Improved Asymmetric Time-varying Coefficients of Particle Swarm Optimization

In this work, a modified version of the newly-developed algorithm Improved Particle Swarm Optimization (PSOI) is proposed. PSOI is a type of a PSO algorithm that uses time-varying social coefficients to train the particles. It shows a better performance compared to the conventional PSO, and this inspires this work. The proposed method uses asymmetric polynomial curves, and hence, it is referred to as the Improved Asymmetric PSO (PSOAI). In this work, PSOAI is tested using several benchmarks and compared to several PSO versions including the PSOI. The results are promising compared to several versions of PSO.

[1]  Y. Nishio,et al.  Network-Structured Particle Swarm Optimizer with Various Topology and Its Behaviors , 2009, WSOM.

[2]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[3]  Ying Tan,et al.  Particle swarm optimisation based on self-organisation topology driven by different fitness rank , 2011, Int. J. Comput. Sci. Eng..

[4]  Tatjana V. Sibalija Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008-2018) , 2019, Appl. Soft Comput..

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

[6]  Guangqing Bao,et al.  Particle swarm optimization algorithm with asymmetric time varying acceleration coefficients , 2009, 2009 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[7]  Pramod Kumar Singh,et al.  Opposition chaotic fitness mutation based adaptive inertia weight BPSO for feature selection in text clustering , 2016, Appl. Soft Comput..

[8]  Zhongzhi Shi,et al.  Chaotic particle swarm optimization with sigmoid-based acceleration coefficients for numerical function optimization , 2019, Swarm Evol. Comput..

[9]  Juan Zhao,et al.  An Improved Grey Wolf Optimization Algorithm with Variable Weights , 2019, Comput. Intell. Neurosci..

[10]  Ke Chen,et al.  A hybrid particle swarm optimizer with sine cosine acceleration coefficients , 2018, Inf. Sci..

[11]  Wei Sun,et al.  Global genetic learning particle swarm optimization with diversity enhancement by ring topology , 2019, Swarm Evol. Comput..

[12]  Yuxin Zhao,et al.  Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach , 2018, ICCS.

[13]  Zhang Dingxue,et al.  A Modified Particle Swarm Optimization with an Adaptive Acceleration Coefficients , 2009, 2009 Asia-Pacific Conference on Information Processing.

[14]  Kari Tammi,et al.  Recent Studies on Chicken Swarm Optimization algorithm: a review (2014–2018) , 2019, Artificial Intelligence Review.

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

[16]  Ahmad Rezaee Jordehi,et al.  Time varying acceleration coefficients particle swarm optimisation (TVACPSO): A new optimisation algorithm for estimating parameters of PV cells and modules , 2016 .

[17]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[18]  Yu Liu,et al.  A New Bio-inspired Algorithm: Chicken Swarm Optimization , 2014, ICSI.

[19]  Weijia Cui,et al.  Biological Flower Pollination Algorithm with Orthogonal Learning Strategy and Catfish Effect Mechanism for Global Optimization Problems , 2018 .

[20]  Bo Liu,et al.  Adaptive particle swarm optimization with population diversity control and its application in tandem blade optimization , 2019 .

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

[22]  Graham Kendall,et al.  A multi-objective particle swarm optimization algorithm based on dynamic boundary search for constrained optimization , 2018, Appl. Soft Comput..

[23]  Tome Eftimov,et al.  A Novel Approach to statistical comparison of meta-heuristic stochastic optimization algorithms using deep statistics , 2017, Inf. Sci..

[24]  Yunfeng Xu,et al.  A Simple and Efficient Artificial Bee Colony Algorithm , 2013 .

[25]  Teresa Wu,et al.  An intelligent augmentation of particle swarm optimization with multiple adaptive methods , 2012, Inf. Sci..

[26]  Dervis Karaboga,et al.  Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems , 2007, IFSA.

[27]  José Gabriel Ramírez-Torres,et al.  A Statistical Study of the Effects of Neighborhood Topologies in Particle Swarm Optimization , 2011 .

[28]  Zhihua Cui,et al.  An Improved PSO with Time-Varying Accelerator Coefficients , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[29]  Yongliang Chen,et al.  Niching particle swarm optimization with equilibrium factor for multi-modal optimization , 2019, Inf. Sci..

[30]  Andrew Lewis,et al.  Autonomous Particles Groups for Particle Swarm Optimization , 2014 .

[31]  Pei-Chann Chang,et al.  A novel complex network community detection approach using discrete particle swarm optimization with particle diversity and mutation , 2019, Appl. Soft Comput..