On the Particle Swarm Optimization Improvement Using Time Delay Auto Synchronization

In this article, a novel approach on how to improve the swarm algorithms is introduced. It is based on the discrete iterative system control, where we look at the individual, or particle, as a system which needs to be controlled to the desired state. The control is presented on the particle swarm optimization algorithm, and the time delay auto-synchronization is used for the control. The modification of the particle swarm optimization algorithm using the control is introduced in this work, and the proposed improvement is tested on the CEC benchmark functions.

[1]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[2]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[3]  Ajith Abraham,et al.  Inertia Weight strategies in Particle Swarm Optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[4]  R. Hilborn.  Sea gulls, butterflies, and grasshoppers: A brief history of the butterfly effect in nonlinear dynamics , 2004 .

[5]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[6]  Chun Lu,et al.  An improved GA and a novel PSO-GA-based hybrid algorithm , 2005, Inf. Process. Lett..

[7]  Yong Feng,et al.  Chaotic Inertia Weight in Particle Swarm Optimization , 2007, Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007).

[8]  Zheng Li,et al.  Expert Systems With Applications , 2022 .

[9]  Andries Petrus Engelbrecht,et al.  Using neighbourhoods with the guaranteed convergence PSO , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[10]  Zhongzhi Shi,et al.  MPSO: Modified particle swarm optimization and its applications , 2018, Swarm Evol. Comput..

[11]  Yuhui Shi,et al.  Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[12]  Alireza Alfi,et al.  PSO with Adaptive Mutation and Inertia Weight and Its Application in Parameter Estimation of Dynamic Systems , 2011 .

[13]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[14]  Veysel Gazi,et al.  Particle swarm optimization with dynamic neighborhood topology: Three neighborhood strategies and preliminary results , 2008, 2008 IEEE Swarm Intelligence Symposium.

[15]  Andrea E. Olsson.  Particle Swarm Optimization: Theory, Techniques and Applications , 2010 .

[16]  Ke-zhe Chen,et al.  Chaotic particle swarm optimization algorithm based on adaptive inertia weight , 2014, The 26th Chinese Control and Decision Conference (2014 CCDC).

[17]  Michel Gendreau,et al.  Handbook of Metaheuristics , 2010 .

[18]  Lawrence. Davis,et al.  Handbook Of Genetic Algorithms , 1990 .

[19]  El-Ghazali Talbi,et al.  Metaheuristics - From Design to Implementation , 2009 .

[20]  Mohammed Chadli,et al.  On the Particle Swarm Optimization Control Using Analytic Programming and Self Organizing Migrating Algorithm , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

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

[22]  Ivan Zelinka,et al.  Self-Organizing Migrating Algorithm , 2016 .

[23]  Mohammad Mehdi Ebadzadeh,et al.  A novel particle swarm optimization algorithm with adaptive inertia weight , 2011, Appl. Soft Comput..

[24]  Ponnuthurai Nagaratnam Suganthan,et al.  Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization , 2014 .

[25]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[26]  Kestutis Pyragas.  Continuous control of chaos by self-controlling feedback , 1992 .

[27]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[28]  Xinbo Huang,et al.  Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[29]  Reza Firsandaya Malik,et al.  New particle swarm optimizer with sigmoid increasing inertia weight , 2007 .