Improved global-best-guided particle swarm optimization with learning operation for global optimization problems

Display Omitted A new population partitioning strategy is employed to PSO algorithm.In the current population, global neighborhood exploration strategy is presented to enhance the global exploration capability.A local learning mechanism is used to improve local exploitation ability in historical best population.Stochastic learning and opposition based learning operations are employed to accelerate convergence speed and improve optimization accuracy in global best population.IGPSO performs better for engineering design optimization problems. In this paper, an improved global-best-guided particle swarm optimization with learning operation (IGPSO) is proposed for solving global optimization problems. The particle population is divided into current population, historical best population and global best population, and each population is assigned a corresponding searching strategy. For the current population, the global neighborhood exploration strategy is employed to enhance the global exploration capability. A local learning mechanism is used to improve local exploitation ability in the historical best population. Furthermore, stochastic learning and opposition based learning operations are employed to the global best population for accelerating convergence speed and improving optimization accuracy. The effects of the relevant parameters on the performance of IGPSO are assessed. Numerical experiments on some well-known benchmark test functions reveal that IGPSO algorithm outperforms other state-of-the-art intelligent algorithms in terms of accuracy, convergence speed, and nonparametric statistical significance. Moreover, IGPSO performs better for engineering design optimization problems.

[1]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[2]  Carlos A. Coello Coello,et al.  Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer , 2008, Informatica.

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

[4]  Adil Baykasoglu,et al.  Design optimization with chaos embedded great deluge algorithm , 2012, Appl. Soft Comput..

[5]  Ali R. Yildiz,et al.  Comparison of evolutionary-based optimization algorithms for structural design optimization , 2013, Eng. Appl. Artif. Intell..

[6]  Huantong Geng,et al.  A self-guided Particle Swarm Optimization with Independent Dynamic Inertia Weights Setting on Each Particle , 2013 .

[7]  Yang Tang,et al.  Feedback learning particle swarm optimization , 2011, Appl. Soft Comput..

[8]  Yu Wang,et al.  Self-adaptive learning based particle swarm optimization , 2011, Inf. Sci..

[9]  Sakti Prasad Ghoshal,et al.  An opposition-based harmony search algorithm for engineering optimization problems , 2014 .

[10]  D. Karaboga,et al.  On the performance of artificial bee colony (ABC) algorithm , 2008, Appl. Soft Comput..

[11]  Narasimhan Sundararajan,et al.  Self regulating particle swarm optimization algorithm , 2015, Inf. Sci..

[12]  Russell C. Eberhart,et al.  Multiobjective optimization using dynamic neighborhood particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[13]  Karim Salahshoor,et al.  Global Dynamic Harmony Search algorithm: GDHS , 2014, Appl. Math. Comput..

[14]  Changhe Li,et al.  A Self-Learning Particle Swarm Optimizer for Global Optimization Problems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  V. Mukherjee,et al.  Particle swarm optimization with an aging leader and challengers algorithm for the solution of optimal power flow problem , 2016, Appl. Soft Comput..

[16]  Wei Liu,et al.  An Improved Comprehensive Learning Particle Swarm Optimization and Its Application to the Semiautomatic Design of Antennas , 2009, IEEE Transactions on Antennas and Propagation.

[17]  Ashok Dhondu Belegundu,et al.  A Study of Mathematical Programming Methods for Structural Optimization , 1985 .

[18]  Wei Li,et al.  A multilevel image thresholding segmentation algorithm based on two-dimensional K-L divergence and modified particle swarm optimization , 2016, Appl. Soft Comput..

[19]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[20]  Dervis Karaboga,et al.  A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems , 2011, Appl. Soft Comput..

[21]  M. Fesanghary,et al.  An improved harmony search algorithm for solving optimization problems , 2007, Appl. Math. Comput..

[22]  Ling Wang,et al.  An effective co-evolutionary particle swarm optimization for constrained engineering design problems , 2007, Eng. Appl. Artif. Intell..

[23]  M. Mahdavi,et al.  ARTICLE IN PRESS Available online at www.sciencedirect.com , 2007 .

[24]  Vinicius Veloso de Melo,et al.  Investigating Multi-View Differential Evolution for solving constrained engineering design problems , 2013, Expert Syst. Appl..

[25]  Jun Zhang,et al.  Adaptive Particle Swarm Optimization , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

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

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

[28]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2011, IEEE Trans. Evol. Comput..

[29]  Tapabrata Ray,et al.  A socio-behavioural simulation model for engineering design optimization , 2002 .

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

[31]  Nor Ashidi Mat Isa,et al.  An adaptive two-layer particle swarm optimization with elitist learning strategy , 2014, Inf. Sci..

[32]  Giovanna Cavazzini,et al.  Adaptive acceleration coefficients for a new search diversification strategy in particle swarm optimization algorithms , 2015, Inf. Sci..

[33]  Zhun Fan,et al.  Constrained optimization based on hybrid evolutionary algorithm and adaptive constraint-handling technique , 2009 .

[34]  Jianhua Wu,et al.  Novel global harmony search algorithm for unconstrained problems , 2010, Neurocomputing.

[35]  P. Suganthan.  Particle swarm optimiser with neighbourhood operator , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[36]  G. Tomassetti.  A cost-effective algorithm for the solution of engineering problems with particle swarm optimization , 2010 .

[37]  Halife Kodaz,et al.  A novel parallel multi-swarm algorithm based on comprehensive learning particle swarm optimization , 2015, Eng. Appl. Artif. Intell..

[38]  Yangyang Li,et al.  A particle swarm optimization based simultaneous learning framework for clustering and classification , 2014, Pattern Recognit..

[39]  Mahamed G. H. Omran,et al.  Global-best harmony search , 2008, Appl. Math. Comput..

[40]  Ivona Brajevic,et al.  An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems , 2012, Journal of Intelligent Manufacturing.

[41]  A. Gandomi,et al.  Mixed variable structural optimization using Firefly Algorithm , 2011 .

[42]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[43]  Maw-Sheng Chern,et al.  A binary particle swarm optimization based on the surrogate information with proportional acceleration coefficients for the 0-1 multidimensional knapsack problem , 2016 .

[44]  P. N. Suganthan,et al.  A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization , 2012, Inf. Sci..

[45]  Ling Wang,et al.  A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization , 2007, Appl. Math. Comput..

[46]  Jian Xu,et al.  Resource allocation based on quantum particle swarm optimization and RBF neural network for overlay cognitive OFDM System , 2016, Neurocomputing.

[47]  Toshiharu Sugie,et al.  Fixed-structure H∞ controller synthesis: A meta-heuristic approach using simple constrained particle swarm optimization , 2009, Autom..

[48]  Maw-Sheng Chern,et al.  Particle swarm optimization with time-varying acceleration coefficients for the multidimensional knapsack problem , 2014 .

[49]  Nor Ashidi Mat Isa,et al.  Bidirectional teaching and peer-learning particle swarm optimization , 2014, Inf. Sci..

[50]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search , 2005, 2005 IEEE Congress on Evolutionary Computation.

[51]  Carlos A. Coello Coello,et al.  Engineering optimization using simple evolutionary algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[52]  Xin-She Yang,et al.  Bat algorithm: a novel approach for global engineering optimization , 2012, 1211.6663.

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

[54]  Sam Kwong,et al.  Gbest-guided artificial bee colony algorithm for numerical function optimization , 2010, Appl. Math. Comput..

[55]  Andries Petrus Engelbrecht,et al.  A study of particle swarm optimization particle trajectories , 2006, Inf. Sci..

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

[57]  Mingchang Chih,et al.  Self-adaptive check and repair operator-based particle swarm optimization for the multidimensional knapsack problem , 2015, Appl. Soft Comput..

[58]  Mingchang Chih,et al.  Particle swarm optimization for the economic and economic statistical designs of the X control chart , 2011, Appl. Soft Comput..

[59]  Hamid R. Tizhoosh,et al.  Opposition-Based Learning: A New Scheme for Machine Intelligence , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[60]  Mohammed El-Abd,et al.  An improved global-best harmony search algorithm , 2013, Appl. Math. Comput..

[61]  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.

[62]  Yaochu Jin,et al.  A social learning particle swarm optimization algorithm for scalable optimization , 2015, Inf. Sci..

[63]  Adil Baykasoglu,et al.  Adaptive firefly algorithm with chaos for mechanical design optimization problems , 2015, Appl. Soft Comput..

[64]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[65]  Dervis Karaboga,et al.  Artificial bee colony algorithm for large-scale problems and engineering design optimization , 2012, J. Intell. Manuf..

[66]  Changhe Li,et al.  An adaptive mutation operator for particle swarm optimization , 2008 .

[67]  Bin Li,et al.  Multi-strategy ensemble particle swarm optimization for dynamic optimization , 2008, Inf. Sci..

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

[69]  Amir Hossein Gandomi,et al.  Bat algorithm for constrained optimization tasks , 2012, Neural Computing and Applications.

[70]  Carlos A. Coello Coello,et al.  Self-adaptive penalties for GA-based optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[71]  Carlos A. Coello Coello,et al.  Constraint-handling in genetic algorithms through the use of dominance-based tournament selection , 2002, Adv. Eng. Informatics.