On some variants of the bandwidth minimization problem
SIAM Journal on Computing
Future paths for integer programming and links to artificial intelligence
Computers and Operations Research - Special issue: Applications of integer programming
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
A survey of graph layout problems
ACM Computing Surveys (CSUR)
Reaction-Diffusion Model of a Honeybee Colony's Foraging Behaviour
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
A survey of very large-scale neighborhood search techniques
Discrete Applied Mathematics
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
An effective two-stage simulated annealing algorithm for the minimum linear arrangement problem
Computers and Operations Research
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
GRASP and path relinking for the max-min diversity problem
Computers and Operations Research
Computers and Operations Research
A swarm intelligence approach to the quadratic minimum spanning tree problem
Information Sciences: an International Journal
Artificial Bee Colony (ABC) for multi-objective design optimization of composite structures
Applied Soft Computing
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Memetic algorithm for the antibandwidth maximization problem
Journal of Heuristics
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
A memetic algorithm for the cyclic antibandwidth maximization problem
Soft Computing - A Fusion of Foundations, Methodologies and Applications
On the performance of bee algorithms for resource-constrained project scheduling problem
Applied Soft Computing
Information Sciences: an International Journal
A modified artificial bee colony algorithm
Computers and Operations Research
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
Performance assessment of foraging algorithms vs. evolutionary algorithms
Information Sciences: an International Journal
DisABC: A new artificial bee colony algorithm for binary optimization
Applied Soft Computing
A global best artificial bee colony algorithm for global optimization
Journal of Computational and Applied Mathematics
A tutorial for competent memetic algorithms: model, taxonomy, and design issues
IEEE Transactions on Evolutionary Computation
Artificial bee colony programming for symbolic regression
Information Sciences: an International Journal
Variable neighborhood search for the Vertex Separation Problem
Computers and Operations Research
Swarm intelligence approaches to estimate electricity energy demand in Turkey
Knowledge-Based Systems
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In this paper, we propose a hybrid metaheuristic algorithm to solve the cyclic antibandwidth problem. This hard optimization problem consists of embedding an n-vertex graph into the cycle C"n, such that the minimum distance (measured in the cycle) of adjacent vertices is maximized. It constitutes a natural extension of the well-known antibandwidth problem, and can be viewed as the dual problem of the cyclic bandwidth problem. Our method hybridizes the artificial bee colony methodology with tabu search to obtain high-quality solutions in short computational times. Artificial bee colony is a recent swarm intelligence technique based on the intelligent foraging behavior of honeybees. The performance of this algorithm is basically determined by two search strategies, an initialization scheme that is employed to construct initial solutions and a method for generating neighboring solutions. On the other hand, tabu search is an adaptive memory programming methodology introduced in the eighties to solve hard combinatorial optimization problems. Our hybrid approach adapts some elements of both methodologies, artificial bee colony and tabu search, to the cyclic antibandwidth problem. In addition, it incorporates a fast local search procedure to enhance the local intensification capability. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown with respect to the current state-of-the-art algorithm for this problem.