New insertion and postoptimization procedures for the traveling salesman problem
Operations Research
Computers and Operations Research
Outline for a Logical Theory of Adaptive Systems
Journal of the ACM (JACM)
Future Generation Computer Systems
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu Search
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Efficient cluster compensation for lin-kernighan heuristics
Efficient cluster compensation for lin-kernighan heuristics
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Chained Lin-Kernighan for Large Traveling Salesman Problems
INFORMS Journal on Computing
Stochastic Local Search: Foundations & Applications
Stochastic Local Search: Foundations & Applications
Expanding Neighborhood GRASP for the Traveling Salesman Problem
Computational Optimization and Applications
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
The P=NP Question and Gdels Lost Letter
The P=NP Question and Gdels Lost Letter
Information Sciences: an International Journal
Filter modeling using gravitational search algorithm
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
A novel hybrid K-harmonic means and gravitational search algorithm approach for clustering
Expert Systems with Applications: An International Journal
Honey bees mating optimization algorithm for the Euclidean traveling salesman problem
Information Sciences: an International Journal
A prototype classifier based on gravitational search algorithm
Applied Soft Computing
Chaotic secure communication based on a gravitational search algorithm filter
Engineering Applications of Artificial Intelligence
Information Sciences: an International Journal
A chaotic digital secure communication based on a modified gravitational search algorithm filter
Information Sciences: an International Journal
A probabilistic heuristic for a computationally difficult set covering problem
Operations Research Letters
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Metaheuristics are general search strategies that, at the exploitation stage, intensively exploit areas of the solution space with high quality solutions and, at the exploration stage, move to unexplored areas of the solution space when necessary. The Gravitational Search Algorithm (GSA) is a stochastic population-based metaheuristic that was originally designed for solving continuous optimization problems. It has a flexible and well-balanced mechanism for enhancing exploration and exploitation abilities. In this paper, a Discrete Gravitational Search Algorithm (DGSA) is proposed to solve combinatorial optimization problems. The proposed DGSA uses a Path Re-linking (PR) strategy instead of the classic way in which the agents of GSA usually move from their current position to the position of other agents. The proposed algorithm was tested on a set of 54 Euclidean benchmark instances of TSP with sizes ranging from 51 to 2392 nodes. The results were satisfactory and in the majority of the instances, the results were equal to the best known solution. The proposed algorithm ranked ninth when compared with 54 different algorithms with regard to quality of the solution.