A diversity-guided hybrid particle swarm optimization based on gradient search

Abstract As an evolutionary computing technique, particle swarm optimization (PSO) has good global search ability, but it is easy to make the swarm lose its diversity and lead to premature convergence. In this paper, a diversity-guided hybrid PSO based on gradient search is proposed to improve the search ability of the swarm. The adaptive PSO is first used to search the solution till the swarm loses its diversity. Then, the search process turns to a new PSO (DGPSOGS), and the particles update their velocities with their gradient directions as well as repel each other to improve the swarm diversity. Depending on the diversity value of the swarm, the proposed hybrid method switches alternately between two PSOs. The hybrid algorithm adaptively searches local minima with random search realized by adaptive PSO and performs global search with semi-deterministic search realized by DGPSOGS, and so its search ability is improved. The experimental results show that the proposed hybrid algorithm has better convergence performance with better diversity compared to some classical PSOs.

[1]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..

[2]  De-Shuang Huang,et al.  An improved approximation approach incorporating particle swarm optimization and a priori information into neural networks , 2010, Neural Computing and Applications.

[3]  Fei Han,et al.  A new approach for function approximation incorporating adaptive particle swarm optimization and a priori information , 2008, Appl. Math. Comput..

[4]  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.

[5]  De-Shuang Huang,et al.  Finding roots of arbitrary high order polynomials based on neural network recursive partitioning method , 2007, Science in China Series F: Information Sciences.

[6]  De-Shuang Huang.  The local minima-free condition of feedforward neural networks for outer-supervised learning , 1998, IEEE Trans. Syst. Man Cybern. Part B.

[7]  De-Shuang Huang,et al.  A Neural Root Finder of Polynomials Based on Root Moments , 2004, Neural Computation.

[8]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[9]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[10]  M. Noel,et al.  A new gradient based particle swarm optimization algorithm for accurate computation of global minimum , 2012, Appl. Soft Comput..

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  De-Shuang Huang,et al.  A constructive approach for finding arbitrary roots of polynomials by neural networks , 2004, IEEE Transactions on Neural Networks.

[13]  Riccardo Poli,et al.  New ideas in optimization , 1999 .

[14]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[15]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  M. Clerc,et al.  The swarm and the queen: towards a deterministic and adaptive particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[17]  Jack L. Meador,et al.  Encoding a priori information in feedforward networks , 1991, Neural Networks.

[18]  Ajith Abraham,et al.  A novel global optimization technique for high dimensional functions , 2009 .

[19]  Jacques Riget,et al.  A Diversity-Guided Particle Swarm Optimizer - the ARPSO , 2002 .

[20]  J R Saunders,et al.  A particle swarm optimizer with passive congregation. , 2004, Bio Systems.

[21]  Fei Han,et al.  Improved Particle Swarm Optimization Combined with Backpropagation for Feedforward Neural Networks , 2013, Int. J. Intell. Syst..

[22]  Anis Sakly,et al.  Fuzzy adaptive particle swarm optimization basec maximum power point tracking , 2016, 2016 17th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA).