Opportunistic Self Organizing Migrating Algorithm for real-time Dynamic Traveling Salesman Problem

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.

[1]  Thomas Stützle,et al.  Ant Colony Optimization: Overview and Recent Advances , 2018, Handbook of Metaheuristics.

[2]  Godfrey C. Onwubolu,et al.  New optimization techniques in engineering , 2004, Studies in Fuzziness and Soft Computing.

[3]  Gaël Varoquaux,et al.  The NumPy Array: A Structure for Efficient Numerical Computation , 2011, Computing in Science & Engineering.

[4]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

[5]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[6]  Ivan Zelinka,et al.  SOMA—Self-organizing Migrating Algorithm , 2016 .

[7]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[8]  Jimy Dudhia,et al.  High-Resolution Hurricane Forecasts , 2011, Computing in Science & Engineering.

[9]  Seema Agrawal,et al.  Log-logistic SOMA with quadratic approximation crossover , 2015, International Conference on Computing, Communication & Automation.

[10]  Sarman K. Hadia,et al.  Solving City Routing Issue with Particle Swarm Optimization , 2012 .

[11]  Kusum Deep,et al.  A new hybrid Self Organizing Migrating Genetic Algorithm for function optimization , 2007, 2007 IEEE Congress on Evolutionary Computation.

[12]  Bruce L. Golden,et al.  VEHICLE ROUTING: METHODS AND STUDIES , 1988 .

[13]  Zbynek Raida,et al.  A Novel Multi-Objective Self-Organizing Migrating Algorithm , 2011 .

[14]  John D. Hunter,et al.  Matplotlib: A 2D Graphics Environment , 2007, Computing in Science & Engineering.