An improved particle swarm optimization algorithm based on comparative judgment

Particle swarm optimization (PSO) algorithm is one of the most effective and popular swarm intelligence algorithms. In this paper, based on comparative judgment, an improved particle swarm optimization (IPSO) is proposed. Firstly, a new search equation is developed by considering individual experience, social experience and the integration of individual and social experience, which can be used to improve the convergence speed of the algorithm. Secondly, in order to avoid falling into a local optima, a location abandoned mechanism is proposed; meanwhile, a new equation to generate a new position for the corresponding particle is proposed. The experimental results show that IPSO algorithm has excellent solution quality and convergence characteristic comparing to basic PSO algorithm and performs better than some state-of-the-art algorithms on almost all tested functions.

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

[2]  Stan Matwin,et al.  HPSOM : A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM WITH GENETIC MUTATION , 2013 .

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

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

[5]  Kui Liu,et al.  A Novel Particle Swarm Optimization Algorithm for Global Optimization , 2016, Comput. Intell. Neurosci..

[6]  Thomas Stützle,et al.  A unified ant colony optimization algorithm for continuous optimization , 2014, Eur. J. Oper. Res..

[7]  Hui Wang,et al.  Diversity enhanced particle swarm optimization with neighborhood search , 2013, Inf. Sci..

[8]  Xiaodong Li,et al.  This article has been accepted for inclusion in a future issue. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 1 Locating and Tracking Multiple Dynamic Optima by a Particle Swarm Model Using Speciation , 2022 .

[9]  Xingsheng Gu,et al.  A dynamic inertia weight particle swarm optimization algorithm , 2008 .

[10]  Li Yu,et al.  A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimization , 2016, Comput. Oper. Res..

[11]  Kusum Deep,et al.  A new mutation operator for real coded genetic algorithms , 2007, Appl. Math. Comput..

[12]  Dervis Karaboga,et al.  A comprehensive survey: artificial bee colony (ABC) algorithm and applications , 2012, Artificial Intelligence Review.

[13]  Witold Pedrycz,et al.  Superior solution guided particle swarm optimization combined with local search techniques , 2014, Expert Syst. Appl..

[14]  Teresa Wu,et al.  An Adaptive Particle Swarm Optimization With Multiple Adaptive Methods , 2013, IEEE Transactions on Evolutionary Computation.

[15]  Wang Hui-fang.  Adaptive particle swarm optimization algorithm with dynamically changing inertia weight , 2010 .

[16]  Harun Uğuz,et al.  A novel particle swarm optimization algorithm with Levy flight , 2014, Appl. Soft Comput..

[17]  Francisco J. Rodríguez,et al.  A genetic algorithm for the minimum generating set problem , 2016, Appl. Soft Comput..

[18]  Musa Nur Gabere,et al.  Simulated annealing driven pattern search algorithms for global optimization , 2008 .

[19]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[20]  Sanyang Liu,et al.  Particle swarm optimization with chaotic opposition-based population initialization and stochastic search technique , 2012 .

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

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

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

[24]  Reza Safabakhsh,et al.  A novel stability-based adaptive inertia weight for particle swarm optimization , 2016, Appl. Soft Comput..

[25]  Siti Mariyam Hj. Shamsuddin,et al.  CAPSO: Centripetal accelerated particle swarm optimization , 2014, Inf. Sci..

[26]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[27]  Xiaoyan Sun,et al.  Adaptive bare-bones particle swarm optimization algorithm and its convergence analysis , 2014, Soft Comput..

[28]  Li Qian.  Adaptive Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight , 2011 .

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

[30]  Milan R. Rapaic,et al.  Time-varying PSO - convergence analysis, convergence-related parameterization and new parameter adjustment schemes , 2009, Inf. Process. Lett..

[31]  Eisuke Kita,et al.  Search performance improvement of Particle Swarm Optimization by second best particle information , 2014, Appl. Math. Comput..

[32]  Joarder Kamruzzaman,et al.  A modified immune network optimization algorithm , 2014 .

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

[34]  B. Alatas,et al.  Chaos embedded particle swarm optimization algorithms , 2009 .

[35]  Changhe Li,et al.  Particle swarm optimisation with simple and efficient neighbourhood search strategies , 2011 .

[36]  Gao Yue-lin.  Adaptive Particle Swarm Optimization Algorithm with Dynamically Changing Inertia Weight , 2008 .

[37]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[38]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[39]  Mohammad Reza Meybodi,et al.  novel multi-swarm algorithm for optimization in dynamic environments based n particle swarm optimization , 2013 .

[40]  Abdelkader Benyettou,et al.  Binary Cuckoo Search Algorithm for Band Selection in Hyperspectral Image Classification , 2022 .

[41]  Zong Woo Geem,et al.  A survey on applications of the harmony search algorithm , 2013, Eng. Appl. Artif. Intell..

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

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