Semidefinite programming in combinatorial optimization
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
A branch-and-cut approach to physical mapping with end-probes
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
A Geometric Approach to Betweenness
SIAM Journal on Discrete Mathematics
An Electromagnetism-like Mechanism for Global Optimization
Journal of Global Optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
On the Convergence of a Population-Based Global Optimization Algorithm
Journal of Global Optimization
On random betweenness constraints
FCT'09 Proceedings of the 17th international conference on Fundamentals of computation theory
Applying electromagnetism-like mechanism for feature selection
Information Sciences: an International Journal
Hybrid metaheuristics in combinatorial optimization: A survey
Applied Soft Computing
Circle detection using electro-magnetism optimization
Information Sciences: an International Journal
A note on teaching-learning-based optimization algorithm
Information Sciences: an International Journal
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In this paper we present an electromagnetism (EM) metaheuristic for solving NP hard Maximum Betweenness Problem (MBP). A new encoding scheme with appropriate objective functions is implemented. Specific representation of the individuals enables the EM operators to explore the searching space in a way that achieves high quality solutions. An effective 1-swap based local search procedure improved by the specific caching technique is performed on each EM point. The algorithm is tested both on real and artificial instances from the literature. Experimental results show that the proposed EM approach achieves all previously known optimal solutions, except one, and achieves the best-known solutions or outperforms other approaches on all large-scale instances, except two. Provided statistical analysis indicates that the EM approach is significantly better than other approaches.