Dynamic Group Optimization Algorithm With Embedded Chaos

Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which is developed to mimic the behaviors of animal and human group socializing. However, one of the major drawbacks of the DGO is the premature convergence. Therefore, in order to deal with this challenge, we introduce chaos theory into the DGO algorithm and come up with a new chaotic dynamic group optimization algorithm (CDGO) that can accelerate the convergence of DGO. Various chaotic maps are used to adjust the update of solutions in CDGO. Extensive experiments have been carried out, and the results have shown that CDGO can be a very promising tool for solving optimization algorithms. We also demonstrated good results based on real world data, where, in particular, solving multimedia data clustering problems.

[1]  Nikolaus Hansen,et al.  Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.

[2]  Leandro dos Santos Coelho,et al.  Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization , 2008, Expert Syst. Appl..

[3]  Edgar Alfredo Portilla-Flores,et al.  Reconfigurable Mechanical System Design for Tracking an Ankle Trajectory Using an Evolutionary Optimization Algorithm , 2017, IEEE Access.

[4]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

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

[6]  Jilei Zhou,et al.  Chaos optimization algorithms based on chaotic maps with different probability distribution and search speed for global optimization , 2014, Commun. Nonlinear Sci. Numer. Simul..

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

[8]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

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

[10]  Xin-She Yang,et al.  Chaos-enhanced accelerated particle swarm optimization , 2013, Commun. Nonlinear Sci. Numer. Simul..

[11]  Wu Xiaolin,et al.  A Novel Color Image Encryption Scheme Using Rectangular Transform-Enhanced Chaotic Tent Maps , 2017 .

[12]  Mohammad Saleh Tavazoei,et al.  Comparison of different one-dimensional maps as chaotic search pattern in chaos optimization algorithms , 2007, Appl. Math. Comput..

[13]  Andrew Lewis,et al.  Biogeography-based optimisation with chaos , 2014, Neural Computing and Applications.

[14]  Ahmad Reza Naghsh-Nilchi,et al.  Chaotic Particle Swarm Optimization with Mutation for Classification , 2015, Journal of medical signals and sensors.

[15]  Simon Fong,et al.  Dynamic group search algorithm , 2016, 2016 4th International Symposium on Computational and Business Intelligence (ISCBI).

[16]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[17]  Simon Fong,et al.  Nature-Inspired Clustering Algorithms for Web Intelligence Data , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

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

[19]  Tomonobu Senjyu,et al.  Enhancement of a Small Power System Performance Using Multi-Objective Optimization , 2017, IEEE Access.

[20]  Xin-She Yang,et al.  Firefly algorithm with chaos , 2013, Commun. Nonlinear Sci. Numer. Simul..

[21]  Amir Hossein Gandomi,et al.  Chaotic Krill Herd algorithm , 2014, Inf. Sci..

[22]  Bin Zhu,et al.  A Novel Color Image Encryption Scheme Using Rectangular Transform-Enhanced Chaotic Tent Maps , 2017, IEEE Access.

[23]  Neil Genzlinger.  A. and Q , 2006 .

[24]  Simon Fong,et al.  Integrating nature-inspired optimization algorithms to K-means clustering , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).

[25]  J Saunders,et al.  Parallelising a model of bacterial interaction and evolution. , 2004, Bio Systems.

[26]  Xiaoming Chang,et al.  An intelligent noise reduction method for chaotic signals based on genetic algorithms and lifting wavelet transforms , 2013, Inf. Sci..

[27]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

[28]  W. Marsden.  I and J , 2012 .

[29]  Luigi Fortuna,et al.  Chaotic sequences to improve the performance of evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

[30]  D. Abbott,et al.  Control systems with stochastic feedback. , 2001, Chaos.