Yifan Zhang¹, Qiancheng Yu^1,2,*, Lisi Zhang¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 661-677, 2024, DOI:10.32604/cmes.2023.044255 - 30 December 2023

Abstract Widely used deep neural networks currently face limitations in achieving optimal performance for purchase intention prediction due to constraints on data volume and hyperparameter selection. To address this issue, based on the deep forest algorithm and further integrating evolutionary ensemble learning methods, this paper proposes a novel Deep Adaptive Evolutionary Ensemble (DAEE) model. This model introduces model diversity into the cascade layer, allowing it to adaptively adjust its structure to accommodate complex and evolving purchasing behavior patterns. Moreover, this paper optimizes the methods of obtaining feature vectors, enhancement vectors, and prediction results within the deep More >

Yue Zhu, Tao Zhao^*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1139-1158, 2024, DOI:10.32604/cmes.2023.030178 - 17 November 2023

Abstract The optimization of the rule base of a fuzzy logic system (FLS) based on evolutionary algorithm has achieved notable results. However, due to the diversity of the deep structure in the hierarchical fuzzy system (HFS) and the correlation of each sub fuzzy system, the uncertainty of the HFS's deep structure increases. For the HFS, a large number of studies mainly use fixed structures, which cannot be selected automatically. To solve this problem, this paper proposes a novel approach for constructing the incremental HFS. During system design, the deep structure and the rule base of the… More >

Shehab Abdulhabib Alzaeemi¹, Kim Gaik Tay^1,*, Audrey Huong¹, Saratha Sathasivam², Majid Khan bin Majahar Ali²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 1163-1184, 2023, DOI:10.32604/csse.2023.038912 - 26 May 2023

Abstract Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior’s integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the near-optimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different… More >

Doaa Sami Khafaga¹, El-Sayed M. El-kenawy^2,3, Faten Khalid Karim^1,*, Sameer Alshetewi⁴, Abdelhameed Ibrahim⁵, Abdelaziz A. Abdelhamid^6,7, D. L. Elsheweikh⁸

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2379-2395, 2023, DOI:10.32604/cmc.2023.032886 - 31 October 2022

Abstract Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the More >

Mohamed A. Meselhi^*, Saber M. Elsayed, Daryl L. Essam, Ruhul A. Sarker

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1-17, 2023, DOI:10.32604/cmc.2023.027448 - 22 September 2022

Abstract Dynamic constrained optimization is a challenging research topic in which the objective function and/or constraints change over time. In such problems, it is commonly assumed that all problem instances are feasible. In reality some instances can be infeasible due to various practical issues, such as a sudden change in resource requirements or a big change in the availability of resources. Decision-makers have to determine whether a particular instance is feasible or not, as infeasible instances cannot be solved as there are no solutions to implement. In this case, locating the nearest feasible solution would be… More >

Qamar Abbas¹, Khalid Mahmood Malik², Abdul Khader Jilani Saudagar^3,*, Muhammad Badruddin Khan³, Mozaherul Hoque Abul Hasanat³, Abdullah AlTameem³, Mohammed AlKhathami³

CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211 - 24 February 2022

Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm… More >

Amit Ramesh Khaparde^1,*, Fawaz Alassery², Arvind Kumar³, Youseef Alotaibi⁴, Osamah Ibrahim Khalaf⁵, Sofia Pillai⁶, Saleh Alghamdi⁷

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1045-1062, 2022, DOI:10.32604/iasc.2022.023270 - 08 February 2022

Abstract This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, More >

Bin Xu^1,2,3, Lu Zhang¹, Zipeng Xu¹, Yichuan Liu¹, Jinming Chai¹, Sichong Qin⁴, Yanfei Sun^1,*

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1671-1686, 2021, DOI:10.32604/cmc.2021.018395 - 21 July 2021

Abstract In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize… More >

Annan Jiang^*, Xinping Guo, Shuai Zheng, Mengfei Xu

CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1177-1199, 2021, DOI:10.32604/cmes.2021.014199 - 24 May 2021

Abstract Due to the geological body uncertainty, the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability. The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes, but conventional methods have certain problems, such as a large number of parameters and large time consumption. To solve the problems, this study combines the orthogonal design, Gaussian process (GP) regression, and difference evolution (DE) optimization, and it constructs the parameters identification method of the jointed surrounding rock.… More >

Aiyun Hu¹, Sunli Cong^1,*, Jian Ding², Yao Cheng¹, Enock Mpofu³

Computer Systems Science and Engineering, Vol.38, No.1, pp. 65-77, 2021, DOI:10.32604/csse.2021.016404 - 01 April 2021

Abstract In the fed-batch cultivation of Saccharomyces cerevisiae, excessive glucose addition leads to increased ethanol accumulation, which will reduce the efficiency of glucose utilization and inhibit product synthesis. Insufficient glucose addition limits cell growth. To properly regulate glucose feed, a different evolution algorithm based on self-adaptive control strategy was proposed, consisting of three modules (PID, system identification and parameter optimization). Performance of the proposed and conventional PID controllers was validated and compared in simulated and experimental cultivations. In the simulation, cultivation with the self-adaptive control strategy had a more stable glucose feed rate and concentration, more stable More >