Scale Free Fully Informed Particle Swarm Optimization: Parametric Perspective

The current research work is mainly based on enhancement of a canonical model of the basic Particle Swarm Optimization (PSO) algorithm for achieving maximum optimized results for the real world problems.The existing topological algorithms like Global PSO (GPSO) and Scale-free fully informed PSO (SFIPSO) performs well but it has low success rate and vulnerable to stuck in local optima.In this paper scale free topology is modified with the inclusion of a new inertial weight (ω) (WSFIPSO) and results are compared with GPSO, SFIPSO, and inertial weight version of GPSO (WGPSO). These algorithms are tested on eight benchmark functions and WSFIPSO achieves 100% success rate on all benchmark functions, each SFIPSO and GPSO on two-two benchmark functions while WGPSO fails to achieve 100% success rate on any one of the benchmark function. Solution quality of the proposed approach outperforms on seven benchmark functions while GPSO on one. SFIPSO ranked 2nd on five benchmark functions even with low success rates. These findings clearly shows that topology based PSO greatly impact the solution quality

[1]  Wenhua Han,et al.  Comparison study of several kinds of inertia weights for PSO , 2010, 2010 IEEE International Conference on Progress in Informatics and Computing.

[2]  Zhang Tao,et al.  A new chaotic PSO with dynamic inertia weight for economic dispatch problem , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[3]  Yan Liu,et al.  Skeleton-Network Reconfiguration Based on Topological Characteristics of Scale-Free Networks and Discrete Particle Swarm Optimization , 2007, IEEE Transactions on Power Systems.

[4]  Meng Zhang,et al.  Chaotic co-evolutionary algorithm based on differential evolution and particle swarm optimization , 2009, 2009 IEEE International Conference on Automation and Logistics.

[5]  Yanguang Sun,et al.  Comparison of multiobjective particle swarm optimization and evolutionary algorithms for optimal reactive power dispatch problem , 2014, 2014 IEEE Congress on Evolutionary Computation (CEC).

[6]  Jun Zhang,et al.  Small-world particle swarm optimization with topology adaptation , 2013, GECCO '13.

[7]  Wang Zhi-gang,et al.  A modified particle swarm optimization , 2009 .

[8]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[9]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[10]  A. Barabasi,et al.  Scale-free characteristics of random networks: the topology of the world-wide web , 2000 .

[11]  Azah Mohamed,et al.  A Survey of the State of the Art in Particle Swarm Optimization , 2012 .

[12]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[13]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[14]  Walter E. Beyeler,et al.  The topology of interbank payment flows , 2007 .

[15]  Wen-Bo Du,et al.  Particle Swarm Optimization with Scale-Free Interactions , 2014, PloS one.

[16]  Lian Wen,et al.  Software Engineering and Scale-Free Networks , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Patricia Melin,et al.  An improved Particle Swarm Optimization algorithm applied to Benchmark Functions , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).

[18]  Wang Jiaying,et al.  A modified particle swarm optimization algorithm , 2005 .

[19]  Chenggong Zhang,et al.  Scale-free fully informed particle swarm optimization algorithm , 2011, Inf. Sci..

[20]  Piotr A. Kowalski,et al.  Fully informed swarm optimization algorithms: Basic concepts, variants and experimental evaluation , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[21]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[22]  M.V.C. Rao,et al.  Competitive approaches to PSO algorithms via new acceleration co-efficient variant with mutation operators , 2005, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05).

[23]  Andries Petrus Engelbrecht,et al.  Fully informed particle swarm optimizer: Convergence analysis , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[24]  Worapoj Kreesuradej,et al.  Input Selection Using Binary Particle Swarm Optimization , 2006, 2006 International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA'06).

[25]  Wang Qun,et al.  Short-term load forecasting model based on ridgelet neural network optimized by particle swarm optimization algorithm , 2017, 2017 8th IEEE International Conference on Software Engineering and Service Science (ICSESS).

[26]  V. Plerou,et al.  A theory of power-law distributions in financial market fluctuations , 2003, Nature.