In this paper the use of metaheuristics techniques in a parallel computing course is explained. In the practicals of the course different metaheuristics are used in the solution of a mapping problem in which processes are assigned to processors in a heterogeneous environment, with heterogeneity in computation and in the network. The parallelization of the metaheuristics is also considered.

Several constrained and unconstrained optimization problems have been adequately solved over the years thanks to advances in the metaheuristics area. In the last decades, di erent metaheuristics have been proposed employing new ideas, and hybrid algorithms that improve the original metaheuristics have been developed. One of the most successfully employed metaheuristics is the Di erential Evolut...

Metaheuristics have been established as one of the most practical approach to simulation optimization. However, these methods are generally designed for combinatorial optimization, and their implementations do not always adequately account for the presence of simulation noise. Research in simulation optimization, on the other hand, has focused on convergent algorithms, giving rise to the impres...

Over the past few decades, metaheuristics methods have been applied to a large variety of bioinformatic applications. There is a growing interest in applying metaheuristics methods in the analysis of gene sequence and microarray data. Therefore, this review is intend to give a survey of some of the metaheuristics methods to analysis biological data such as gene sequence analysis, molecular 3D s...

Research in metaheuristics has recently evolved to new issues such as : simplicity, robustness and modularity of metaheuristics. Distributed Artificial Intelligence and particularly multiagent systems seem to be a promising field of research to tackle these new issues. In this paper we propose AMF, an Agent Metaheuristic Framework that aims at supporting the design and implementation of metaheu...

Metaheuristics such as Ant Colony Optimization, Evolutionary Computation, Simulated Annealing, Tabu Search and Stochastic Partitioning Methods are introduced, and their recent applications to a wide class of combinatorial optimization problems under uncertainty are reviewed. The flexibility of metaheuristics in being adapted to different modeling approaches and problem formulations emerges clea...

Manifold possibilities of hybridizing individual metaheuristics with each other and/or with algorithms from other fields exist. A large number of publications documents the benefits and great success of such hybrids. This article overviews several popular hybridization approaches and classifies them based on various characteristics. In particular with respect to low-level hybrids of different m...

Many real life optimization problems are nonconvex and may have several local minima within their feasible region. Therefore, global search methods are needed. Metaheuristics are efficient global optimizers including a metastrategy that guides a heuristic search. Genetic algorithms, simulated annealing, tabu search and scatter search are the most well-know metaheuristics. In general, they do no...