the tabu search is described. An illustrative example is given in section 4. The last section will be devoted to the presentation of some results and some conclusions relating to this research work. CHAPTER 2 LITERATURE SURVEY Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. Metaheuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic starting from a null solution and adding elements to build a good complete one, or a
the tabu search is described. An illustrative example is given in section 4. The last section will be devoted to the presentation of some results and some conclusions relating to this research work. CHAPTER 2 LITERATURE SURVEY Ant Colony Optimization (ACO) is a paradigm for designing metaheuristic algorithms for combinatorial optimization problems. The first algorithm which can be classified within this framework was presented in 1991 and, since then, many diverse variants of the basic principle have been reported in the literature. The essential trait of ACO algorithms is the combination of a priori information about the structure of a promising solution with a posteriori information about the structure of previously obtained good solutions. Metaheuristic algorithms are algorithms which, in order to escape from local optima, drive some basic heuristic: either a constructive heuristic starting from a null solution and adding elements to build a good complete one, or a
ANT_COLONY_OPTIMISATION_APPLIED_TO_JOB_SHOP_SCHEDULING_PROBLEM.docx