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Enhanced Stock Market Trend Prediction on the Indonesia Stock Exchange Using Improved Bacterial Foraging optimization and Elitist whale optimization algorithms

Enhanced Stock Market Trend Prediction on the Indonesia Stoc...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Deepak Kumar Priyanka Pramod Pawar Mohan Kumar Meesala Piyush Kumar Pareek Santosh Reddy Addula K S Shwetha Department of Information Technology University of the Cumberlands Williamsburg USA Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Engineering Dayananda Sagar College of Engineering Bangalore India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

It was difficult to find low-risk firms in 2022 due to the large number of 825 listed on the Indonesia Stock Exchange (IDX). Essential concerns in financial works include stock price forecasting and price movement forecast. The stock market research has shown encouraging outcomes in the past. We still don't have a consensus on the finest stock value forecasting classical due to the fact that each study report has different architecture and uses different stock issuers. This paper presents a new method for optimizing bacterial foraging that uses optimal deep learning to forecast stock market trends. For the prediction process, the given model employs a multiplicative long short term memory (MLSTM) perfect, with IBFO serving as the model's fine-tuner. The research paper presents a modified whale optimization technique for feature selection. This research proposes an elitist whale optimization algorithm with the nonlinear parameter (EWOANP) to fix the issue where the whale optimization procedure does not strike a balance among exploration and exploitation and instead falls into the local optimum. To improve the odds of evading the local optimum, the shrinking encircling mechanism employs an elitist tactic based on random Cauchy mutation. Using the random Cauchy mutation as a starting point, the method cleverly selects the population with the best mutation solutions to move on to the next iteration. To strike a compromise among the two, the logarithmic spiral mechanism makes use of a nonlinear parameter. There are 2,588,451 entries in the dataset representing 727 IDX firms' open, highest, lowest, closed, and volume transactions. Various stock time records are used to examine the trial findings using current methodologies in terms of distinct metrics.

关键词： Microorganisms Spirals Predictive models Feature extraction Market research Prediction algorithms whale optimization algorithms Stock markets Forecasting Tuning

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whale optimization Algorithm-Based Channel Parameter Estimation for Ranging Applications in Closely Spaced Multipath Environment

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第4期73卷 2684-2689页

作者： Liang, Zhiwei Lyu, Yue Wang, Wei An, Yisheng Changan Univ Sch Informat Engn Xian 710064 Shaanxi Peoples R China

Range-based positioning using wireless signal has gained remarkable attention. Accurate estimation of channel parameters including delay and complex amplitude is essential for positioning applications. However, multipath propagation of wireless signal significantly impacts the performance of channel estimation, particularly the estimation accuracy degrades in closely spaced multipath environment. In this communication, we propose a whale optimization algorithm (WOA)-based channel estimator to extract channel parameter in closely spaced multipath environment. We derive the fitness function based on the maximum likelihood criterion. Both the simulation and measurement-based validation for closely spaced multipath environment reveal that the proposed method outperforms several state-of-the-art methods including as space-alternating generalized expectation-maximization (SAGE), gray wolf optimizer (GWO), and salp swarm algorithm (SSA). The results conclusively demonstrate that the WOA is well-suited for dense multipath parameter estimation.

关键词： Channel estimation Distance measurement Vectors whales whale optimization algorithms Parameter estimation Spirals Frequency response Delays Autoregressive processes Channel parameter estimation positioning ranging performance whale optimization algorithm (WOA)

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Near-Global-Optimal Resource Allocation for Uplink SCMA Systems Based on whale optimization Algorithm

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2025年第2期24卷 1089-1103页

作者： Soltanpour, Meysam Zhang, Haixia Shandong Key Lab Intelligent Commun Sensing Comp I Jinan 250061 Shandong Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250061 Shandong Peoples R China

This paper investigates a joint resource allocation (RA) algorithm to maximize the average sum rate (ASR) of the sparse code multiple access (SCMA) random model-based networks. The ASR optimization problem turns out to be non-convex mixed-integer nonlinear programming (MINLP) problem which is challenging to address. The previous works strongly depend on the upper/lower bound of ASR, successive convex approximation (SCA) methods, initial assignment definition, post-processing procedures, and generating/training the data sets, thus cannot be applied. Given the above challenges, considering the ability of the whale optimization algorithm (WOA) in solving the non-convex MINLP NP-hard problems, we first construct the resource element assignment (REA) matrix utilizing the binary WOA (BWOA) through the reshaping the pattern of the arrangement of non-zero elements (PANs) and converting the structural constraints. Then we enhance the continuous WOA (CWOA) to mine the power allocation (PA) coefficients by using an acceleration factor that improves the exploration and exploitation phases. We also develop a Kuhn-Munkres (KM)-based algorithm as an REA benchmark which needs no for post-processing requirements. Finally, our proposed algorithms are compared with the state-of-the-art works in the literature. Results show that SCMA CWOA-BWOA algorithm (CBWOA) provides more robust optimality in massive connectivity situations with a larger number of users. Besides, the CBWOA method guarantees the structural constraints by increasing the number of search agents (NSAs) through the exploitation phase.

关键词： Resource management Uplink Interference whale optimization algorithms Vectors Clustering algorithms Training Measurement Industrial Internet of Things Wireless networks SCMA whale optimization algorithm non-convex MINLP resource allocation metaheuristic

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Integrating Two-Tier optimization Algorithm With Convolutional Bi-LSTM Model for Robust Anomaly Detection in Autonomous Vehicles

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IEEE ACCESS 2025年 13卷 6820-6833页

作者： Alotaibi, Moneerah Alohali, Manal Abdullah Mahmood, Khalid Alhashmi, Asma A. Alqurni, Jehad Saad Alotaibi, Sultan Refa Alzahrani, Ahmad A. Issaoui, Imene Shaqra Univ Coll Sci & Humanities Dawadmi Dept Comp Sci Shaqra 11961 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Appl Coll Mahayil Dept Informat Syst Abha 61421 Saudi Arabia Northern Border Univ Coll Sci Dept Comp Sci Ar Ar 91431 Saudi Arabia Imam Abdulrahman bin Faisal Univ Coll Educ Dept Educ Technol Dammam 31441 Saudi Arabia Saudi Elect Univ Coll Comp & Informat Dept Comp Sci Riyadh 11673 Saudi Arabia Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca 21955 Saudi Arabia Qassim Univ Appl Coll Unit Sci Res Buraydah 51452 Saudi Arabia

Industrial development has changed vehicles of traditional into autonomous vehicles (AVs). AVs play a significant role since they are measured as a vital module of smart cities. The AV is an advanced automobile efficient in preserving secure driving by evading collisions formed by drivers. In contrast with traditional vehicles, which are fully coordinated and functioned by humans, AVs gather information regarding the exterior environment utilizing sensors to guarantee secure navigation. AVs decrease environmental effects since they regularly employ electricity to function rather than fossil fuel, thus diminishing greenhouse gasses. However, AVs might be exposed to cyber-attacks, causing dangers to human life. Machine learning (ML) and deep learning (DL) based anomaly recognition has progressed as a new study track in autonomous driving. ML and DL-based anomaly detection scholars have focused on improving accuracy as a typical classification task without aiming at mischievous information. This article develops an improved whale optimization algorithm-based feature selection using explainable artificial intelligence for robust anomaly detection (IWOAFS-XAIAD) technique in autonomous driving. The major aim of the IWOAFS-XAIAD technique is an endwise XAI structure to construe and imagine the anomaly recognition classifications prepared by AI models securing autonomous driving systems. Initially, the IWOAFS-XAIAD technique utilizes the Z-score data normalization method to convert input data into a compatible layout. Besides, the IWOAFS-XAIAD technique employs an improved whale optimization algorithm (IWOA)-based feature subset selection to pick an optimum set of features. An attention mechanism with the CNN-BiLSTM (CNN-BiLSTM-A) model is employed for anomaly detection and classification. Moreover, the catch-fish optimization algorithm (CFOA) selects the hyperparameters connected to the CNN-BiLSTM-A model. Finally, utilizing the SHAP XAI method, the IWOAFS-XAIAD technique

关键词： Anomaly detection Artificial intelligence Feature extraction Autonomous vehicles Accuracy Transportation Classification algorithms whale optimization algorithms Safety Data models INDEX TERMS Explainable artificial intelligence anomaly detection improved whale optimization algorithm autonomous vehicles catch fish optimization

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Optimized Multi-User Dependent Tasks Offloading in Edge-Cloud Computing Using Refined whale optimization Algorithm

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

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IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第1期9卷 14-30页

作者： Hosny, Khalid M. Awad, Ahmed I. Khashaba, Marwa M. Fouda, Mostafa M. Guizani, Mohsen Mohamed, Ehab R. Zagazig Univ Fac Comp & Informat Dept Informat Technol Zagazig 44519 Egypt Idaho State Univ Dept Elect & Comp Engn Pocatello ID 83209 USA Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Masdar City Abu Dhabi U Arab Emirates Zagazig Univ Dept Informat Technol Zagazig 44519 Egypt

Despite the extensive use of IoT and mobile devices in the different applications, their computing power, memory, and battery life are still limited. Multi-Access Edge Computing (MEC) has recently emerged to address the drawbacks of these limitations. With MEC on the network's edge, mobile and IoT devices can offload their computing operations to adjacent edge servers or remote cloud servers. However, task offloading is still a challenging research issue, and it is necessary to improve the overall Quality of Service (QoS) and attain optimized performance and resource utilization. Another crucial issue that is usually overlooked while handling this matter is offloading an application that consists of dependent tasks. In this study, we suggest a Refined whale optimization Algorithm (RWOA) for solving the multiuser dependent tasks offloading problem in the Edge-Cloud computing environment with three objectives: 1- minimizing the application execution latency, 2- minimizing the energy consumption of end devices, and 3- the charging cost for used resources. We also avoid the traditional binary planning mechanisms by allowing each task to be partially processed simultaneously at three processing locations (local device, MEC, cloud). We compare RWOA with other Optimizers, and the results demonstrate that the RWOA has optimized the fitness by 52.7% relative to the second best comparison optimizer.

关键词： Task analysis Cloud computing Servers Internet of Things Energy consumption Costs whale optimization algorithms multi-access edge computing multi-objective computational offloading multi-user scenario task dependency whale optimization algorithm

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Comprehensive Technical Review of Recent Bio-Inspired Population-Based optimization (BPO) algorithms for Mobile Robot Path Planning

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IEEE ACCESS 2024年 12卷 20942-20961页

作者： Saleh, Izzati Borhan, Nuradlin Yunus, Azan Rahiman, Wan Univ Sains Malaysia Sch Elect & Elect Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia CL Solut Sdn Bhd Wisma Rampai Kuala Lumpur 59200 Malaysia Univ Sains Malaysia Cluster Smart Port & Logist Technol COSPALT Nibong Tebal 14300 Malaysia Daffodil Int Univ Dept Comp Sci & Engn Daffodil Robot Lab Dhaka 1216 Bangladesh

Over recent decades, the field of mobile robot path planning has evolved significantly, driven by the pursuit of enhanced navigation solutions. The need to determine optimal trajectories within complex environments has led to the exploration of diverse path planning methodologies. This paper focuses on a specific subset: Bio-inspired Population-based optimization (BPO) methodologies. BPO methods play a pivotal role in generating efficient paths for path planning. Amidst the abundance of optimization approaches over the past decade, only a fraction of studies has effectively integrated these methods into path planning strategies. This paper's focus is on the years 2014-2023, reviewing BPO techniques applied to mobile robot path planning challenges. Contributions include a comprehensive review of recent BPO methods in mobile robot path planning, along with an experimental methodology to compare them under consistent conditions. This encompasses the same environment, initial conditions, and replicates. A multi-objective function is incorporated to evaluate optimization methods. The paper delves into key concepts, mathematical models, and algorithm implementations of examined optimization techniques. The experimental setup, methodology, and benchmarking performance results are discussed. Based on the proposed experimental methodology, Improved Sparrow Search Algorithm (ISpSA) shows the best cost improvement percentage (7.87%), but suffers in terms of optimization time. On the other hand, whale optimization Algorithm (WOA) has lesser improvement percentage of 6.05% but better optimization time. In conclusion, the standardized approach for benchmarking BPO algorithms provides useful insights into their strengths and challenges in mobile robot path planning.

关键词： optimization methods Path planning Mathematical models whales Spirals Mobile robots Bio-inspired computing Metaheuristics Particle swarm optimization whale optimization algorithms Grasshopper optimization algorithm grey wolf optimization metaheuristic optimization path planning salp swarm optimization sparrow search optimization whale optimization

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Enhanced Speed Control for BLDC Motors Using WOA-Integrated PID Controller optimization

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IEEE ACCESS 2024年 12卷 162465-162475页

作者： Krishnamoorthy, Saravana Kumar Das, Narottam Gudimetla, Prasad Emami, Kianoush Cent Queensland Univ Melbourne Sch Engn & Technol Melbourne Vic 3000 Australia Cent Queensland Univ Ctr Intelligent Syst Brisbane Qld 4000 Australia Cent Queensland Univ Bundaberg Sch Engn & Technol Bundaberg Qld 4670 Australia Cent Queensland Univ Cairns Sch Engn & Technol Cairns Qld 4870 Australia

In electrical engineering, Brushless Direct Current (BLDC) motors are frequently used in mechanical applications because of their effectiveness, strong torque, and small design. Nevertheless, reaching peak performance and making accurate adjustments to parameters can be difficult when using a simple, customized Proportional Integral Derivative (PID) controller. In the past, speed control typically included adjusting crucial factors like voltage, and current. However, manual speed regulation has drawbacks, including being time-consuming, susceptible to human error, and lacking scalability. Different traditional models have tried to enhance speed control efficiency with Artificial Intelligence (AI) but face challenges in improving Rise Time, Settling Time, Maximum Overshoot, and overall efficiency. A proposed approach to address this problem involves the implementation of a developed model using the Enhanced whale optimization Algorithm- Tuned PID (EWOA-TPID) Controller. This system utilizes the benefits of the whale optimization Algorithm (WOA) to increase convergence speed, enhance exploitation and exploration abilities, and accurate speed control by efficiently tuning the parameters of PID to decrease the steady state error and overshoot. The key performance metrics which comprise Rise Time, Settling Time, and Maximum Overshoot are utilized to assess the efficacy of this approach. Moreover, the presented system is compared with conventional models to showcase the improved effectiveness of the respective model. This innovative approach intends to contribute significantly to studies in areas like robotics, automation, electric vehicles, industrial machinery, and other systems that use BLDC motors for speed regulation.

关键词： Motors Velocity control Torque Artificial intelligence optimization Regulation PI control MATLAB whale optimization algorithms Pulse width modulation Brushless DC motors Artificial intelligence (AI) brushless direct current (BLDC) motor enhanced whale optimization algorithm (EWOA) proportional integral derivative (PID) pulse width modulation (PWM)

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Optimal Design of a PIDD² Controller for an AVR System Using Hybrid whale optimization Algorithm

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IEEE ACCESS 2024年 12卷 128525-128540页

作者： Dai, Fei Gao, Song Xian Technol Univ Sch Mechatron Engn Xian 710021 Peoples R China

Many meta-heuristic algorithms have been used in designing PID controllers to control automatic voltage regulator (AVR) system. PIDD2 controller is a PID controller with second-order derivative gain. The fireworks whale optimization algorithm is an advanced metaheuristic algorithm that combines fireworks and whale optimization algorithms. It has good exploratory properties because it explores a large potential space and avoids premature local optima. This paper presents a PIDD2 controller for improving the performance of the AVR system using the hybrid whale optimization algorithm. The simulation results, divided into two parts based on whether the limitation of the excitation voltage is considered or not, include a detailed analysis of the proposed controller in terms of step response, stability analysis, trajectory tracking, parameter uncertainty, and rejection of disturbance. To demonstrate the effectiveness of the proposed controller of the AVR system, it was compared with the controllers in recent literature in each of the aforementioned terms. The proposed controller, in most comparisons, provides better results than other controllers. All these results demonstrate that the performance and robustness of the proposed controller are competitive.

关键词： Voltage control Generators whale optimization algorithms Transfer functions Tuning Synchronous generators Automatic voltage control Optimal control Automatic voltage regulator PIDD2 controller optimal control hybrid whale optimization algorithm

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UAV-NavS: Three-Dimensional Navigation System of Multiple Unmanned Aerial Vehicles Using Hybrid optimization Algorithm

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IEEE ACCESS 2025年 13卷 20247-20259页

作者： Digra, Monia Jain, Upma Dewangan, Ram Kishan Suyal, Himanshu Jain Dept CSE FET Bengaluru 560069 India Graph Era Dept Comp Sci & Engn Dehra Dun 248002 India Bennett Univ Sch Comp Sci Engn & Technol Greater Noida 201310 India

This paper proposes a hybrid approach for multiple Unmanned Aerial Vehicle navigation. This is an NP-hard problem since the robots must find the optimal safe path without colliding with other robots and obstacles in a three-dimensional search space. The proposed approach enhances the exploration capabilities of the whale optimization algorithm. Then, it hybridises this improved whale optimization algorithm with the sine cosine algorithm to improve the overall exploitation capabilities. The efficiency of the proposed hybrid approach is compared with other meta-heuristic algorithms for multi-UAV navigation. Results obtained through simulation ensure the validity of the proposed approach.

关键词： Navigation Autonomous aerial vehicles Three-dimensional displays Robots Metaheuristics Heuristic algorithms whale optimization algorithms Trajectory Polynomials NP-hard problem UAV whale optimization algorithm sine cosine algorithm navigation deterministic meta-heuristic hybrid algorithm

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Research on Interference Resource optimization Based on Improved whale optimization Algorithm

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IEEE ACCESS 2025年 13卷 83136-83147页

作者： Chen, Xuyi Ma, Mingxi Liu, Chengkui Xie, Haifeng Wang, Shaoqi Air Force Command Coll Beijing 100080 Peoples R China Army 96716 Ganzhou 341001 Peoples R China

Interference resource optimization is a prerequisite for air defense suppression mission planning, and the degree of optimization of interference resources directly determines the quality of the interference results. In this problem, this paper establishes an interference resource optimization model with radar detection probability, interference effectiveness, and interference bandwidth utilization as the objective functions. Then, in the solution process, to address the issues of the whale optimization Algorithm (WOA) easily falling into local optima and low convergence accuracy, the BIO-WOA (Bernoulli Chaotic mapping In-nonlinear Factors and Opposition-based Learning Improved whale optimization Algorithm, BIO-WOA) is proposed. First, the population initialization is completed using Bernoulli chaotic mapping based on the whale optimization algorithm, increasing the diversity and uniformity of solutions and enhancing the algorithm's global search capability. Then, a nonlinear convergence factor is proposed to balance the local and global search capabilities of the algorithm. Subsequently, the centroid opposition-based learning is used to generate mutated whales, improving the algorithm's ability to escape local optima. Finally, the effectiveness of the algorithm is verified through test functions and simulation experiments.

关键词： Interference Radar Jamming whale optimization algorithms optimization Radar detection Heuristic algorithms Resource management Radar measurements Linear programming whale optimization algorithm chaotic mapping nonlinear convergence factor centroid opposition-based learning interference resource optimization

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