Special issue on “Metaheuristics”

In the last decades the field of metaheuristics has grown considerably. From the technical point of view as well as from the application-oriented side, these optimization tools have established their value in a remarkable success story. Researchers have demonstrated the ability of these methods to solve hard combinatorial optimization problems of practical sizes within reasonable computational time. This special issue of the Journal of Mathematical Modelling and Algorithms (JMMA) contains selected articles presented at the META’06 conference held in November 2–4, 2006, in Hammamet, Tunisia. The articles are original research papers that describe recent advance in the use of metaheuristics to solve both academic problems (MAX-SAT, knapsack, k-coloring, ...) and real life applications. D. Boughaci, B. Banhamou, H. Drias propose scatter search variant and two genetic algorithms for MAX-SAT problems. The approach profits from both exploration power of genetic algorithm and intensification capability of the SLS. They show that an hybrid genetic algorithm with stochastic local search and a new selection strategy outperforms classical algorithms. Competitive results are shown by H. Bouziri , K. Mellouli and E-G. Talbi in their work on the k-coloring problem. They develop a cooperation between three agents: the search agent, the intensification agent and the diversification agent. They test their method on several instances extracted from the second DIMACS challenge. C. Wilbaut and S. Hanafi introduce a new relaxation-based diversification generator in a scatter search algorithm for the 0–1 multidimensional knapsack problem. The results obtained on a classic set of correlated instances show that the proposed algorithm outperforms other population-based methods for solving the multidimensional knapsack problem.