metaheuristic


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metaheuristic

(algorithm, complexity, computability)
A top-level general strategy which guides other heuristics to search for feasible solutions in domains where the task is hard.

Metaheuristics have been most generally applied to problems classified as NP-Hard or NP-Complete by the theory of computational complexity. However, metaheuristics would also be applied to other combinatorial optimisation problems for which it is known that a polynomial-time solution exists but is not practical.

Examples of metaheuristics are Tabu Search, simulated annealing, genetic algorithms and memetic algorithms.
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The shortcomings of both mathematical and heuristic methods encouraged many researchers to study metaheuristic approaches in order to find more reliable optimization alternatives in solving resource leveling problems.
PESHAWAR -- A PhD Research Scholar Sadiq Akbar has submitted thesis on 'Parameter Estimation of Electromagnetic Plane Waves Using Novel Adoptive Metaheuristic Techniques' to the University of Peshawar for award of degree of Doctor of Philosophy (Ph.D) in Electronics.
Section 3 introduces the Differential Evolution metaheuristic and the parallel versions we have implemented.
The findings based on multiple regression model verified that the metaheuristic algorithms produced that more accurate and faster results for estimation of excitation current than other existing approaches.
The use of metaheuristics is a good alternative to tackle this problem as can be swarm intelligence continuous metaheuristic.
Task scheduling problems in the cloud have been tackled using heuristic and metaheuristic algorithms.
Ant Colony Optimization (ACO) is a metaheuristic optimization based on a probabilistic technique, proposed by Marco Dorigo in his PhD thesis in 1992 [21].
They also consider some special topics, among them local anesthetics classification: artificial intelligence information entropy, and applying an artificial neural network and metaheuristic algorithm in applied chemistry and chemical engineering.
We can also see that a lot of researchers use simulation environment to create and test new metaheuristic approaches such as Shuffled frog-leaping algorithm [11,12], Intelligent water drops algorithm [13-15], Cloud-Entropy enhanced genetic algorithm [16] and so on [17].
Therefore, two metaheuristic algorithms of GA and ACO have been proposed for its solution.
Here, various metaheuristic optimization [1] algorithms prove to be quite successful.