Yongxin Li¹, Guoyun Zhou¹, Tao Chang^1,*, Liming Yang², Fenghe Wu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 499-511, 2022, DOI:10.32604/cmes.2022.017630 - 29 November 2021

Abstract Because of descriptive nonlinearity and computational inefficiency, topology optimization with fatigue life under aperiodic loads has developed slowly. A fatigue constraint topology optimization method based on bidirectional evolutionary structural optimization (BESO) under an aperiodic load is proposed in this paper. In view of the severe nonlinearity of fatigue damage with respect to design variables, effective stress cycles are extracted through transient dynamic analysis. Based on the Miner cumulative damage theory and life requirements, a fatigue constraint is first quantified and then transformed into a stress problem. Then, a normalized termination criterion is proposed by approximate More >

Wei-Cheng Wang, Chow-Yong Ng, Rongshun Chen^*

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 991-1006, 2022, DOI:10.32604/iasc.2022.022067 - 17 November 2021

Abstract Path planning is intrinsically regarded as a multi-objective optimization problem (MOOP) that simultaneously optimizes the shortest path and the least collision-free distance to obstacles. This work develops a novel optimized approach using the genetic algorithm (GA) to drive the multi-objective evolutionary algorithm (MOEA) for the path planning of a mobile robot in a given finite environment. To represent the positions of a mobile robot as integer-type genes in a chromosome of the GA, a grid-based method is also introduced to relax the complex environment to a simple grid-based map. The system architecture is composed of More >

R. Joshua Samuel Raj¹, M. Varalatchoumy², V. L. Helen Josephine³, A. Jegatheesan⁴, Seifedine Kadry⁵, Maytham N. Meqdad⁶, Yunyoung Nam^7,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1095-1109, 2022, DOI:10.32604/cmc.2022.021859 - 03 November 2021

Abstract Internet of Things (IoT) is transforming the technical setting of conventional systems and finds applicability in smart cities, smart healthcare, smart industry, etc. In addition, the application areas relating to the IoT enabled models are resource-limited and necessitate crisp responses, low latencies, and high bandwidth, which are beyond their abilities. Cloud computing (CC) is treated as a resource-rich solution to the above mentioned challenges. But the intrinsic high latency of CC makes it nonviable. The longer latency degrades the outcome of IoT based smart systems. CC is an emergent dispersed, inexpensive computing pattern with massive… More >

P. Suresh^1,*, R. H. Aswathy¹, Sridevi Arumugam², Amani Abdulrahman Albraikan³, Fahd N. Al-Wesabi⁴, Anwer Mustafa Hilal⁵, Mohammad Alamgeer⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1713-1728, 2022, DOI:10.32604/cmc.2022.021789 - 03 November 2021

Abstract In India, water wastage in agricultural fields becomes a challenging issue and it is needed to minimize the loss of water in the irrigation process. Since the conventional irrigation system needs massive quantity of water utilization, a smart irrigation system can be designed with the help of recent technologies such as machine learning (ML) and the Internet of Things (IoT). With this motivation, this paper designs a novel IoT enabled deep learning enabled smart irrigation system (IoTDL-SIS) technique. The goal of the IoTDL-SIS technique focuses on the design of smart irrigation techniques for effectual water… More >

Mesfer Al Duhayyim¹, Radwa Marzouk^2,3, Fahd N. Al-Wesabi⁴, Maram Alrajhi⁵, Manar Ahmed Hamza^6,*, Abu Sarwar Zamani⁶

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1233-1247, 2022, DOI:10.32604/cmc.2022.021652 - 03 November 2021

Abstract Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can… More >

Haykel Marouani^1,2, Omar Al-mutiri^1,*

CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 537-571, 2022, DOI:10.32604/cmc.2022.020098 - 03 November 2021

Abstract The study of optimization methods for reliability–redundancy allocation problems is a constantly changing field. New algorithms are continually being designed on the basis of observations of nature, wildlife, and humanity. In this paper, we review eight major evolutionary algorithms that emulate the behavior of civilization, ants, bees, fishes, and birds (i.e., genetic algorithms, bee colony optimization, simulated annealing, particle swarm optimization, biogeography-based optimization, artificial immune system optimization, cuckoo algorithm and imperialist competitive algorithm). We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation problems. Results from a More >

Monday Eze^1,*, Charles Okunbor², Deborah Aleburu³, Olubukola Adekola¹, Ibrahim Ramon⁴, Nneka Richard-Nnabu⁵, Oghenetega Avwokuruaye⁶, Abisola Olayiwola³, Rume Yoro⁷, Esomu Solomon⁸

Computer Systems Science and Engineering, Vol.41, No.2, pp. 595-609, 2022, DOI:10.32604/csse.2022.020835 - 25 October 2021

Abstract This work demonstrates in practical terms the evolutionary concepts and computational applications of Parametric Curves. Specific cases were drawn from higher order parametric Bezier curves of degrees 2 and above. Bezier curves find real life applications in diverse areas of Engineering and Computer Science, such as computer graphics, robotics, animations, virtual reality, among others. Some of the evolutionary issues explored in this work are in the areas of parametric equations derivations, proof of related theorems, first and second order calculus related computations, among others. A Practical case is demonstrated using a graphical design, physical hand More >

Akshya Jothi, P. L. K. Priyadarsini^*

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1577-1591, 2022, DOI:10.32604/iasc.2022.020974 - 09 October 2021

Abstract Unmanned Aerial Vehicles (UAVs) are in use for surveillance services in the geographic areas, that are very hard and sometimes not reachable by humans. Nowadays, UAVs are being used as substitutions to manned operations in various applications. The intensive utilization of autonomous UAVs has given rise to many new challenges. One of the vital problems that arise while deploying UAVs in surveillance applications is the Coverage Path Planning(CPP) problem. Given a geographic area, the problem is to find an optimal path/tour for the UAV such that it covers the entire area of interest with minimal… More >

Umanesan Ramakrishnan^1,*, Nandhagopal Nachimuthu²

Intelligent Automation & Soft Computing, Vol.31, No.3, pp. 1547-1559, 2022, DOI:10.32604/iasc.2022.017123 - 09 October 2021

Abstract Recently, various research fields have begun dealing with massive datasets forseveral functions. The main aim of a feature selection (FS) model is to eliminate noise, repetitive, and unnecessary featuresthat reduce the efficiency of classification. In a limited period, traditional FS models cannot manage massive datasets and filterunnecessary features. It has been discovered from the state-of-the-art literature that metaheuristic algorithms perform better compared to other FS wrapper-based techniques. Common techniques such as the Genetic Algorithm (GA) andParticle Swarm Optimization (PSO) algorithm, however, suffer from slow convergence and local optima problems. Even with new generation algorithms such… More >

Yang Shi^*, Xiaoyu Song, Marek Perkowski, Fu Li

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2191-2207, 2022, DOI:10.32604/cmc.2022.018718 - 27 September 2021

Abstract Search-based statistical structural testing (SBSST) is a promising technique that uses automated search to construct input distributions for statistical structural testing. It has been proved that a simple search algorithm, for example, the hill-climber is able to optimize an input distribution. However, due to the noisy fitness estimation of the minimum triggering probability among all cover elements (Tri-Low-Bound), the existing approach does not show a satisfactory efficiency. Constructing input distributions to satisfy the Tri-Low-Bound criterion requires an extensive computation time. Tri-Low-Bound is considered a strong criterion, and it is demonstrated to sustain a high fault-detecting… More >