1 research outputs found
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.Comment: 6 pages, published in CISS 201