Since its inception in the early 1980s, we have seen a lot of exciting developments in the field of metaheuristics. The complexity of many real-world problems, which are often associated with large search spaces, real-time performance demands and dynamic environments, has made exact solution methods impractical to solve them within a reasonable amount of time. This gives rise to various types o...

Metaheuristic parallel search methods { tabu search, simulated annealing and genetic algorithms, essentially { are reviewed, classi ed and examined not according to particular methodological characteristics, but following the unifying approach of the level of parallelization. It is hoped that by examining the commonalities among parallel implementations across the eld of metaheuristics, insight...

Define the problem Formulate clearly the problem State the combinatorial hardness of the problem Model the problem and recognize possible similar problems Search in the literature (or the Internet) for: complexity results (is the problem NP-hard?) solution algorithms for the original problem solution algorithms for the simplified problem Design and Implementation Experimentation configuring tun...

To improve the unreasonable distribution of sensors' random deployment and increase network coverage rate, an optimization method of wireless sensor networks coverage based on improved shuffled frog leaping algorithm (ISFLA) was proposed in this paper.During the process of updating the frog, a novel learning strategy is introduced, in which the poor frog learns not only from the best frog of it...

This paper proposes to link the concepts of self-organization and adaptive memory programming (AMP) in the field of optimization metaheuristics. The self-organization is described within the framework of biology and the AMP is defined. The main categories of the metaheuristics using these two concepts are described and uses of the self-organization and the AMP are highlighted. MOTS-CLÉS : optim...

Quadratic assignment problem (QAP) is one of the hardest combinatorial optimization problems which can model many real life problems. Because of its theoretical and practical importance, QAP has attracted attention of many researchers. In this paper, a multi hybrid genetic algorithm for solving QAP is proposed. The key feature of our approach is the hybridization of three metaheuristics, tabu s...

Solution of Abstract Optimization problems with two or more conflicting functions or objectives by using metaheuristics has attracted attention of researches and become a rapidly developing area known as Multiobjective Optimization. Metaheuristics are non-exact techniques aimed to produce satisfactory solutions to complex optimization problems where exact techniques are not applicable; they are...

A metaheuristic is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms (Sörensen and Glover, To appear). Notable examples of metaheuristics include genetic/evolutionary algorithms, tabu search, simulated annealing, and ant colony optimization, although many more exist. A problem-specific implementati...

Complexity is a prevalent feature of numerous natural and artificial systems and as such has attracted much scientific interest in the last decades. The pursuit of computational tools capable of analyzing, modeling or designing systems exhibiting this complex nature –in which the properties of the system are not evident at the bottom level but emerge from its global structure– is a major issue....