A novel oppositional biogeography-based optimization for combinatorial problems

In this paper, a novel definition of opposite path is proposed. Its core feature is that the node sequence of candidate paths and the distances between adjacent nodes in the tour are considered simultaneously. In a sense, the path and its corresponding opposite path have the same (or similar, at least) distance from the optimal path in the current population. Based on an accepted framework for employing opposition-based learning, the Oppositional Biogeography-Based Optimization using the Current Optimum, called COOBBO algorithm, is introduced to solve combinatorial problem, such as traveling salesman problems. The performance of COOBBO on 8 benchmark problems is demonstrated and compared with other optimization algorithms. Simulation results illustrate that the excellent performance of our proposed algorithm is attributed to the distinct definition of opposite path.

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

[2]  Dan Simon,et al.  Biogeography-Based Optimization for Large Scale Combinatorial Problems , 2013 .

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

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

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

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

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

[8]  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.

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

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

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

[12]  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).

[13]  Gerhard Reinelt,et al.  TSPLIB - A Traveling Salesman Problem Library , 1991, INFORMS J. Comput..

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

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

[16]  Dan Simon,et al.  Biogeography-Based Optimization , 2022 .

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

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

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

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

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

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

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

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

[25]  Li Zhao,et al.  A review of opposition-based learning from 2005 to 2012 , 2014, Eng. Appl. Artif. Intell..

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

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