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The Set Covering Problem (SCP) is one of the classical Karp’s 21 NP-complete problems. Although this is a traditional optimization problem, we find many papers assuming metaheuristics for solving the SCP in the current literature. However, while the SCP is a discrete problem, most metaheuristics are defined for solving continuous optimization problems, specially Swarm Intelligence Algorithms (SIAs). Hence, such algorithms should be adapted for working on the discrete scope, but most authors did not perform any study to select a concrete binarization approach. This situation might lead to the conclusion that selecting a concrete binarization technique does not influence the behavior of the algorithm, but rather the general approach of the metaheuristic. This circumstance led us to write this paper focusing on the inherent difficulty in binarization of metaheuristics designed for continuous optimization, when solving a discrete optimization problem, concretely the SCP. To this end, we consider a recent SIA inspired by the behavior of cats and adapted to the discrete scope, which is called Binary Cat Swarm Optimization (BCSO). We replace the binarization technique assumed in the original BCSO by forty different approaches from the current literature. The results obtained while solving a standard SCP benchmark are analyzed through a widely accepted statistical method, concluding that it is crucial to select an adequate binarization approach to ensure that the solving algorithm reaches its full potential. Thus, the task of adapting a metaheuristic to the discrete scope is not a simple matter and should be carefully studied. To this end and as a result of this study, we give some recommendations to perform this task. [ABSTRACT FROM AUTHOR]

The Software Project Scheduling Problem is a specific Project Scheduling Problem present in many industrial and academic areas. This problem consists in making the appropriate worker-task assignment in a software project so the cost and duration of the project are minimized. We present the design of a Max–Min Ant System algorithm using the Hyper-Cube framework to solve it. This framework improves the performance of the algorithm. We illustrate experimental results and compare with other techniques demonstrating the feasibility and robustness of the approach, while reaching competitive solutions. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose a new hybrid AC3-tabu search algorithm for Sudoku problems. [•] We perform experiments considering easy, medium, and hard Sudokus. [•] The approach generally outperforms the best results reached by incomplete search. [ABSTRACT FROM AUTHOR]

Abstract: A Constraint Satisfaction Problem is defined by a set of variables and a set of constraints, each variable has a nonempty domain of possible values. Each constraint involves some subset of the variables and specifies the allowable combinations of values for that subset. A solution of the problem is defined by an assignment of values to some or all of the variables that does not violate any constraints. To solve an instance, a search tree is created and each node in the tree represents a variable of the instance. The order in which the variables are selected for instantiation changes the form of the search tree and affects the cost of finding a solution. In this paper we explore the use of a Choice Function to dynamically select from a set of variable ordering heuristics the one that best matches the current problem state in order to show an acceptable performance over a wide range of instances. The Choice Function is defined as a weighted sum of process indicators expressing the recent improvement produced by the heuristic recently used. The weights are determined by a Particle Swarm Optimization algorithm in a multilevel approach. We report results where our combination of strategies outperforms the use of individual strategies. [Copyright &y& Elsevier]

Abstract: Cell formation consists in organizing a plant as a set of cells, each of them containing machines that process similar types or families of parts. The idea is to minimize the part flow among cells in order to reduce costs and increase productivity. The literature presents different approaches devoted to solve this problem, which are mainly based on mathematical programming and on evolutionary computing. Mathematical programming can guarantee a global optimal solution, however at a higher computational cost than an evolutionary algorithm, which can assure a good enough optimum in a fixed amount of time. In this paper, we model and solve this problem by using state-of-the-art constraint programming (CP) techniques and Boolean satisfiability (SAT) technology. We present different experimental results that demonstrate the efficiency of the proposed optimization models. Indeed, CP and SAT implementations are able to reach the global optima in all tested instances and in competitive runtime. [Copyright &y& Elsevier]