A review of opposition-based learning from 2005 to 2012

Diverse forms of opposition are already existent virtually everywhere around us, and utilizing opposite numbers to accelerate an optimization method is a new idea. Since 2005, opposition-based learning is a fast growing research field in which a variety of new theoretical models and technical methods have been studied for dealing with complex and significant problems. As a result, an increasing number of works have thus proposed. This paper provides a survey on the state-of-the-art of research, reported in the specialized literature to date, related to this framework. This overview covers basic concepts, theoretical foundation, combinations with intelligent algorithms, and typical application fields. A number of challenges that can be undertaken to help move the field forward are discussed according to the current state of the opposition-based learning.

[1]  Mohammed El-Abd,et al.  Opposition-based artificial bee colony algorithm , 2011, GECCO '11.

[2]  Jing Wang,et al.  Diversity Analysis of Opposition-Based Differential Evolution - An Experimental Study , 2010, ISICA.

[3]  Alice R. Malisia,et al.  Investigating the Application of Opposition-Based Ideas to Ant Algorithms , 2007 .

[4]  Ching-Yuen Chan,et al.  An opposition-based chaotic GA/PSO hybrid algorithm and its application in circle detection , 2012, Comput. Math. Appl..

[5]  Lei Peng,et al.  A Novel Opposition-Based Multi-objective Differential Evolution Algorithm for Multi-objective Optimization , 2008, ISICA.

[6]  Zhiguo Huang,et al.  Opposition-Based Artificial Bee Colony with Dynamic Cauchy Mutation for Function Optimization , 2012 .

[7]  Lingling Huang,et al.  A global best artificial bee colony algorithm for global optimization , 2012, J. Comput. Appl. Math..

[8]  Shahryar Rahnamayan,et al.  Opposition-based Differential Evolution with protective generation jumping , 2011, 2011 IEEE Symposium on Differential Evolution (SDE).

[9]  M. Arfan Jaffar,et al.  Opposition Based Genetic Algorithm with Cauchy Mutation for Function Optimization , 2010, 2010 International Conference on Information Science and Applications.

[10]  Ajith Abraham,et al.  A Hybrid Ant Colony Differential Evolution and its application to water resources problems , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[11]  Lin Han,et al.  A Novel Opposition-Based Particle Swarm Optimization for Noisy Problems , 2007, Third International Conference on Natural Computation (ICNC 2007).

[12]  Shahryar Rahnamayan,et al.  Investigating in scalability of opposition-based differential evolution , 2008 .

[13]  A. Kai Qin,et al.  Dynamic regional harmony search with opposition and local learning , 2011, GECCO '11.

[14]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[15]  Ling Li,et al.  Bacterial Foraging Optimization Algorithm Based on Opposition-Based Learning , 2011 .

[16]  Na Wang,et al.  Influence of Dimensionality and Population Size on Opposition-based Differential Evolution Using the Current Optimum , 2013 .

[17]  K. Ponnambalam,et al.  Opposition-Based Reinforcement Learning in the Management of Water Resources , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

[18]  Mohammed El-Abd,et al.  Generalized opposition-based artificial bee colony algorithm , 2012, 2012 IEEE Congress on Evolutionary Computation.

[19]  Shahryar Rahnamayan,et al.  Center-based sampling for population-based algorithms , 2009, 2009 IEEE Congress on Evolutionary Computation.

[20]  Shahryar Rahnamayan,et al.  Quasi-oppositional Differential Evolution , 2007, 2007 IEEE Congress on Evolutionary Computation.

[21]  Yonggang Li,et al.  An improved particle swarm optimization algorithm with opposition mutation , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[22]  A. L. Gutierrez,et al.  Comparison of different PSO initialization techniques for high dimensional search space problems: A test with FSS and antenna arrays , 2011, Proceedings of the 5th European Conference on Antennas and Propagation (EUCAP).

[23]  Jing Wang,et al.  Space transformation search: a new evolutionary technique , 2009, GEC '09.

[24]  Ajith Abraham,et al.  A Modified Differential Evolution Algorithm and Its Application to Engineering Problems , 2009, 2009 International Conference of Soft Computing and Pattern Recognition.

[25]  Mohamed S. Kamel,et al.  Oppositional target domain estimation using grid-based simulation , 2009, Appl. Soft Comput..

[26]  Mario Ventresca,et al.  Numerical condition of feedforward networks with opposite transfer functions , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[27]  Muhammad Rashid,et al.  Improved Opposition-Based PSO for Feedforward Neural Network Training , 2010, 2010 International Conference on Information Science and Applications.

[28]  Hamid R. Tizhoosh,et al.  Applying Opposition-Based Ideas to the Ant Colony System , 2007, 2007 IEEE Swarm Intelligence Symposium.

[29]  Xiu-Kun Wang,et al.  Opposition-based Cooperative Coevolutionary Differential Evolution Algorithm With Gaussian Mutation for Simplified Satellite Module Optimization , 2012 .

[30]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution (ODE) with Variable Jumping Rate , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[31]  Mahamed G. H. Omran,et al.  Constrained optimization using CODEQ , 2009 .

[32]  Masoud Yaghini,et al.  HIOPGA : A New Hybrid Metaheuristic Algorithm to Train Feedforward Neural Networks for Prediction , 2011 .

[33]  Weifeng Gao,et al.  A modified artificial bee colony algorithm , 2012, Comput. Oper. Res..

[34]  Zhijian Wu,et al.  Hybrid Differential Evolution Algorithm with Chaos and Generalized Opposition-Based Learning , 2010, ISICA.

[35]  Provas Kumar Roy,et al.  Quasi-oppositional Biogeography-based Optimization for Multi-objective Optimal Power Flow , 2011 .

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

[37]  Mahamed G. H. Omran.  CODEQ: an effective metaheuristic for continuous global optimisation , 2010, Int. J. Metaheuristics.

[38]  Shahryar Rahnamayan,et al.  An intuitive distance-based explanation of opposition-based sampling , 2012, Appl. Soft Comput..

[39]  Dejun Mu,et al.  A Hybrid Differential Evolution for Numerical Optimization , 2009, 2009 2nd International Conference on Biomedical Engineering and Informatics.

[40]  Ning Dong,et al.  Multiobjective Differential Evolution Based on Opposite Operation , 2009, 2009 International Conference on Computational Intelligence and Security.

[41]  H.R. Tizhoosh,et al.  Application of Opposition-Based Reinforcement Learning in Image Segmentation , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.

[42]  G. Samanta,et al.  A novel design strategy of low-pass FIR filter using Opposition-based Differential Evolution algorithm , 2012, 2012 IEEE Students' Conference on Electrical, Electronics and Computer Science.

[43]  Shahriar B. Shokouhi,et al.  A novel opposition-based classifier for mass diagnosis in mammography images , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).

[44]  B. K. Panigrahi,et al.  ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2010 .

[45]  Dan Simon,et al.  Biogeography-based optimization of neuro-fuzzy system parameters for diagnosis of cardiac disease , 2010, GECCO '10.

[46]  Xiaolei Wang,et al.  A hybrid optimization method of harmony search and opposition-based learning , 2012 .

[47]  Aniruddha Bhattacharya,et al.  Oppositional Biogeography-Based Optimization for multi-objective Economic Emission Load Dispatch , 2010, 2010 Annual IEEE India Conference (INDICON).

[48]  Bidyadhar Subudhi,et al.  A differential evolution based neural network approach to nonlinear system identification , 2011, Appl. Soft Comput..

[49]  Xin Yang,et al.  Improved opposition-based biogeography optimization , 2011, The Fourth International Workshop on Advanced Computational Intelligence.

[50]  Janez Brest,et al.  History mechanism supported differential evolution for chess evaluation function tuning , 2010, Soft Comput..

[51]  Jun Tang,et al.  On the improvement of opposition-based differential evolution , 2010, 2010 Sixth International Conference on Natural Computation.

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

[53]  Junjie Li,et al.  An Improved Artificial Bee Colony Algorithm , 2010, 2010 2nd International Workshop on Intelligent Systems and Applications.

[54]  Tarun Kumar Sharma,et al.  Intermediate Population Based Differential Evolution Algorithm , 2011 .

[55]  Masoud Yaghini,et al.  A hybrid algorithm for artificial neural network training , 2013, Eng. Appl. Artif. Intell..

[56]  Zhiwei Ni,et al.  Opposition based comprehensive learning particle swarm optimization , 2008, 2008 3rd International Conference on Intelligent System and Knowledge Engineering.

[57]  Mario Ventresca,et al.  Improving gradient-based learning algorithms for large scale feedforward networks , 2009, 2009 International Joint Conference on Neural Networks.

[58]  Sakti Prasad Ghoshal,et al.  Solution of combined economic and emission dispatch problems of power systems by an opposition-based harmony search algorithm , 2012 .

[59]  Giovanni Iacca,et al.  Opposition-Based Learning in Compact Differential Evolution , 2011, EvoApplications.

[60]  Uzay Kaymak,et al.  Proceedings of the joint 2009 International Fuzzy Systems Association world congress and 2009 European Society of Fuzzy Logic and Technology conference (IFSA/EUSFLAT 2009), Lisbon, Portugal, July 20-24, 2009 , 2009 .

[61]  Leandro dos Santos Coelho,et al.  Opposition-based shuffled PSO with passive congregation applied to FM matching synthesis , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[62]  S. Rahnamayan,et al.  Solving large scale optimization problems by opposition-based differential evolution (ODE) , 2008 .

[63]  Shahryar Rahnamayan,et al.  Opposition versus randomness in soft computing techniques , 2008, Appl. Soft Comput..

[64]  Shahryar Rahnamayan,et al.  A novel population initialization method for accelerating evolutionary algorithms , 2007, Comput. Math. Appl..

[65]  Jun Tang,et al.  An Enhanced Opposition-Based Particle Swarm Optimization , 2009, 2009 WRI Global Congress on Intelligent Systems.

[66]  Mahamed G. H. Omran,et al.  Using opposition-based learning to improve the performance of particle swarm optimization , 2008, 2008 IEEE Swarm Intelligence Symposium.

[67]  Millie Pant,et al.  An efficient Differential Evolution based algorithm for solving multi-objective optimization problems , 2011, Eur. J. Oper. Res..

[68]  Uğur Yüzgeç,et al.  Performance comparison of differential evolution techniques on optimization of feeding profile for an industrial scale baker's yeast fermentation process. , 2010, ISA transactions.

[69]  Hongzhi Liu,et al.  An improved artificial bee colony algorithm , 2013, 2013 25th Chinese Control and Decision Conference (CCDC).

[70]  Zhijian Wu,et al.  Parallel differential evolution with self-adapting control parameters and generalized opposition-based learning for solving high-dimensional optimization problems , 2013, J. Parallel Distributed Comput..

[71]  James S. Welsh,et al.  Using redundant fitness functions to improve optimisers for humanoid robot walking , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[72]  Mario Ventresca,et al.  Opposite Transfer Functions and Backpropagation Through Time , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[73]  Hamid R. Tizhoosh,et al.  Opposition-Based Reinforcement Learning , 2006, J. Adv. Comput. Intell. Intell. Informatics.

[74]  Maryam Shokri,et al.  Knowledge of opposite actions for reinforcement learning , 2011, Appl. Soft Comput..

[75]  Haiping Ma,et al.  Oppositional Particle Swarm Optimization Algorithm and Its Application to Fault Monitor , 2009, 2009 Chinese Conference on Pattern Recognition.

[76]  Zhijian Wu,et al.  A Hybrid Parallel Evolutionary Algorithm Based on Elite-Subspace Strategy and Space Transformation Search , 2009, HPCA.

[77]  Jing Wang,et al.  A New Population Initialization Method Based on Space Transformation Search , 2009, 2009 Fifth International Conference on Natural Computation.

[78]  Adel M. Alimi,et al.  Opposition-based particle swarm optimization for the design of beta basis function neural network , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[79]  Wang Na.  Opposition-Based Differential Evolution Using the Current Optimum for Function Optimization , 2011 .

[80]  Mehmet Ergezer,et al.  Survey of oppositional algorithms , 2011, 14th International Conference on Computer and Information Technology (ICCIT 2011).

[81]  Zhenping Li,et al.  A Hybrid Particle Swarm Optimization for Numerical Optimization , 2009, 2009 International Conference on Business Intelligence and Financial Engineering.

[82]  Zhiming Cui,et al.  Fuzzy c-means clustering and opposition-based reinforcement learning for traffic congestion identification , 2012 .

[83]  Zhiwei Ni,et al.  A Novel Swarm Model With Quasi-oppositional Particle , 2009, 2009 International Forum on Information Technology and Applications.

[84]  Abdul Rauf Baig,et al.  Opposition based initialization in particle swarm optimization (O-PSO) , 2009, GECCO '09.

[85]  Zhiwei Ni,et al.  A Novel PSO for Multi-stage Portfolio Planning , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.

[86]  Hamid R. Tizhoosh,et al.  Reinforcement Learning Based on Actions and Opposite Actions , 2005 .

[87]  Dan Simon,et al.  Oppositional biogeography-based optimization for combinatorial problems , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[88]  Hamid R. Tizhoosh,et al.  Active exploratory q-learning for large problems , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[89]  Muhammad Imran,et al.  Opposition based PSO and mutation operators , 2010, 2010 2nd International Conference on Education Technology and Computer.

[90]  Andries Petrus Engelbrecht,et al.  Free Search Differential Evolution , 2009, 2009 IEEE Congress on Evolutionary Computation.

[91]  Sanyang Liu,et al.  Improved artificial bee colony algorithm for global optimization , 2011 .

[92]  Wu Zhijian.  Opposition-Based Particle Swarm Optimization for Solving Large Scale Optimization Problems on Graphic Process Unit , 2011 .

[93]  Morteza Alinia Ahandani,et al.  Opposition-based learning in the shuffled differential evolution algorithm , 2012, Soft Comput..

[94]  Sakti Prasad Ghoshal,et al.  A novel opposition-based gravitational search algorithm for combined economic and emission dispatch problems of power systems , 2012 .

[95]  H.R. Tizhoosh,et al.  Opposition-Based Q(λ) Algorithm , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[96]  Fei Jiang,et al.  An improved artificial bee colony algorithm for directing orbits of chaotic systems , 2011, Appl. Math. Comput..

[97]  F. Khalvati,et al.  Opposition-Based Window Memoization for Morphological Algorithms , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.

[98]  Shahryar Rahnamayan,et al.  A note on "Opposition versus randomness in soft computing techniques" [Appl. Soft Comput 8 (2) (2008) 906-918] , 2010, Appl. Soft Comput..

[99]  Massimiliano Kaucic.  A multi-start opposition-based particle swarm optimization algorithm with adaptive velocity for bound constrained global optimization , 2013, J. Glob. Optim..

[100]  Zhijian Wu,et al.  A Scalability Test for Accelerated DE Using Generalized Opposition-Based Learning , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[101]  Mario Ventresca,et al.  Simulated Annealing with Opposite Neighbors , 2007, 2007 IEEE Symposium on Foundations of Computational Intelligence.

[102]  Guobiao Cai,et al.  Particle swarm optimization with opposition-based disturbance , 2010, 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).

[103]  Mario Ventresca,et al.  A diversity maintaining population-based incremental learning algorithm , 2008, Inf. Sci..

[104]  Li Yonggang,et al.  An improved particle swarm algorithm and its application in grinding process optimization , 2008, 2008 27th Chinese Control Conference.

[105]  Ying Gao,et al.  Opposition-Based Learning Estimation of Distribution Algorithm with Gaussian Copulas and Its Application to Placement of RFID Readers , 2011, AICI.

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

[107]  Shiu Yin Yuen,et al.  Multiobjective differential evolution algorithm with opposition-based parameter control , 2012, 2012 IEEE Congress on Evolutionary Computation.

[108]  Haiping Ma,et al.  Oppositional ant colony optimization algorithm and its application to fault monitoring , 2010, Proceedings of the 29th Chinese Control Conference.

[109]  Jiahua Xie,et al.  Improved differential evolution for global optimization , 2010, 2010 2nd IEEE International Conference on Information Management and Engineering.

[110]  Jianghua Li.  A Hybrid Differential Evolution Algorithm with Opposition-based Learning , 2012, 2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[111]  Shahryar Rahnamayan,et al.  Opposition based computing — A survey , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[112]  Fan Wang,et al.  Opposition-based Particle Swarm Optimization with plow operator , 2011, Proceedings of 2011 Cross Strait Quad-Regional Radio Science and Wireless Technology Conference.

[113]  Wang Yan-jiao.  Artificial bee colony algorithm with fast convergence , 2011 .

[114]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution for Optimization of Noisy Problems , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[115]  Peng-Jun Zhao,et al.  A Hybrid Harmony Search Algorithm for Numerical Optimization , 2010, 2010 International Conference on Computational Aspects of Social Networks.

[116]  Hamid R. Tizhoosh,et al.  Visualization of hidden structures in corporate failure prediction using opposite pheromone per node model , 2010, IEEE Congress on Evolutionary Computation.

[117]  Zhijian Wu,et al.  Enhanced opposition-based differential evolution for solving high-dimensional continuous optimization problems , 2011, Soft Comput..

[118]  Shahryar Rahnamayan,et al.  Opposition-Based Differential Evolution Algorithms , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[119]  R. Balamurugan,et al.  Emission-constrained Dynamic Economic Dispatch using Opposition-based Self-adaptive Differential Evolution Algorithm , 2009 .

[120]  Hamid R. Tizhoosh.  Opposite Fuzzy Sets with Applications in Image Processing , 2009, IFSA/EUSFLAT Conf..

[121]  Mario Ventresca,et al.  Improving the Convergence of Backpropagation by Opposite Transfer Functions , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[122]  Shiu Yin Yuen,et al.  Opposition-based adaptive differential evolution , 2012, 2012 IEEE Congress on Evolutionary Computation.

[123]  Shahryar Rahnamayan,et al.  Image thresholding using micro opposition-based Differential Evolution (Micro-ODE) , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[124]  S. Reghunathan,et al.  Performance evaluation of opposition based Differential Evolution on non-convex economic dispatch , 2012, 2012 International Conference on Advances in Power Conversion and Energy Technologies (APCET).

[125]  Dan Simon,et al.  Oppositional biogeography-based optimization , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.

[126]  Shahryar Rahnamayan,et al.  Oppositional fuzzy image thresholding , 2010, International Conference on Fuzzy Systems.

[127]  Zhijian Wu,et al.  A Hybrid Particle Swarm Optimization Algorithm Based on Space Transformation Search and a Modified Velocity Model , 2009, HPCA.

[128]  Junliang Yang,et al.  An improved quantum-behaved particle swarm optimization algorithm , 2010, CAR 2010.

[129]  M.S. Kamel,et al.  Tradeoff between exploration and exploitation of OQ(λ) with non-Markovian update in dynamic environments , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[130]  Hui Wang,et al.  Opposition-based particle swarm algorithm with cauchy mutation , 2007, 2007 IEEE Congress on Evolutionary Computation.

[131]  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).

[132]  P. K. Chattopadhyay,et al.  Solution of Economic Power Dispatch Problems Using Oppositional Biogeography-based Optimization , 2010 .

[133]  Hamid R. Tizhoosh,et al.  Quasi-global oppositional fuzzy thresholding , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[134]  Tarun Kumar Sharma,et al.  Enhancing the food locations in an artificial bee colony algorithm , 2011, 2011 IEEE Symposium on Swarm Intelligence.

[135]  Muhammad Kamran,et al.  Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO) , 2009 .

[136]  Xiao Zhi Gao,et al.  A Hybrid Harmony Search Method Based on OBL , 2010, 2010 13th IEEE International Conference on Computational Science and Engineering.

[137]  M.S. Kamel,et al.  Opposition-Based Q(λ) with Non-Markovian Update , 2007, 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning.

[138]  Adel M. Alimi,et al.  Opposition-based differential evolution for beta basis function neural network , 2010, IEEE Congress on Evolutionary Computation.

[139]  Adel Torkaman Rahmani,et al.  Molecular docking with opposition-based differential evolution , 2012, SAC '12.