Augment-insert algorithms for the capacitated arc routing problem
Computers and Operations Research
Routing winter gritting vehicles
CO89 Selected papers of the conference on Combinatorial Optimization
A deterministic tabu search algorithm for the capacitated arc routing problem
Computers and Operations Research
Solving an urban waste collection problem using ants heuristics
Computers and Operations Research
An improved heuristic for the capacitated arc routing problem
Computers and Operations Research
A weight-coded genetic algorithm for the capacitated arc routing problem
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A global repair operator for capacitated arc routing problem
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving capacitated arc routing problems using a transformation to the CVRP
Computers and Operations Research
Memetic algorithm with extended neighborhood search for capacitated arc routing problems
IEEE Transactions on Evolutionary Computation
An evolutionary approach to the multidepot capacitated arc routing problem
IEEE Transactions on Evolutionary Computation
A branch-cut-and-price algorithm for the capacitated arc routing problem
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
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Capacitated Arc Routing Problem (CARP) is a well known combinatorial problem that requires the identification of the minimum total distance travelled by a set of vehicles to service a given set of roads subject to the vehicle's capacity constraints. While a number of optimization algorithms have been proposed over the years to solve CARP problems, all of them require a large number of function evaluations prior to its convergence. Application of such algorithms are thus limited for practical applications as many of such applications require an acceptable solution within a limited time frame, e.g., dynamic versions of the problem. This paper is a pre-cursor to such applications, and the aim of this study is to develop an algorithm that can solve such problems with a limited computational budget of 50,000 function evaluations. The algorithm is embedded with a similarity based parent selection scheme inspired by the principles of multiple sequence alignment, hybrid crossovers, i.e., a combination of similarity preservation schemes, path scanning heuristics and random key crossovers. The performance of the algorithm is compared with a recent Memetic algorithm, i.e., Decomposition-Based Memetic Algorithm proposed in 2010 across three sets of commonly used benchmarks (gdb, val, egl). The results clearly indicate the superiority of performance across both small and large instances.