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In this study, the optimum design for an induction motor (IM) was realized by providing details of its geometric design. The IM optimization was carried out using the Artificial Ecosystem-based Optimization (AEO) algorithm, a metaheuristic method. The AEO algorithm was used for the first time in IM optimization, and the design parameters were optimized. Ten motor design parameters were used as design variables. IM efficiency was improved, as the objective function. The genetic algorithm (GA) optimization method was used for comparison with the results obtained with the AEO method. The optimized and unoptimized results of the IM design generated with codes created in the Matlab program were verified with the Ansys RMxprt EM Suite 19.2 program, and it could be seen that the results are in good agreement. As a result of these studies, it was observed that the use of AEO in determining the geometric parameters of the IM had better convergence accuracy and reached the optimum result in a shorter time compared to the GA optimization method. It was observed that IM efficiency increased from 90.34% to 91.575% on average with the AEO method.

This study proposes a new optimization approach, which is called as artificial ecosystem optimization algorithm with fitness-distance balance guiding mechanism by using opposite based learning methods (FDBAEO_OBLs) for the speed regulation of direct current (DC) motor. The performance of the proposed FDBAEO_OBL algorithm is tested in two different experimental studies. In the first experimental study, the proposed approach is tested in the CEC2020 benchmark test functions and the FDBAEO algorithm, which included the best OBL approach, is determined using non-parametric Wilcoxon and Friedman statistical analysis methods. Second, the parameters of proportional integral derivative (PID), tilt integral derivative (TID), proportional integral derivative with filter (PIDF), tilt integral derivative with filter (TIDF), fractional-order proportional integral derivative (FOPID), fractional-order proportional integral derivative with filter (FOPIDF), proportional integral derivative with fractional-order filter (PIDFF) and fractional-order proportional integral derivative with fractional-order filter (FOPIDFF) controller structures to be used in DC motor closed loop speed control are determined with FDBAEO_OBL, and the performances of the controllers are investigated. Integral absolute error (IAE), integral time absolute error (ITAE), integral time squared error (ITSE) and integral squared error (ISE) performance indices are used as the objective function of the operation process in which the control parameters are determined. According to the comparative step response results of the controller structures, the four best controller structures for DC motor speed regulation are determined. The performances of these controllers are examined under different simulation conditions and according to the results obtained, it is seen that the best controller structure is FOPIDFF. The FDBAEO_OBL algorithm, which is used in both benchmark test functions and DC motor speed regulation, shows an effective, durable and superior performance in finding the optimal solution values during the optimization.

The process of data classification involves determining the optimal number of features that lead to high accuracy. However, feature selection (FS) is a complex task that necessitates robust metaheuristics due to its challenging NP-hard nature. This paper introduces a hybrid algorithm that combines the Artificial Ecosystem Optimization (AEO) operators with the Whale Optimization Algorithm (WOA) to enhance numerical optimization and FS. While the WOA algorithm, inspired by the hunting behavior of whales, has been successful in solving various optimization problems, it can sometimes be limited in its ability to explore and may become trapped in local optima. To address this limitation, the authors propose the use of AEO operators to improve the exploration process of the WOA algorithm. The authors conducted experiments to evaluate the effectiveness of their proposed method, called AEOWOA, using the CEC’20 test suite for numerical optimization and sixteen datasets for FS. They compared the results with those obtained from other optimization methods. Through experimental and statistical analyses, it was observed that AEOWOA delivers efficient search results with faster convergence, reducing the feature size by up to 89% while achieving up to 94% accuracy. These findings shed light on potential future research directions in this field.

Tuberculosis (TB) is is an infectious disease that generally attacks the lungs and causes death for millions of people annually. Chest radiography and deep-learning-based image segmentation techniques can be utilized for TB diagnostics. Convolutional Neural Networks (CNNs) has shown advantages in medical image recognition applications as powerful models to extract informative features from images. Here, we present a novel hybrid method for efficient classification of chest X-ray images. First, the features are extracted from chest X-ray images using MobileNet, a CNN model, which was previously trained on the ImageNet dataset. Then, to determine which of these features are the most relevant, we apply the Artificial Ecosystem-based Optimization (AEO) algorithm as a feature selector. The proposed method is applied to two public benchmark datasets (Shenzhen and Dataset 2) and allows them to achieve high performance and reduced computational time. It selected successfully only the best 25 and 19 (for Shenzhen and Dataset 2, respectively) features out of about 50,000 features extracted with MobileNet, while improving the classification accuracy (90.2% for Shenzen dataset and 94.1% for Dataset 2). The proposed approach outperforms other deep learning methods, while the results are the best compared to other recently published works on both datasets.

Renal cancer is a key reason for cancer-related deaths among males worldwide. Earlier diagnosis of renal cancer is critical since it can considerably increase the chance of survivability. However evaluating the histopathological renal tissue is a tedious process and usually, this is manually done by the pathologist, resulting in a strong possibility of misdiagnosis or misdetection, particularly in the earlier phases, and susceptible to inter-pathologist variations. The advancement of automated histopathological diagnoses of renal cancer could significantly decrease the bias and offer correct classification of disease though the pathology and microscopy nature are more complicated and complex. Current researchers recommend that clinicians successfully implement the classification task by investigating the image texture feature of RCC from computed tomography (CT) data. However, image feature detection for RCC grading frequently depends on a physical process that is time-intensive and error-prone. Therefore, this article develops an Exploiting Deep Transfer Learning based Precise Classification and Grading of Renal Cell Carcinoma (EDTL-PCGRCC) method using Histopathological Imaging. The projected EDTL-PCGRCC methods inspect the histopathological images for the classification and detection of RCC. In the suggested EDTL-PCGRCC method, a wiener filtering (WF) based noise removal technique takes place for noise removal procedure. Furthermore, the EDTL-PCGRCC method uses an improved MobileNetV2 technique to derive the feature vector from pre-processed images. Furthermore, the classification of RCC takes place using the Elman Neural Network (ENN) mechanism. Lastly, improved artificial ecosystem optimization (IAEO) is applied for the parameter selection of the ENN model. The efficiency of the EDTL-PCGRCC method is assessed under the biomedical image dataset. The empirical findings reported the robustness of the EDTL-PCGRCC method under various measures.

Optimization of reactive power dispatch (ORPD) problem is a key factor for stable and secure operation of the electric power systems. In this paper, a newly explored nature-inspired optimization through artificial ecosystem optimization (AEO) algorithm is proposed to cope with ORPD problem in large-scale and practical power systems. ORPD is a well-known highly complex combinatorial optimization task with nonlinear characteristics, and its complexity increases as a number of decision variables increase, which makes it hard to be solved using conventional optimization techniques. However, it can be efficiently resolved by using nature-inspired optimization algorithms. AEO algorithm is a recently invented optimizer inspired by the energy flocking behavior in a natural ecosystem including non-living elements such as sunlight, water, and air. The main merit of this optimizer is its high flexibility that leads to achieve accurate balance between exploration and exploitation abilities. Another attractive property of AEO is that it does not have specific control parameters to be adjusted. In this work, three-objective version of ORPD problem is considered involving active power losses minimization and voltage deviation and voltage stability index. The proposed optimizer was examined on medium- and large-scale IEEE test systems, including 30 bus, 118 bus, 300 bus and Algerian electricity grid DZA 114 bus (220/60 kV). The results of AEO algorithm are compared with well-known existing optimization techniques. Also, the results of comparison show that the proposed algorithm performs better than other algorithms for all examined power systems. Consequently, we confirm the effectiveness of the introducing AEO algorithm to relieve the over losses problem, enhance power system performance, and meet solutions feasibility. One-way analysis of variance (ANOVA) has been employed to evaluate the performance and consistency of the proposed AEO algorithm in solving ORPD problem.

This paper proposes an improved version of the artificial ecosystem-based optimization (AEO) algorithm called artificial ecosystem-based optimization–opposition-based learning (AEO-OBL), with the aim of improving the performance of the original AEO. In addition, it is utilized for determining the optimal allocation of distributed generation (DG) units in radial distribution networks (RDNs) with the aim of minimizing power and energy losses. The stochastic nature of renewable DGs such as wind turbine and photovoltaic generation is taken in consideration using appropriate probability models. The Loss Sensitivity Index is used to assess the most suitable busses for the integration of DG units in the RDN. AEO is nature-inspired optimization algorithm which imitates the flow of energy in an ecosystem on earth. In the proposed AEO-OBL, the search ability and the balance between the exploration and exploitation phases in the original AEO are enhanced. In the AEO-OBL, five efficient strategies are used to avoid falling on a local optimal: (1) enhanced linear weight coefficient a, (2) production operator, (3) modified consumption operator, (4) modified decomposing operator and (5) opposition-based learning (OBL). The performance of the proposed technique is validated on IEEE 33-bus and 85-bus RDNs. To emphasize the superiority of the proposed technique, the results are compared with the original AEO, Henry gas solubility optimizer (HGSO) and Harris hawks optimization (HHO) algorithm results. Besides, the developed algorithm is compared with other optimization algorithms in literature that solved the same problem. The outcomes indicate a better performance of AEO-OBL relative to other algorithms. Accordingly, AEO-OBL can be a very suitable algorithm in solving the problem of optimal DG allocation in RDNs.

The Inventory-Routing Problem (IRP) is considered a major issue in supply chain management as it comprises two areas: vehicle routing and inventory control. The existing techniques were unable to incorporate location details for enhancing the decision-making and it failed to consider the uncertainty of the demands. Hence to solve this issue, a Snake Artificial Ecosystem Optimization (SAEO) algorithm is proposed in this paper. The SAEO algorithm is developed to address the transshipment IRP with dynamic demands by combining the AEO model and SO to enhance the optimizer's performance. Further, a penalty strategy is proposed, where Deep Quantum Neural Network (DQNN) is employed for calculating the penalty for verifying the feasibility of the solution generated in case of violations in model constraints. In addition, the efficiency of the proposed SAEO-DQNN technique is examined by considering metrics, like transportation cost, transshipment cost, and total cost, and it achieved improved values of 0.391, 0.518, and 1.012 when compared to existing techniques such as Genetic Algorithm with Deep Reinforcement Learning (GA + Deep RL) and Kernel Search Multi-vehicle IRP (KSMIRP).

Meta-Heuristic (MH) algorithms have recently proven successful in a broad range of applications because of their strong capabilities in picking the optimal features and removing redundant and irrelevant features. Artificial Ecosystem-based Optimization (AEO) shows extraordinary ability in the exploration stage and poor exploitation because of its stochastic nature. Dwarf Mongoose Optimization Algorithm (DMOA) is a recent MH algorithm showing a high exploitation capability. This paper proposes AEO-DMOA Feature Selection (FS) by integrating AEO and DMOA to develop an efficient FS algorithm with a better equilibrium between exploration and exploitation. The performance of the AEO-DMOA is investigated on seven datasets from different domains and a collection of twenty-eight global optimization functions, eighteen CEC2017, and ten CEC2019 benchmark functions. Comparative study and statistical analysis demonstrate that AEO-DMOA gives competitive results and is statistically significant compared to other popular MH approaches. The benchmark function results also indicate enhanced performance in high-dimensional search space.

The number of Internet of Things (IoT) devices overgrows, and this technology dominates. The importance of IoT security and the growing need to devise intrusion detection systems (IDSs) to detect all types of attacks. The limited sources on the IoT. They have led researchers to explore and provide new and efficient solutions to create Botnet Detection in IoT systems. These systems use data features to detect network traffic status and thus detect malicious behavior. Also, data set features indicate the type of network traffic. Many features in the problem space and network behaviour unpredictability make IDSs the main challenge in establishing security in computer networks. Many unnecessary features have also made feature selection an essential aspect of attack detection systems. This paper developed a multi-objective MOAEOSCA algorithm hybridizing Artificial Ecosystem-based Optimization (AEO) algorithms and the Sine Cosine Algorithm (SCA) for botnet detection in IoT. By accurately identifying the weaknesses of the MOAEOSCA algorithm, it has been tried to cover the weaknesses to a large extent and to reach a robust algorithm. We promoted the proposed algorithm using Bitwise operations, Disruption operator, and Opposition-based learning (OBL) mechanisms. Ten standard datasets in the UCI repository were examined to evaluate the proposed algorithm’s performance in solving the feature selection problem to detect a botnet. Simulation findings indicated that the proposed algorithm had an acceptable accuracy in Botnet Detection in the IoT, outperforming other methods. According to the experiments carried out in this paper, the MOAEOSCA algorithm has shown that nine data sets out of ten data sets in the feature selection problem performed better than other optimization algorithms. But in all seven botnet data sets, performance has shown better than different optimization algorithms.