Investigation of Deterministic Particle Swarm Optimization with Periodic Function
Abstract
In this study, we propose the deterministic particle swarm optimization introducing periodic function (DPSOP). The particle swarm optimization (PSO) is one of evolutional algorithm and is constructed by many agents. The agents have a position and velocity that are updated according to the global best and the local best. The amount of agent moving is generally decided randomly. In the proposed DPSOP, the amount of agent moving is decided sine and cosine wave. Each agent is obtained different phase, and the amount of agent moving to the global best and the local best is changed according to cosine and sine wave, respectively. The moving for global best is out of phase with the local best by pi/2. Depending on the phase difference, the agent's movement will be divided into a time when it is maximally close to the global best and a time when it is maximally close to the local best. Thereby, the DPSOP is able to perform efficient solution search. We confirm that the performance of the proposed DPSOP by using searching solution of five benchmark functions.
References
D. Whitley,“A Genetic Algorithm Tutorial,” Springer Statistics and Computing:, vol.4, pp.65-–85, 1994, https://doi.org/10.1007/BF00175354.
X.S. Yang and X. He,“ Firefly algorithm: recent advances and applications,” International Journal of Swarm Intelligence, vol.1, no.1, pp.36–50, 2013
J.H. Holland,“Genetic Algorithm,” Scientific American, vol.267, no.1, pp.67-73, 1992.
D. Karaboga and B. Basturk,“ On the Performance of Artificial Bee Colony (ABC) Algorithm,” Applied Soft Computing, vol.8, no.1, pp.687-697, 2008, https://doi.org/10.1016/j.asoc.2007.05.007.
J. Kennedy and R. Eberhart,“ Particle Swarm Optimization, ”Proc. Of International Conference of Neural Networks, 1995, https://doi.org/10.1109/ICNN.1995.488968.
H. Matsushita, Y. Nishio, and C.K. Tse,“ Network-Structured Particle Swarm Optimizer that Considers Neighborhood Distance and Behaviors,” Journal of Signal Processing, vol.18, no.6, pp.291-302, 2014, https://doi.org/10.2299/jsp.18.291.
R.A. Krohling, and L.D.S Coelho “ Conevolutionaly Particle Swarm Optimization Using Gaussian Distribution for Solving Constrained Optimization Problems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol.36, no.6, pp.1407-1416, 2006.
L.D.S Coelho and C.S. Lee,“ Solving Economic Load Dispatch Problems in Power Systems Using Chaotic and Gaussian Particle Swarm Optimization Approaches,” International Journal of Electrical Powar and Energy Systems, vol.30, no.5, pp.297-307, 2008, https://doi.org/10.1016/j.ijepes.2007.08.001.
D. Sedighizadeh and E. Masehian,“Particle Swarm Optimization Methods, Taxonomy and Applications,” International Journal of Computer Theory and Engineering, vol.1, no.5, pp.486-502, 2009.
D. Tian and Z. Shi, “ MPSO:Modified Particle Swarm Optimization and its Applications,” Swarm and Evolutionary Computation, vol.41, pp.49-68, 2018, https://doi.org/10.1016/j.swevo.2018.01.011.
T.A.A. Victoire and A.E. Jeyakumar,“ Deterministically Guided PSO for Dynamic Dispatch Considering Value-point Effect,” Journal of Electric Power Systems Research, vol.73, no.3, pp.313-322, 2005.
K. Jin’no, ”A Novel Deterministic Particle Swarm Optimization System,” Journal of Signal Processing, vol.13, no.6, 2009.
T. Tsujimoto, T. Shindo, and K. Jin’no, ”The Neighborhood of Canonical Deterministic PSO,” Proceedings of IEEE Congress of Evolutionary Computation, pp.1811-1817, 2011.
A. Pinto, D. Peri, and E.F. Campana,“ A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition,” IEEE Transactions on Industrial Elections, vol.60, no.8, pp.3195-3206, 2013.
K. Ishaque and Z. Salam, “ Multiobjective Optimization of a Containership Using Deterministic Particle Swarm Optimization,” Journal of Ship Research, vol.51, no.3, pp.217-228, 2007, https://doi.org/10.5957/jsr.2007.51.3.217.
C.W. Cleghorn and A.P. Engelbrecht,“ A Generalized Theoretical Deterministic Particle Swarm Model,” Springer Swarm Intelligence, vol.8, pp.35-59, 2014.
M. Styblinski and T. Tang, “ Experiments in NonConvex Optimization: Stochastic Approximation with Function Smoothing and Simulated Annealing,” Neural Networks, vol.85, pp.245-276, 1990, https://doi.org/10.1016/0893-6080(90)90029-K.
Y. Shi and R.C. Eberhart,“ Empirical Study of Particle Swarm Optimization,” Proc. of the 1999 Congress on Evolutionary Computation, pp.1945-1950, 1999.
Q. Bai,“ Analysis of Particle Swarm Optimization Algorithm,” Computer and Information Science, vol.3, no.1, pp.180-184, 2010.
