COOBBO: A Novel Opposition-Based Soft Computing Algorithm for TSP Problems

In this paper, we propose a novel definition of opposite path. Its core feature is that the sequence of candidate paths and the distances between adjacent nodes in the tour are considered simultaneously. In a sense, the candidate path and its corresponding opposite path have the same (or similar at least) distance to the optimal path in the current population. Based on an accepted framework for employing opposition-based learning, Oppositional Biogeography-Based Optimization using the Current Optimum, called COOBBO algorithm, is introduced to solve traveling salesman problems. We demonstrate its performance on eight benchmark problems and compare it with other optimization algorithms. Simulation results illustrate that the excellent performance of our proposed algorithm is attributed to the distinct definition of opposite path. In addition, its great strength lies in exploitation for enhancing the solution accuracy, not exploration for improving the population diversity. Finally, by comparing different version of COOBBO, another conclusion is that each successful opposition-based soft computing algorithm needs to adjust and remain a good balance between backward adjacent node and forward adjacent node.

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

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

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

[4]  Mario Ventresca,et al.  Oppositional Concepts in Computational Intelligence , 2008, Oppositional Concepts in Computational Intelligence.

[5]  Marjan Mernik,et al.  Exploration and exploitation in evolutionary algorithms: A survey , 2013, CSUR.

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

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

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

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

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

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

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

[15]  Miguel Casquilho,et al.  Travelling Salesman Problem , 2012 .

[16]  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.

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

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

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

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

[21]  Lei Wang,et al.  A novel oppositional biogeography-based optimization for combinatorial problems , 2014, 2014 10th International Conference on Natural Computation (ICNC).

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

[23]  Mohammad Bagher Ahmadi,et al.  Opposition versus randomness in binary spaces , 2015, Appl. Soft Comput..

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

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

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

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

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

[29]  Hajime Kita,et al.  A genetic solution for the traveling salesman problem by means of a thermodynamical selection rule , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

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

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

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

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

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

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

[36]  Jacek M. Zurada,et al.  Efficiency and Scalability Methods for Computational Intellect , 2013 .