Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
A Hybrid Genetic Algorithm for School Timetabling
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Scatter Search: Methodology and Implementations in C
Scatter Search: Methodology and Implementations in C
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
A memetic algorithm and a tabu search for the multi-compartment vehicle routing problem
Computers and Operations Research
Column Generation Algorithms for the Capacitated m-Ring-Star Problem
COCOON '08 Proceedings of the 14th annual international conference on Computing and Combinatorics
Assignment Problems
The Capacitated m-Ring-Star Problem
Operations Research
Metaheuristics and cooperative approaches for the Bi-objective Ring Star Problem
Computers and Operations Research
A global repair operator for capacitated arc routing problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An effective memetic algorithm for the cumulative capacitated vehicle routing problem
Computers and Operations Research
The maximum weight perfect matching problem for complete weighted graphs is in PC
SPDP '90 Proceedings of the 1990 IEEE Second Symposium on Parallel and Distributed Processing
Handbook of Memetic Algorithms
Handbook of Memetic Algorithms
A memetic algorithm for the quadratic multiple container packing problem
Applied Intelligence
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The Capacitated m-Ring-Star Problem (CmRSP) models a network topology design problem in the telecommunications industry. In this paper, we propose to solve this problem using a memetic algorithm that includes a crossover operation, a mutation operation, a local search involving three neighborhood operators, and a population selection strategy that maintains population diversity. Our approach generates the best known solutions for 131 out of 138 benchmark instances, improving on the previous best solutions for 24 of them, and exhibits more advantages on large benchmark instances when compared with the best existing approach. Additionally, all existing algorithms for this problem in literature assume that the underlying graph of the problem instance satisfies the triangle inequality rule; our approach does not require this assumption. We also generated a new set of 36 larger test instances based on a digital data service network price structure to serve as a new benchmark data set for future researchers.