Special Issue "Recent Advances in Metaheuristic Techniques and Their Real-World Applications"

Submission Deadline: 25 August 2021 (closed)
Guest Editors
Dr. Dilbag Singh, Manipal University Jaipur, India.
Prof. Kehui Sun, Central South University, China.
Prof. Robertas Damaševičius, Silesian University of Technology, Poland.
Dr. Ahmed A. Abd El-Latif, Menoufia University, Egypt.


Metaheuristic techniques are extensively utilized to solve many complex and real-world problems. These techniques do not provide an exact solution, but only an estimated result in feasible time. Recently, many researchers have utilized these techniques for solving the various artificial intelligence (AI) enabled applications. These techniques are generally used in two different ways to improve the AI applications. In first case, these techniques can help for evaluating the potential features from the pool of features of a given machine learning/ deep learning problem. It can improve the performance and computation speed of the given machine learning/ deep learning models. In second approach, metaheuristic techniques are widely accepted to resolve the hyper-parameters tuning issue with the most of machine learning and deep learning models. Even metaheuristic techniques are accepted as a hyper-parameters tuning tool for various kind of other techniques such as chaotic map, deep generative models, fuzzy logic, chaotic maps, etc. Therefore, the metaheuristic techniques have their own importance in various fields of computational sciences. However, most metaheuristic techniques suffer from the pre-mature convergence, stuck in local optima, poor convergence speed, etc. kind of issues.

The purpose of this special issue is to demonstrate the new development and application of metaheuristic techniques. The goal is to promote research and development of metathetic based techniques for real-time applications by publishing high-quality research papers in this interdisciplinary field that can profoundly impact the future of the metaheuristic techniques. Potential topics include, but are not limited to:


• Evolutionary approaches

• Nature inspired optimization techniques

• Swarm intelligence

• Hybrid metaheuristic techniques

• Tuning of hyper-parameters using metaheuristic techniques

• Feature selection using metaheuristic techniques

• Metaheuristic techniques based machine learning models

• Metaheuristic techniques based deep learning models

• Metaheuristic techniques based explainable AI models

• Metaheuristic techniques based real-time applications

Deep learning, Metaheuristic, Explainable AI, Real-time applications

Published Papers
  • ATS: A Novel Time-Sharing CPU Scheduling Algorithm Based on Features Similarities
  • Abstract Minimizing time cost in time-shared operating systems is considered basic and essential task, and it is the most significant goal for the researchers who interested in CPU scheduling algorithms. Waiting time, turnaround time, and number of context switches are the most time cost criteria used to compare between CPU scheduling algorithms. CPU scheduling algorithms are divided into non-preemptive and preemptive. Round Robin (RR) algorithm is the most famous as it is the basis for all the algorithms used in time-sharing. In this paper, the authors proposed a novel CPU scheduling algorithm based on RR. The proposed algorithm is called Adjustable… More
  •   Views:164       Downloads:203        Download PDF

  • Extremal Coalitions for Influence Games Through Swarm Intelligence-Based Methods
  • Abstract An influence game is a simple game represented over an influence graph (i.e., a labeled, weighted graph) on which the influence spread phenomenon is exerted. Influence games allow applying different properties and parameters coming from cooperative game theory to the contexts of social network analysis, decision-systems, voting systems, and collective behavior. The exact calculation of several of these properties and parameters is computationally hard, even for a small number of players. Two examples of these parameters are the length and the width of a game. The length of a game is the size of its smaller winning coalition, while the… More
  •   Views:146       Downloads:220        Download PDF

  • Semantic Information Extraction from Multi-Corpora Using Deep Learning
  • Abstract Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity… More
  •   Views:81       Downloads:48        Download PDF

  • Position Control of Flexible Joint Carts Using Adaptive Generalized Dynamics Inversion
  • Abstract

    This paper presents the design and implementation of Adaptive Generalized Dynamic Inversion (AGDI) to track the position of a Linear Flexible Joint Cart (LFJC) system along with vibration suppression of the flexible joint. The proposed AGDI control law will be comprised of two control elements. The baseline (continuous) control law is based on principle of conventional GDI approach and is established by prescribing the constraint dynamics of controlled state variables that reflect the control objectives. The control law is realized by inverting the prescribed dynamics using dynamically scaled Moore-Penrose generalized inversion. To boost the robust attributes against system nonlinearities, parametric… More

  •   Views:65       Downloads:49        Download PDF

  • Fractional Order Linear Active Disturbance Rejection Control for Linear Flexible Joint System
  • Abstract A linear flexible joint system using fractional order linear active disturbance rejection control is studied in this paper. With this control scheme, the performance against disturbances, uncertainties, and attenuation is enhanced. Linear active disturbance rejection control (LADRC) is mainly based on an extended state observer (ESO) technology. A fractional integral (FOI) action is combined with the LADRC technique which proposes a hybrid control scheme like FO-LADRC. Incorporating this FOI action improves the robustness of the standard LADRC. The set-point tracking of the proposed FO-LADRC scheme is designed by Bode's ideal transfer function (BITF) based robust closed-loop concept, an appropriate pole… More
  •   Views:53       Downloads:39        Download PDF

  • MLA: A New Mutated Leader Algorithm for Solving Optimization Problems
  • Abstract Optimization plays an effective role in various disciplines of science and engineering. Optimization problems should either be optimized using the appropriate method (i.e., minimization or maximization). Optimization algorithms are one of the efficient and effective methods in providing quasi-optimal solutions for these type of problems. In this study, a new algorithm called the Mutated Leader Algorithm (MLA) is presented. The main idea in the proposed MLA is to update the members of the algorithm population in the search space based on the guidance of a mutated leader. In addition to information about the best member of the population, the mutated… More
  •   Views:60       Downloads:50        Download PDF

  • Reactions’ Descriptors Selection and Yield Estimation Using Metaheuristic Algorithms and Voting Ensemble
  • Abstract Bioactive compounds in plants, which can be synthesized using N-arylation methods such as the Buchwald-Hartwig reaction, are essential in drug discovery for their pharmacological effects. Important descriptors are necessary for the estimation of yields in these reactions. This study explores ten metaheuristic algorithms for descriptor selection and model a voting ensemble for evaluation. The algorithms were evaluated based on computational time and the number of selected descriptors. Analyses show that robust performance is obtained with more descriptors, compared to cases where fewer descriptors are selected. The essential descriptor was deduced based on the frequency of occurrence within the 50 extracted… More
  •   Views:54       Downloads:41        Download PDF

  • HARTIV: Human Activity Recognition Using Temporal Information in Videos
  • Abstract Nowadays, the most challenging and important problem of computer vision is to detect human activities and recognize the same with temporal information from video data. The video datasets are generated using cameras available in various devices that can be in a static or dynamic position and are referred to as untrimmed videos. Smarter monitoring is a historical necessity in which commonly occurring, regular, and out-of-the-ordinary activities can be automatically identified using intelligence systems and computer vision technology. In a long video, human activity may be present anywhere in the video. There can be a single or multiple human activities present… More
  •   Views:127       Downloads:114        Download PDF

  • Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank
  • Abstract One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for… More
  •   Views:142       Downloads:103        Download PDF