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 major advantage of using a Meta heuristic algorithm is that it performs optimization.
The speed and accuracy of a meta heuristic algorithm can be boosted up by incorporating the parallelization capability and utilizing the multi core CPUs and the many core GPUs, that have become a de facto in the current hardware specifications.
The remainder of this paper is structured as follows; section II provides the review of literature, section III provides an overview of the available data mining techniques, section IV discusses the importance of meta heuristics for data mining, section V presents a brief overview of the concept of error existing in evolutionary algorithms, section VI discusses the concept of PSO, section VII discusses the mode of usage and advantages in using PSO for classification, section VIII compares PSO with other meta heuristic approaches, section IX discusses PSO's flexibility towards hybridization, section X presents the results and discussion and section XI concludes the study.
It has been discussed earlier that meta heuristic evolutionary algorithms provide near optimal solutions at the stipulated time.
On close inspection, it can be found that all the meta heuristic evolutionary algorithms have a component or mechanism built into it that introduces the much needed error into the system.
Hence analysis is to be carried out in terms of performance to determine their applicability in case of meta heuristic evolutionary algorithms.
This section gives a comparison of the meta heuristic approaches with PSO and the motive for the authors to choose PSO over other algorithms.
A greedy algorithm is a meta heuristic algorithm when it makes decisions for each step based on what seems best at the current step.
Interest in meta-heuristics has generated the development of hybrid approaches [8] and recent significant advances have combined meta heuristics with other problem solving paradigms and improved their use in important application areas [23].
One reason is that many developers are afraid of dealing with complex multiple vehicle routing algorithms as this requires in depth experience with meta heuristics, which is of course not the primary focus of modern software engineers," Vincent Mayer says, (DNA's responsible engineer for application integration).
Topics include linear, nonlinear, queuing, decision analysis, meta heuristics, robust optimization, and large-scale networks.