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...

Metaheuristics represent an important class of techniques to solve, approximately, hard combinatorial optimization problems for which the use of exact methods is impractical. In this work, we propose a hybrid version of the GRASP metaheuristic, which incorporates a data mining process, to solve the p-median problem. We believe that patterns obtained by a data mining technique, from a set of sub...

The High School Timetabling Problem requires the assignment of times and resources to events, while sets of required and desirable constraints must be considered. The most common approach for this problem is to employ metaheuristic methods. This work presents a matheuristic approach that combines a Variable Neighbourhood Search algorithm with mathematical programming-based neighbourhoods for hi...

This paper introduces a hybrid metaheuristic, the Variable Neighborhood Particle Swarm Optimization (VNPSO), consisting of a combination of the Variable Neighborhood Search (VNS) and Particle Swarm Optimization(PSO). The proposed VNPSO method is used for solving the multi-objective Flexible Job-shop Scheduling Problems (FJSP). The details of implementation for the multi-objective FJSP and the c...

This paper deals with the Permutation Flow Shop scheduling problem with the objective of minimizing total flow time, and therefore reducing in-process inventory. A new hybrid metaheuristic Genetic Algorithm Cluster Search is proposed for the scheduling problem solution. The performance of the proposed method is evaluated and results are compared with the best reported in the literature. Experim...

The Internet of Things (IoT) has subsequently been applied to a variety sectors, including smart grids, farming, weather prediction, power generation, wastewater treatment, and so on. So if the enormous promise in wide range applications, there still are certain areas where it may be improved. Designers had focused our present research on reducing energy consumption devices IoT networks, which ...

Hybridizing metaheuristic approaches becomes a common way to improve the efficiency of optimization methods. Many hybridizations deal with the combination of several optimization methods. In this paper we are interested in another type of hybridization, where datamining approaches are combined within an optimization process. Hence, we propose to study the interest of combining metaheuristics an...

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper has proposed a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired by the whales’ behavior of communicating with each other via ultras...