Most Data Mining tasks are performed by the application of Machine Learning techniques. Metaheuristic approaches are becoming very useful for designing efficient tools in Machine Learning. Metaheuristics are general strategies to design efficient heuristic procedures. Scatter Search is a recent metaheuristic that has been successfully applied to solve standard problems in three central paradigm...

This paper proposes a two-phase metaheuristic approach to planning daily farm work for agriculture production corporations. The two-phase metaheuristic contains the optimization of resources assignment and searching schedule based on Genetic Algorithm and hybrid Petri nets model. In the experiment, the effect on optimizing the resource assignment and priority list, initializing population of GA...

This study has developed and evaluated a multiobjective metaheuristic for redistricting to draw territory lines for geographical zones for the purpose of space control. The proposed multiobjective metaheuristic is briefly explained to shows its components and functionality. The redistricting problem definition is discussed, followed by the metaheuristic and the multiobjective decision rules. Th...

We study here the application of a metaheuristic, issued from the noising methods and that we call “descent with mutations”, to a problem arising in the field of the aggregation of symmetric relations: the clique partitioning of a weighted graph. This local search metaheuristic, of which the design is very simple, is compared with another very efficient metaheuristic, which is a simulated annea...

This paper presents a metaheuristic approach based on the Honey Bee Mating Algorithm (HBMA) to the problem of hydropower reservoir optimization. As shortage in power resources becomes more and more pressing, the issue of obtaining more power using less primary energy resources, in general, and less water resources, in particular, becomes an important target for the efficient usage of hydropower...

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning ...

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief hi...

The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also lead to effective algorithms, and if possible, general purpose ones. If we think of a metaheurist...

The metaheuristics are approximation methods which deal with difficult optimization problems. The Work that we present in this paper has primarily as an objective the adaptation and the implementation of two advanced metaheuristics which are the Memetic Algorithms (MA) and the Electromagnetism Metaheuristic (EM) applied in the production systems of Hybrid Flow Shop (HFS) type for the problem of...

We propose GRASP with evolutionary path-relinking, a metaheuristic resulting from the hybridization of GRASP, path-relinking, and evolutionary path-relinking. This metaheuristic is applied to a network migration problem. Experiments show that a GRASP with evolutionary path-relinking heuristic finds solutions faster than a heuristic based on GRASP with path-relinking as well as one based on pure...