Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.

[1]  Yanchun Liang,et al.  An Effective PSO and AIS-Based Hybrid Intelligent Algorithm for Job-Shop Scheduling , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[2]  Siti Zaiton Mohd Hashim,et al.  Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..

[3]  P. Venkatesh,et al.  Glowworm swarm optimization algorithm with topsis for solving multiple objective environmental economic dispatch problem , 2014, Appl. Soft Comput..

[4]  Parham Moradi,et al.  Gene selection for microarray data classification using a novel ant colony optimization , 2015, Neurocomputing.

[5]  Basilio Sierra,et al.  Classifier Subset Selection to construct multi-classifiers by means of estimation of distribution algorithms , 2015, Neurocomputing.

[6]  Chao Zhang,et al.  Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode , 2018, Mathematical Problems in Engineering.

[7]  Manjit Kaur,et al.  Multi-objective differential evolution based random forest for e-health applications , 2019, Modern Physics Letters B.

[8]  Dilbag Singh,et al.  Multi-objective particle swarm optimization-based adaptive neuro-fuzzy inference system for benzene monitoring , 2017, Neural Computing and Applications.

[9]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[10]  Xia Li,et al.  A novel particle swarm optimizer hybridized with extremal optimization , 2010, Appl. Soft Comput..

[11]  Aizhu Zhang,et al.  A Hybrid Genetic Algorithm and Gravitational Search Algorithm for Image Segmentation Using Multilevel Thresholding , 2013, IbPRIA.

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

[13]  Francisco Herrera,et al.  A study on the use of non-parametric tests for analyzing the evolutionary algorithms’ behaviour: a case study on the CEC’2005 Special Session on Real Parameter Optimization , 2009, J. Heuristics.

[14]  Minghao Yin,et al.  Hybrid differential evolution and gravitation search algorithm for unconstrained optimization , 2011 .

[15]  Manjit Kaur,et al.  An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-D chaotic maps , 2019, Journal of Ambient Intelligence and Humanized Computing.

[16]  A. Shunmugalatha,et al.  Optimum cost of generation for maximum loadability limit of power system using hybrid particle swarm optimization , 2008 .

[17]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[18]  Dantong Ouyang,et al.  A novel hybrid differential evolution and particle swarm optimization algorithm for unconstrained optimization , 2009, Oper. Res. Lett..

[19]  Zheng Tang,et al.  Hybrid Gravitational Search Algorithm with Random-key Encoding Scheme Combined with Simulated Annealing , 2011 .

[20]  Nor Ashidi Mat Isa,et al.  Teaching and peer-learning particle swarm optimization , 2014, Appl. Soft Comput..

[21]  S. Mirjalili,et al.  A new hybrid PSOGSA algorithm for function optimization , 2010, 2010 International Conference on Computer and Information Application.

[22]  S. S. Thakur,et al.  Optimal static state estimation using improved particle swarm optimization and gravitational search algorithm , 2013 .

[23]  Yu Xue,et al.  An Artificial Immune System Algorithm with Social Learning and Its Application in Industrial PID Controller Design , 2017 .

[24]  Robert C. Green,et al.  Training neural networks using Central Force Optimization and Particle Swarm Optimization: Insights and comparisons , 2012, Expert Syst. Appl..

[25]  Yan Wang,et al.  A new design method for adaptive IIR system identification using hybrid particle swarm optimization and gravitational search algorithm , 2015 .

[26]  Zhicheng Ji,et al.  A novel hybrid particle swarm optimization and gravitational search algorithm for solving economic emission load dispatch problems with various practical constraints , 2014 .

[27]  Germano Lambert-Torres,et al.  Hybrid Evolutionary Algorithm Based on PSO and GA Mutation , 2006, 2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06).

[28]  Dilbag Singh,et al.  Improved Particle Swarm Optimization Based Adaptive Neuro-Fuzzy Inference System for Benzene Detection , 2018 .

[29]  Yong Wang,et al.  Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization , 2010, Appl. Soft Comput..

[30]  Shanhe Jiang,et al.  An improved hybrid particle swarm optimization with dependent random coefficients for global optimization , 2018, 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC).

[31]  Zongyan Li,et al.  An Improved Global Harmony Search Algorithm for the Identification of Nonlinear Discrete-Time Systems Based on Volterra Filter Modeling , 2016 .

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

[33]  Manjit Kaur,et al.  Parallel strength Pareto evolutionary algorithm-II based image encryption , 2020, IET Image Process..

[34]  Lei Guo,et al.  Parameter identification and sensitivity analysis of solar cell models with cat swarm optimization algorithm , 2016 .

[35]  Ye Tian,et al.  Chaotic S-Box: Intertwining Logistic Map and Bacterial Foraging Optimization , 2017 .

[36]  Xiao-Feng Xie,et al.  DEPSO: hybrid particle swarm with differential evolution operator , 2003, SMC'03 Conference Proceedings. 2003 IEEE International Conference on Systems, Man and Cybernetics. Conference Theme - System Security and Assurance (Cat. No.03CH37483).

[37]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[38]  Erwie Zahara,et al.  A hybrid genetic algorithm and particle swarm optimization for multimodal functions , 2008, Appl. Soft Comput..

[39]  Fazli Wahid,et al.  An Efficient Approach for Energy Consumption Optimization and Management in Residential Building Using Artificial Bee Colony and Fuzzy Logic , 2016 .

[40]  Pascal Bouvry,et al.  Particle swarm optimization: Hybridization perspectives and experimental illustrations , 2011, Appl. Math. Comput..

[41]  Andrew Lim,et al.  Example-based learning particle swarm optimization for continuous optimization , 2012, Information Sciences.

[42]  Zhijian Wu,et al.  Enhancing particle swarm optimization using generalized opposition-based learning , 2011, Inf. Sci..

[43]  Hossein Nezamabadi-pour,et al.  Facing the classification of binary problems with a GSA-SVM hybrid system , 2013, Math. Comput. Model..