 (Part B), 2012, pp. 374-381

D. Merkle, Swarm Intelligence: Introduction and Applications (Springer,2010)

A. Soroudi, M. Afrasiab, Binary PSO-based dynamic multi-objective model for distributed generation planning under uncertainty. Renewable Power Generation, vol. 6 n. 2, 2012, pp.67-78.

Particle Swarm Optimization, Methods, Taxonomy and Applications, International Journal of Computer Theory and Engineering, vol. 1 n. 4, 2009, pp.1793-8201

D. K. Behera, Cooperative swarm based clustering algorithm based on PSO and k-means to find optimal cluster centroids. National Conference of Computing and Communication Systems (NCCCS),Nov 21-22, 2012, West Bengal, India

Y. Yan, A Multi-swarm Based Hybrid Optimization Algorithm in Dynamic Environments. Second International Conference on Computer Modeling and Simulation, Jan 7-9., 2010, Mumbai, India

L. Cheng-Jian, A self-organizing neural network using hierarchical particle swarm optimization. The International Joint Conference on Neural Networks (IJCNN), June 10-15, 2012, Brisbane ,Australia

L. Yun, H.S. Chung, Adaptive Particle Swarm Optimization. IEEE Transactions on Systems, Man, and Cybernetics, vol.39,n. 6, 2009,pp.1362-1381

K. Deep, P. Chauhan, M. Pant, A new fine grained inertia weight Particle Swarm Optimization. Congress on Information and Communication Technologies, Dec 11-14, 2011, Mimbai, India.
J. Zh, and X. Cai,. A guaranteed convergence dynamic double particle swarm optimizer. Congress on Intelligent Control and Automation, June 15-19 2004, Hangzhou, China

DataMarket, Annual employment. From http://datamarket.com/data/set/22v2/annual-employment-us-1890-to-1970 .2013.02.012.


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