Meta-Heuristic Solution Approaches for Traveling Salesperson Problem
The traveling salesperson problem (TSP) is the NP-hard optimization
problems which have been widely studied over the past years. TSP creates a
Hamiltonian cycle where each node is visited once and only once to minimize the
total traveled distance. TSPs are difficult to be solved using classical
mathematical methods. Even with nowadays computers solving TSP problems with
these methods takes very plenty of time. Therefore, many efficient optimization
methods have been focused for academic proposes for the TSP all the times. Most
of the TSP problems are now solved by meta-heuristic methods, that provides a
satisfactory solutions in real-time. Meta-heuristic algorithms were inspired
from behaviors of animals and insects such as ants, bees, fish schools, bird
flocks and mammals.This paper focuses on three meta-heuristic methods: Whale
Optimization Algorithm (WOA), Particle Swarm Optimization (PSO) algorithm and
Grey Wolf Optimizer (GWO). The problem for application was selected from
TSPLIB. Probably the best implemented solutions were Whale Optimization
Algorithm and Grey Wolf Optimizer which can be recommended as primary algorithm
to solve the TSP or to start with the meta-heuristic solution
Keywords:
Travelling salesperson problem, Meta-heuristic optimization, Whale Optimization Algorithm Grey Wolf Optimizer, Particle Swarm Optimization,
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- ISSN: 2147-8228
- Yayın Aralığı: Yılda 4 Sayı
- Başlangıç: 2013
- Yayıncı: Selçuk Üniversitesi