Adapting TRIBES algorithm for Traveling Salesman Problem

Metaheuristics constitute an important alternative in solving NP-Hard combinatorial optimization problems. Unfortunately, many parameters have to be tuned for any metaheuristic, and their values may have a great influence on the efficiency and effectiveness of the search. The exploration of an optim...

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Bibliographic Details
Main Authors: Daoudi, M., Boukra, A., Ahmed-Nacer, M.
Format: Conference Proceeding
Language:English
Subjects:
Branch and Bound
Cities and towns
Combinatorial Optimization Problem
Gaussian distribution
Genetic Algorithm
Genetic algorithms
Metaheuristic
NP-Hard problems
Optimization
Particle swarm optimization
Search problems
Traveler Salesman Problem
Traveling salesman problems
TRIBES
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Summary:Metaheuristics constitute an important alternative in solving NP-Hard combinatorial optimization problems. Unfortunately, many parameters have to be tuned for any metaheuristic, and their values may have a great influence on the efficiency and effectiveness of the search. The exploration of an optimal combination of such values may be difficult and big time consuming. Clerc et al have defined a parameter-free algorithm for PSO (Particle Swarm Optimization), called TRIBES. In this paper, we propose to adapt TRIBES to solve discrete problems. To highlight our approach, we treat of the well-known NP-Hard Traveling Salesman Problem (TSP) problem. Modifications in different mechanisms and formulae adaptations are made, like in the generation process of the particles and in the displacement strategies. The experimentations results show the good behavior of the "Adapted TRIBES". Comparison is made with a basic genetic algorithm, and with a branch and bound method.
DOI:10.1109/ISPS.2011.5898889