An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Aquila Optimizer: A novel meta-heuristic optimization algorithm

Moth–flame optimization (MFO) algorithm is a simple and easy to implement, nature-inspired, meta-heuristic algorithm. This chapter covers the introduction to moth flame optimization algorithm, steps for generating moths randomly within the neighbourhood or solution space and solved numerical example are provided. The fitness value for each moth is calculated and the best position obtained is tagged by flame. The updating process takes place after which the process is repeated until a point where criteria for termination is attained. The MFO model was implemented in MATLAB, to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations were set to 100. The best solution obtained by MFO [1.7999 0.19996] and the best optimal value of the objective function found by MFO is 84. MFO algorithm can be used in diverse areas of operation for scheduling, estimation, simulation, and control.

Moths–Flame Optimization Algorithm

Efficient task scheduling is considered as one of the main critical challenges in cloud computing. Task scheduling is an NP-complete problem, so finding the best solution is challenging, particularly for large task sizes. In the cloud computing environment, several tasks may need to be efficiently scheduled on various virtual machines by minimizing makespan and simultaneously maximizing resource utilization. We present a novel hybrid antlion optimization algorithm with elite-based differential evolution for solving multi-objective task scheduling problems in cloud computing environments. In the proposed method, which we refer to as MALO, the multi-objective nature of the problem derives from the need to simultaneously minimize makespan while maximizing resource utilization. The antlion optimization algorithm was enhanced by utilizing elite-based differential evolution as a local search technique to improve its exploitation ability and to avoid getting trapped in local optima. Two experimental series were conducted on synthetic and real trace datasets using the CloudSim tool kit. The results revealed that MALO outperformed other well-known optimization algorithms. MALO converged faster than the other approaches for larger search spaces, making it suitable for large scheduling problems. Finally, the results were analyzed using statistical t-tests, which showed that MALO obtained a significant improvement in the results.

A novel hybrid antlion optimization algorithm for multi-objective task scheduling problems in cloud computing environments

Although the continuous version of ant colony optimizer (ACOR) has been successfully applied to various problems, there is room to boost its stability and improve convergence speed and precision. In addition, it is prone to stagnation, which means it cannot step out of the local optimum (LO). To effectively mitigate these concerns, an improved method using a random spare strategy and chaotic intensification strategy is proposed. Also, its selection mechanism is enhanced in our research. Among the new components, the convergence speed is mainly boosted by using a random spare approach. To effectively augment the ability to step out of LO and to refine the convergence accuracy, the chaotic intensification strategy and improved selection mechanism are applied to ACOR. To better verify the effectiveness of the proposed method, a series of comparative experiments are conducted by using 30 benchmark functions. According to all experimental results, it is evident that the convergence rapidity and accuracy of the proposed method is better than other peers. In addition, it is observed that the capability of enhanced RCACO is more reliable than other techniques in stepping out of LO. Furthermore, an excellent multi-threshold image segmentation method is proposed in this paper. On this basis, image segmentation experiments at low threshold levels and high threshold levels are also respectively carried out. The experimental results also adequately disclose that the segmentation results of RCACO for both multi-threshold image segmentation at a low threshold level and high threshold level, are even more satisfactory compared to other studied algorithms. An online homepage supports this research for access to sharable codes, any question and info about this research at https://aliasgharheidari.com .

Chaotic random spare ant colony optimization for multi-threshold image segmentation of 2D Kapur entropy

This paper thoroughly presents a comprehensive review of the so-called moth–flame optimization (MFO) and analyzes its main characteristics. MFO is considered one of the promising metaheuristic algorithms and successfully applied in various optimization problems in a wide range of fields, such as power and energy systems,
economic dispatch, engineering design, image processing and medical applications. This manuscript describes the available literature on MFO, including its variants and hybridization, the growth of MFO publications, MFO application areas, theoretical analysis and comparisons of MFO with other algorithms.
Conclusions focus on the current work on MFO, highlight its weaknesses, and suggest possible future research directions. Researchers and practitioners of MFO belonging to different fields, like the domains of optimization, medical, engineering, clustering and data mining, among others will benefit from this study.

Moth–flame optimization algorithm: variants and applications

Development of an efficient neural-based segmentation technique for Arabic handwriting recognition

This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm.

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Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

This paper thoroughly introduces a comprehensive review of the so-called Dragonfly algorithm (DA) and highlights its main characteristics. DA is considered one of the promising swarm optimization algorithms because it successfully applied in a wide range of optimization problems in several fields, such as engineering design, medical applications, image processing, power and energy systems, and economic load dispatch problems. The review describes the available literature on DA, including its variants like binary, discrete, modify, and hybridization of DA. Conclusions focus on the current work on DA, highlighting its disadvantages with suggests possible future research directions. Researchers and practitioners of DA belonging to a wide range of audiences from the domains of optimization, engineering, medical, data mining, and clustering, among others will benefit from this study.

Dragonfly algorithm: a comprehensive survey of its results, variants, and applications

Using an efficient neural network for recognition and segmentation will definitely improve the performance and accuracy of the results; in addition to reduce the efforts and costs. This paper investigates and compares between results of four different artificial neural network models. The same algorithm has been applied for all with applying two major techniques, first, neural-segmentation technique, second, apply a new fusion equation. The neural techniques calculate the confidence values for each Prospective Segmentation Points (PSP) using the proposed classifiers in order to recognize the better model, this will enhance the overall recognition results of the handwritten scripts. The fusion equation evaluates each PSP by obtaining a fused value from three neural confidence values. CPU times and accuracies are also reported. Experiments that were performed of classifiers will be compared with each other and with the literature.

Husam Al Hamad

Papers

Moth–flame optimization algorithm: variants and applications

Development of an efficient neural-based segmentation technique for Arabic handwriting recognition

Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering

Dragonfly algorithm: a comprehensive survey of its results, variants, and applications

Use an efficient neural network to improve the Arabic handwriting recognition