Results 321 to 330 of about 151,821 (357)

Constraint handling in Firefly Algorithm

2013 IEEE International Conference on Cybernetics (CYBCO), 2013
Most of the contemporary nature-/bio-inspired techniques are unconstrained algorithms. Their performance may get affected when dealing with the constrained problems. There are number of constraint handling techniques developed for these algorithms. This paper intends to compare the performance of the emerging metaheuristic swarm optimization technique ...
Aditya Milind Deshpande, Gaurav Mohan Phatnani, Anand J. Kulkarni   +2 more
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An Algorithm for Constraint Satisfaction Problem

2017 IEEE 47th International Symposium on Multiple-Valued Logic (ISMVL), 2017
Many natural combinatorial problems can be expressed as constraint satisfaction problems. This class of problems is known to be NP-complete in general, but certain restrictions on theform of the constraints can ensure tractability. The standard way to parameterize interesting subclasses of the constraint satisfaction problem is via finite constraint ...
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On Algorithms for Decomposable Constraints

2002
Non-binary constraints are present in many real-world constraint satisfaction problems. Certain classes of these constraints, like the all-different constraint, are "decomposable". That is, they can be represented by binary constraints on the same set of variables.
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An Elimination Algorithm for Functional Constraints

2008
Functional constraints are studied in Constraint Satisfaction Problems (CSP) using consistency concepts (e.g., [1,4]). In this paper, we propose a new method --- variable substitution --- to process functional constraints. The idea is that if a constraint is functional on a variable, this variable in another constraint can be substituted using the ...
Yuanlin Zhang 0002, Roland H. C. Yap, Chendong Li, Satyanarayana Marisetti   +3 more
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Algorithmic Granularity with Constraints

2013
We introduce a notion of algorithmic underspecification in the formal language of acyclic recursion. By this concept of underspecification, we represent denotational ambiguity via algorithmic underspecification. Then we introduce two kinds of constraints on possible specifications of underspecified algorithms (1) general acyclicity constraints, and (2)
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Shipment Routing Algorithms with Tree Constraints

Transportation Science, 1992
Routing shipments efficiently on less-than-truckload trucking networks represents an important subproblem of the general network design problem that arises when designing a service network. The objective of the LTL shipment routing problem is to minimize the total transportation and handling costs subject to two key constraints: (i) service between ...
Warren B. Powell, Ioannis A. Koskosidis
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An Algorithm for Determining Related Constraints

2002
Constraints are a class of business rules that many organisations implement in their information systems. However, it is common that many implemented constraints do not get documented. This has led researchers to consider how to recover constraints from implementations.
Fu, Gaihua, Shao, Jianhua, Embury, Suzanne M; id_orcid 0000-0002-3711-0778, Gray, W A   +3 more
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Constraint consistent genetic algorithms

Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97), 2002
It has commonly been acknowledged that solving constrained problems with a variety of complex constraints is a challenging task for genetic algorithms (GA). Existing methods to handle constraints in GA are often computationally expensive, problem dependent or constraint specific.
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Algorithmic Mechanism Design and Constraints

2003
Ensuring truthfulness amongst self-interested agents who are bidding against one another in an auction is computationally expensive. The Vickrey-Clarke-Groves (VCG) mechanism guarantees that each of the agent's dominant strategy is to tell the truth, but it requires solving n+1 optimisation problems for n agents.
Alan Holland, Barry O'Sullivan
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Constraint decomposition algorithms in global optimization

Journal of Global Optimization, 1994
An objective function is supposed linear. The variable vector is separated into two subvectors \(x\) and \(y\) and it is supposed that \((x, y)\) belongs to a convex set, \(x\) and \(y\) belong to the polytopes, \(y\) belongs to the complement of an open convex set. The problem is decomposed in such a way that global optimization is performed only in \(
Reiner Horst, Nguyen V. Thoai
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