Results 211 to 220 of about 17,999 (266)
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Parameter tuning for meta-heuristics
Knowledge-Based Systems, 2020Abstract These days meta-heuristic algorithms are gaining lot of popularity. The performance of the meta-heuristics depends upon the suitable selection of user dependent parameters. Finding the most suitable values for the parameters (fine tuning) is a challenging problem.
Susheel Kumar Joshi +1 more
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Affine invariance of meta-heuristic algorithms
Information Sciences, 2021Abstract An algorithm whose performance depends on the objective function being aligned with a privileged coordinate system is a poor choice in general because it is unlikely that the optimal orientation will be known in advance. In this paper, a property of meta-heuristic algorithms, named affine invariance, is introduced to verify whether the ...
ZhongQuan Jian, GuangYu Zhu 0005
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Meta-heuristic optimization reloaded
2011 Third World Congress on Nature and Biologically Inspired Computing, 2011We consider the meta-heuristic approach to optimization as to be performed in four stages (model, optimality, algorithm, verification), and point out the potential of varying the optimality stage, in contrary to the design of new algorithms. Thus, we can also apply the meta-heuristic approach to optimization to the task of fair distribution of ...
Mario Köppen +2 more
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An electromagnetic meta-heuristic for the nurse scheduling problem [PDF]
Journal of Heuristics, 2007 In this paper, we present a novel meta-heuristic technique for the nurse scheduling problem (NSP). This well-known scheduling problem assigns nurses to shifts per day maximizing the overall quality of the roster while taking various constraints into account. The problem is known to be NP-hard.
Broos Maenhout, Mario Vanhoucke
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Meta heuristics for prime factorization problem
2013 World Congress on Nature and Biologically Inspired Computing, 2013Generally, cryptographic algorithms are based on NP-Complete problems like prime factorization, discrete logarithm etc. The difficulties of RSA and Rabin cryptographic algorithms are based on prime factorization problem. Prime factorization problem may be modeled as a Non-uniform discrete optimization problem.
Pranav Dass +3 more
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Meta-heuristic approach to proportional fairness
Evolutionary Intelligence, 2012Proportional fairness is a concept from resource sharing tasks among n users, where each user receives at least 1/n of her or his total value of the infinitely divisible resource. Here we provide an approach to proportional fairness that allows its extension to discrete domains, as well as for the direct application of evolutionary computation to ...
Mario Köppen +3 more
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A Meta-heuristic for Subset Problems
2001In constraint solvers, variable and value ordering heuristics are used to finetune the performance of the underlying search and propagation algorithms. However, few guidelines have been proposed for when to choose what heuristic among the wealth of existing ones.
Pierre Flener +2 more
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Meta-heuristic bus transportation algorithm
Iran Journal of Computer Science, 2018Over recent decades, several experience-based mathematical models have been proposed. In addition to collective intelligence, in recent years some efforts have been made to apply human experience-based intelligence to open up a new world of possibilities to design new meta-heuristic algorithms for solving NP problems.
Mohammad Bodaghi, Koosha Samieefar
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Dynamic Problems and Nature Inspired Meta-Heuristics
2006 Second IEEE International Conference on e-Science and Grid Computing (e-Science'06), 2006Biological systems are, by their very nature, adaptive. However, the meta-heuristic search algorithms inspired by them have mainly been applied to static problems (i.e., problems that do not change while they are being solved). Recently, a greater body of work has been completed on the newer meta-heuristics, particularly ant colony optimisation ...
Tim Hendtlass +2 more
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Ant colony optimization: a new meta-heuristic
Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), 2003Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic.
Marco Dorigo, Gianni Di Caro
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