3,160 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
An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem
The Generalized Traveling Salesman Problem (GTSP) is an extension of the
well-known Traveling Salesman Problem (TSP), where the node set is partitioned
into clusters, and the objective is to find the shortest cycle visiting each
cluster exactly once. In this paper, we present a new hybrid Ant Colony System
(ACS) algorithm for the symmetric GTSP. The proposed algorithm is a
modification of a simple ACS for the TSP improved by an efficient GTSP-specific
local search procedure. Our extensive computational experiments show that the
use of the local search procedure dramatically improves the performance of the
ACS algorithm, making it one of the most successful GTSP metaheuristics to
date.Comment: 7 page
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