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...

Selecting the routes and the assignment of link flow in a computer communication networks are extremely complex combinatorial optimization problems. Metaheuristics, such as genetic or simulated annealing algorithms, are widely applicable heuristic optimization strategies that have shown encouraging results for a large number of difficult combinatorial optimization problems. This paper considers...

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...

Instruction scheduling is an optimization phase aimed at balancing the performance-cost tradeoffs of the design of digital systems. In this paper, a formal framework is tailored in particular to find an optimal solution to the resource-constrained instruction scheduling problem in high-level synthesis. The scheduling problem is formulated as a discrete optimization problem and an efficient popu...

Nature-inspired swarm metaheuristics become one of the most powerful methods for optimization. In discrete optimization, the efficiency of an algorithm depends on how it is adapted to the problem. This paper aims to provide a discretization of the Firefly Algorithm (FF) for the scheduling of a specific manufacturing system, which is the mono processors two-stage hybrid flow shop (HFS). This kin...

Many real world problems can be modelled as an optimization problem. Evolutionary algorithms are used to solve these problems. Ant colony algorithm is a class of evolutionary algorithms that have been inspired of some specific ants looking for food in the nature. These ants leave trail pheromone on the ground to mark good ways that can be followed by other members of the group. Ant colony optim...

The integrative collaboration of genetic algorithms and integer linear programming as specified by the Generate and Solve methodology tries to merge their strong points and has offered significant results when applied to wireless sensor networks domains. The Generate and Solve (GS) methodology is a hybrid approach that combines a metaheuristics component with an exact solver. GS has been recent...

In this paper we describe computer experiments while testing a family of parallel and hybrid metaheuristics against a small set of graph partitioning problems like clustering, partitioning into cliques and coloring. In all cases the search space is composed of vertex partitions satisfying specific problem requirements. The solver application contains two sequential and nine parallel/hybrid algo...

With the name of`Memetic Algorithms' we recognize a class of metaheuristics which constitutes an emerging paradigm for optimization. For more than a decade now it has been applied with success to a large variety of combinatorial and nonlinear problems. In the last four years, and mostly in the last two, several techniques introduced in molecular optimization problems can be characterized in thi...

The paper describes a comparative study of multiple-objective metaheuristics on the bi-objective set covering problem. Ten representative methods based on genetic algorithms, multiple start local search, hybrid genetic algorithms and simulated annealing are evaluated in the computational experiment. Nine of the methods are well known from the literature. The paper introduces also a new hybrid g...