A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem
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Date
2013
Authors
M. Fatih Tasgetiren
Ozge Buyukdagli
Damla Kizilay
Korhan Karabulut
Journal Title
Journal ISSN
Volume Title
Publisher
SPRINGER-VERLAG BERLIN
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
In this study we propose a populated iterated greedy algorithm with an Inver-Over operator to solve the traveling salesman problem. The iterated greedy (IG) algorithm is mainly based on the central procedures of destruction and construction. The basic idea behind it is to remove some solution components from a current solution and reconstruct them in the partial solution to obtain the complete solution again. In this paper we apply this idea in a populated manner (IGP) to the traveling salesman problem (TSP). Since the destruction and construction procedure is computationally expensive we also propose an iteration jumping to an Inver-Over operator during the search process. We applied the proposed algorithm to the well-known 14 TSP instances from TSPLIB. The computational results show that the proposed algorithm is very competitive to the recent best performing algorithms from the literature.
Description
Keywords
traveling salesman problem, iterated greedy algorithm, inver-over operator, memetic algorithm, genetic algorithm, meta-heuristics, DIFFERENTIAL EVOLUTION ALGORITHM, PARTICLE SWARM OPTIMIZATION, HEURISTIC ALGORITHM, NEURAL-NETWORK, LIN-KERNIGHAN, FLOW-SHOPS, MACHINE, SEARCH, MINIMIZATION, Genetic Algorithm, Traveling Salesman Problem, Iterated Greedy Algorithm, Inver-over Operator, Memetic Algorithm, Meta-heuristics
Fields of Science
Citation
WoS Q
Scopus Q

OpenCitations Citation Count
2
Source
4th International Conference on Swarm Evolutionary and Memetic Computing (SEMCCO)
Volume
8297
Issue
PART 1
Start Page
1
End Page
12
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Citations
CrossRef : 1
Scopus : 3
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Mendeley Readers : 9
SCOPUS™ Citations
3
checked on Apr 09, 2026
Web of Science™ Citations
3
checked on Apr 09, 2026
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