This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in eng...

Software defect prediction has been an important research topic in the software engineering field, especially to solve the inefficiency and ineffectiveness of existing industrial approach of software testing and reviews. The software defect prediction performance decreases significantly because the data set contains noisy attributes and class imbalance. Feature selection is generally used in ma...

Erklärung Hiermit erkläre ich an Eides statt, dass ich die vorliegende Arbeit selbstständig und ohne fremde Hilfe verfasst, andere als die angegebenen Quellen und Hilfsmit-tel nicht benutzt und die aus anderen Quellen entnommenen Stellen als solche gekennzeichnet habe. Abstract 1 Kurzfassung 2 1 Introduction 3 1.

Today, planning and scheduling problems are the most significant issues in world make a great impact on improving organizational productivity serving systems such as medical healthcare providers. Since operating room is major problem organizations, optimization of staff equipment plays an essential role. Thus, this study presents multi-objective mathematical model with new categorization (preop...

High power consumption efficiency in wireless sensor networks is always desirable. One way to deal with this issue is using a linear integer programming model based upon a schedule of sensor allocation plans in multiple time intervals subject to coverage and connectivity constraints. The Generate-and-Solve (GS) methodology is a hybrid approach that combines a metaheuristic component with an exa...

Given the NP-Hard nature of many optimization problems, it is often impractical to obtain optimal solutions to large-scale problems in reasonable computing time. For this reason, heuristic and metaheuristic search approaches are used to obtain good solutions fast. However, these techniques often struggle to develop a good balance between local and global search. In this paper we propose a hybri...

We investigate two matheuristic strategies using the periodic vehicle routing problem with time windows as a testbed. Two different metaheuristics are suitably combined with parts of a developed column generation approach: On the one hand a variable neighborhood search (VNS) acts as the sole provider of columns for a set covering model, hence realizing a pure metaheuristic column generation. He...

This paper presents an approach for the automated cryptanalysis of substitution ciphers based on a recent evolutionary metaheuristic called Scatter Search. It is a population-based metaheuristic founded on a formulation proposed two decades ago by Fred Glover. It uses linear combinations on a population subsets to create new solutions while other evolutionary approaches like genetic algorithms ...

Optimization problems in design engineering are complex by nature, often because of the involvement of critical objective functions accompanied by a number of rigid constraints associated with the products involved. One such problem is Economic Load Dispatch (ED) problem which focuses on the optimization of the fuel cost while satisfying some system constraints. Classical optimization algorithm...

Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in...