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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2007.02.055 Byline: Tiente Hsu (a), Ouajdi Korbaa (b), Remy Dupas (a), Gilles Goncalves (a) Keywords: Flexible manufacturing systems; Cyclic scheduling; Genetic algorithm; Petri nets Abstract: This paper concerns the domain of flexible manufacturing systems (FMS) and focuses on the scheduling problems encountered in these systems. We have chosen the cyclic behaviour to study this problem, to reduce its complexity. This cyclic scheduling problem, whose complexity is NP-hard in the general case, aims to minimise the work in process (WIP) to satisfy economic constraints. We first recall and discuss the best known cyclic scheduling heuristics. Then, we present a two-step resolution approach. In the first step, a performance analysis is carried out; it is based on the Petri net modelling of the production process. This analysis resolves some indeterminism due to the system's flexibility and allows a lower bound of the WIP to be obtained. In the second step, after a formal model of the scheduling problem has been given, we describe a genetic algorithm approach to find a schedule which can reach the optimal production speed while minimizing the WIP. Finally, our genetic approach is validated and compared with known heuristics on a set of test problems. Author Affiliation: (a) Laboratoire de Genie Informatique et Automatique de l'Artois (L.G.I.2.A), Universite d'Artois Faculte des Sciences Appliquees, Technoparc Zone Futura BP 819, 62400 Bethune, France (b) Institut Superieur d'Informatique et des Technologies de Communication de Hammam Sousse, 5 bis rue 01 Juin 1955, 4011 Hammam Sousse, Tunisia Article History: Received 27 January 2004; Accepted 1 February 2007

Chaos Game Optimization Algorithm (CGO) is a novel advancement in metaheuristic optimization inspired by chaos theory. It addresses complex optimization problems in dynamical systems, exhibiting unique behaviours such as fractals and self-organized patterns. CGO's design exemplifies adaptability and robustness, making it a significant tool for tackling intricate optimization scenarios. This study presents a comprehensive and updated overview of CGO, exploring the various variants and adaptations that have been published in numerous research studies since its introduction in 2020, with 4% in book chapters, 7% in international conference proceedings, and 89% in prestigious international journals. CGO variants covered in this paper include 4% binary, 22% for multi-objective and modification and 52% for hybridization variants. Moreover, the applications of CGO, demonstrate its efficacy and flexibility across different domains with 32% in energy, 28% in engineering, 11% in IoT and machine learning, 6% in truss structures, 4% in big data, 2% in medical imaging, in security, in electronic, and in microarray technology. Furthermore, we discuss the future directions of CGO, hypothesizing its potential advancements and broader implications in optimization theory and practice. The primary objectives of this survey paper are to provide a comprehensive overview of CGO, highlighting its innovative approach, discussing its variants and their usage in different sectors, and the burgeoning interest it has sparked in metaheuristic algorithms. As a result, this manuscript is expected to offer valuable insights for engineers, professionals across different sectors, and academic researchers. [ABSTRACT FROM AUTHOR]

Image processing is a crucial effort to discover suspicious regions and robust features from magnetic resonance imaging (MRI). Many segmentation algorithms have been proposed to increase the accuracy of brain tumor detection. However, brain image segmentation is considered a complex and challenging step in medical image processing. K-means is an unsupervised learning method for MRI image clustering and other tasks such as image segmentation and pattern identification of interest. However, the K-means algorithm still has many shortcomings, including a low convergence rate, a tendency to get stuck in local minima, and sensitivity to initialization. An improved version of Aquila Optimizer (AO) boosted with an alternative selection scheme and Cauchy mutation, called Mutated Aquila Optimizer (MAO), and the K-Means algorithm were combined to create a new dynamic and intelligent clustering method for brain tumor segmentation. The proposed MAO-Kmeans approach aims to increase the ability of K-Means to automatically extract the correct number and location of cluster centers and the number of pixels in each cluster in abnormal MRI images (multiple sclerosis lesions). The experimental results on the Brain Tumor Segmentation dataset (BraTS 2020) demonstrated the effectiveness of the MAO-Kmeans method in improving the performance of conventional K-means clustering. Furthermore, qualitative and quantitative comparisons between MAO-Kmeans and the other 12 state-of-the-art segmentation techniques (which include machine learning, artificial neural network, and deep learning-based methods) demonstrate the superiority of our proposed algorithm. • This work proposed an MAO-Kmeans approach for the segmentation of brain tumors. • MAO is the AO variant embedded with Cauchy mutation and truncation selection schemes. • The initial performance of the MAO is tested and verified on CEC'21 benchmarks. • MAO-Kmeans approach is tested for brain tumor segmentation on BraTS 2020 dataset. • Quantitative and qualitative analysis shows the superiority of the MAO-Kmeans method. [ABSTRACT FROM AUTHOR]

The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets, which presents a challenging task for machine learning or pattern recognition applications due to the large and complex search space involved. This paper provides an in-depth examination of nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms, as they have drawn significant interest from the feature selection community due to their potential for global search and simplicity. An analysis of various advanced approach types, along with their advantages and disadvantages, is presented in this study, with the goal of highlighting important issues and unanswered questions in the literature. The article provides advice for conducting future research more effectively to benefit this field of study, including guidance on identifying appropriate approaches to use in different scenarios. [ABSTRACT FROM AUTHOR]