The aim of this paper is to develop a parallel Scatter Search metaheuristic for solving the Feature Subset Selection Problem in classification. Given a set of instances characterized by several features, the classification problem consists of assigning a class to each instance. Feature Subset Selection Problem selects a relevant subset of features from the initial set in order to classify futur...

Many problems that we face nowadays can be expressed as optimization problems. Finding the best solution for real-world instances of such problems is hard or even infeasible. Metaheuristic algorithms have been used for decades to guide the search for satisfactory solutions in hard optimization problems at an affordable cost. However, despite its many benefits, the application of metaheuristics ...

Solving efficiently complex problems using metaheuristics, and in particular local search algorithms, requires incorporating knowledge about the problem to solve. In this paper, the permutation flowshop problem is studied. It is well known that in such problems, several solutions may have the same fitness value. As this neutrality property is an important issue, it should be taken into account ...

Analysts in many areas of national security face a massive (high volume), dynamically changing (high velocity) flood of possibly relevant information. Identifying reasonable suspects confronts a tension between data that is too atomic to be diagnostic and knowledge that is too complex to guide search. DREEM (Dynamic Data Relevance Estimation by Exploring Models) is a knowledge-based metaheurist...

This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE′–HJ, which is a modification of MA–MDE′ in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE′–IHS–HJ, which...

Mass optimization on shape and sizing with multiple natural frequency constraints are highly nonlinear dynamic optimization problems. Multiple natural frequency constraints normally cause difficult dynamic sensitivity analysis and, in addition, two different types of design variables, nodal coordinates and crosssectional areas, often lead to divergence. Thus, the choice of the appropriated meth...

Nature-inspired algorithms such as Particle Swarm Optimization and Firefly Algorithm are among the most powerful algorithms for optimization. In this paper, we intend to formulate a new metaheuristic algorithm by combining Lévy flights with the search strategy via the Firefly Algorithm. Numerical studies and results suggest that the proposed Lévy-flight firefly algorithm is superior to existing...

We call this problem of designing the appropriate metaheuristic problem for a combinatorial (optimization) problem, the metaheuristic tuning problem [1, 3, 7]. Anecdotal evidence suggests that tuning takes a major effort, i.e. [1] states that 90% of the design and testing time can be spent fine-tuning the algorithm. Although it can be easy to come up with a variety of metaheuristics, tuning the...

Main methods, algorithms and applications of the Variable Neighborhood Search metaheuristic are surveyed, in view of a chapter of the Encyclopedia of Optimization.

Fuzzy clustering is a famous unsupervised learning method used to collecting similar data elements within cluster according to some similarity measurement. But, clustering algorithms suffer from some drawbacks. Among the main weakness including, selecting the initial cluster centres and the appropriate clusters number is normally unknown. These weaknesses are considered the most challenging tas...