Examples of 'metaheuristics' in a sentence
Meaning of "metaheuristics"
Metaheuristic is a high-level problem-solving method designed to find near-optimal solutions efficiently. It is commonly used in optimization and search problems
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- plural of metaheuristic
How to use "metaheuristics" in a sentence
Basic
Advanced
metaheuristics
Evolutionary computation metaheuristics such as genetic algorithms.
These methods explore several existing metaheuristics.
Metaheuristics for the vehicle routing.
The problems are solved by exact methods as well as metaheuristics.
Metaheuristics will be useful if you know what you.
It focuses on a measurement approach to tune the parameters of the metaheuristics.
The use of metaheuristics is justified by the nonconvexity of the problem.
A trend in combinatorial optimization research has been the exploration of hybrid metaheuristics.
Constructive metaheuristics construct solutions from their constituting parts.
This work aims to present an autonomous exploration algorithm based on metaheuristics.
The metaheuristics have an important role in solving various optimization problems.
Different solving approaches based on hybrid metaheuristics and dynamic programming are proposed.
Metaheuristics sample a set of solutions which is too large to be completely sampled.
This thesis proposes various metaheuristics for solving the two packing problems.
Metaheuristics are a high level procedure designed to solve hard optimization problems.
See also
This is a survey of the application of feature selection metaheuristics lately used in the literature.
Metaheuristics in combinatorial optimization.
The second approach describe these metaheuristics as some methods manipulating a sampling of a probability density.
Metaheuristics are often applied to problems for which no efficient solution algorithm is known.
This PhD thesis proposed to solve the deterministic and stochastic optimization problems with metaheuristics.
The research on metaheuristics remains an attractive area and receives growing attention.
His research in LISSI laboratory focused on the design of Metaheuristics to medical image processing.
Multiobjective metaheuristics provide particularly effective means of addressing this class of problems.
However, this is not the case for metaheuristics.
The application of metaheuristics on real problems with constraints is not possible without adjustments.
In this thesis, this special representation is adapted to metaheuristics.
Metaheuristics are also widely used for jobshop scheduling and job selection problems.
In order to generate the reference solutions, four metaheuristics were developed.
Metaheuristics have been extremely successful in a wide variety of fields and demonstrate significant results.
A detailed explanation of heuristics and metaheuristics can be found in, and.
Metaheuristics are generic approaches to finding solutions with good quality for several problems.
However, the development of efficient metaheuristics results in a complex engineering process.
Metaheuristics represent an important class of approximative algorithms for solving np-hard problems.
Next, some cooperative approaches combining metaheuristics for multiobjective optimization are proposed.
Brute-force search is also useful as a baseline method when benchmarking other algorithms or metaheuristics.
As many algorithms, metaheuristics expose many parameters that significantly impact their performance.
In addition, tabu search is sometimes combined with other metaheuristics to create hybrid methods.
Single based metaheuristics and a population based metaheuristic, the particle swarm optimization, are used.
Two major approaches are traditionally used to tackle these problems, exact methods and metaheuristics.
Among partial search algorithms, metaheuristics are generic algorithms, widely studied in the literature.
There is a lineage of models that incorporate higher-level algorithmic constructs: Metaheuristics.
Because of this, many heuristics and metaheuristics have been developed through the time.
Such metaheuristics include simulated annealing, evolutionary algorithms, ant colony optimization and particle swarm optimization.
Matheuristics are optimization algorithms made by the interoperation of metaheuristics and mathematical programming ( MP ) techniques.
However, metaheuristics such as DE do not guarantee an optimal solution is ever found.
These are properties that characterize most metaheuristics, Metaheuristics are strategies that guide the search process.
Metaheuristics are not problem-specific.
They proposed these metaheuristics as a "research-based model.
Metaheuristics - the metaphor exposed.
Heuristics and Metaheuristics Trajectory-based methods, hill climbing, tabu search, simulated annealing.
