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Multi-objective group learning algorithm with a multi-objective real-world engineering problem

APPLIED SOFT COMPUTING 2024年 166卷

作者： Rahman, Chnoor M. Mohammed, Hardi M. Abdul, Zrar Khalid Charmo Univ Coll Sci Comp Dept Sulaymaniyah Iraq

The concern of multi-objective optimization is to deal with optimization problems with more than one objective that should be optimized at the same time. In this paper, a new multi-objective optimization algorithm called multi-objective group learning algorithm is examined. The algorithm is based on the effect of group leaders on the group members and the effect that the group members have on each other. The algorithm is evaluated against the five Zitzler, Deb and Thiele (ZDT) benchmark functions, two standard functions of the DTLZ benchmarks, and a real-world engineering problem. To quantitatively examine the ability of the algorithm four metrics are utilized. Additionally, to statistically confirm the results, standard deviation and the average metrics are used. Moreover, the Wilcoxon rank sum test is used to examine the importance of the results statistically. Finally, the results of the algorithm are evaluated against multi-objective ant lion optimizer, multi-objective evolutionary algorithm based on decomposition, Pareto envelop-based selection algorithm-II, and multi-object moth swarm algorithm. The results of the benchmark functions showed that the proposed work produced better results in most of the metrics for three out of five ZDT benchmarks, and for the rest of the benchmarks it produced better results in at least one of the metrics compared to the participated algorithms. Moreover, the result of the real world engineering problem proved the ability of the proposed algorithm in producing acceptable and better Pareto front in almost all the metrics. For the proposed algorithm, the result of the generational distance for the speed reducer design problem is 0.8002, whereas it is 7.9375710, and 61.98134 for the multi-objective evolutionary algorithm based on decomposition, multi-objective ant lion optimizer, respectively. Additionally, the produced result for the spacing metric by the proposed algorithm was smaller than the result of the other algorithms for

关键词： optimization Multi-objective optimization Evolutionary algorithm Group learning algorithm metaheuristic optimization algorithm Multi-objective group learning algorithm MOGLA

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Solar energy forecasting based on hybrid neural network and improved metaheuristic algorithm

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COMPUTATIONAL INTELLIGENCE 2018年第1期34卷 241-260页

作者： Abedinia, Oveis Amjady, Nima Ghadimi, Noradin Semnan Univ Elect & Comp Engn Fac Dept Elect Engn Semnan Iran Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil *** Iran

Prediction of solar power involves the knowledge of the sun, atmosphere and other parameters, and the scattering processes and the specifications of a solar energy plant that employs the sun's energy to generate solar power. This prediction result is essential for an efficient use of the solar power plant, the management of the electricity grid, and solar energy trading. However, because of nonlinear and nonstationary behavior of solar power time series, an efficient forecasting model is needed to predict it. Accordingly, in this paper, we propose a new forecast approach based on combination of a neural network with a metaheuristic algorithm as the hybrid forecasting engine. The metaheuristic algorithm optimizes the free parameters of the neural network. This approach also includes a 2-stage feature selection filter based on the information-theoretic criteria of mutual information and interaction gain, which filters out the ineffective input features. To demonstrate the effectiveness of the proposed forecast approach, it is implemented on a real-world engineering test case. Obtained results illustrate the superiority of the proposed approach in comparison with other prediction methods.

关键词： forecasting engine metaheuristic optimization algorithm neural network solar power

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Direct Model-Reference Adaptive Control for Wheelchair Simulator Control via a Haptic Interface

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IFAC-PapersOnLine 2022年第29期55卷 49-54页

作者： Amel Ait Ghezala Chouki Sentouh Philippe Pudlo

A wheelchair locomotion simulator (WCS) is an innovative solution to assess the biomechanical cost of wheelchairs (WC) accessibility in a controlled and safe virtual environment. In this context, this paper presents a haptic feedback control architecture based on a direct model reference adaptive control (MRAC) with intelligent tuning of its adaptation gains. The control objective is to follow the reference model velocity while producing the force feedback during the push phase, in order to faithfully recreate the dynamic behavior of the WC in a virtual environment. To accomplish this, a wheelchair ergometer model with friction is used to provide realistic navigation in the virtual environment (VE), by detecting and driving the wheelchair wheels. A two-wheeled vehicle model including the rolling resistance aspect is used to describe the wheelchair dynamic behavior. Since the controller adaptation gains are operated on the tracking error between the reference model and the simulator output, the WC model is also used as a reference model to specify the desired dynamics of the adaptive control system. For an optimal solution, an intelligent metaheuristic algorithm Elephant Herding optimization (EHO) is employed to optimize the controller gain adaptation parameter to keep the tracking error as small as possible. Finally, the simulation results obtained show the effectiveness of the proposed control strategy.

关键词： Direct MRAC system People with motor disabilities Wheelchair Simulator metaheuristic optimization algorithm Haptic interface Parameter estimation

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Integrated metaheuristic algorithms with extreme learning machine models for river streamflow prediction

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SCIENTIFIC REPORTS 2024年第1期14卷 1-26页

作者： Thieu, Nguyen Van Nguyen, Ngoc Hung Sherif, Mohsen El-Shafie, Ahmed Ahmed, Ali Najah PHENIKAA Univ Fac Comp Sci Ha Dong 12116 Hanoi Vietnam Artificial Intelligence Independent Res Grp Hanoi Vietnam United Arab Emirates Univ Coll Engn Civil & Environm Engn Dept POB 15551 Al Ain U Arab Emirates Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirate Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates Sunway Univ Sch Engn & Technol Dept Engn 5 Jalan Univ Bandar Sunway 47500 Selangor Darul Malaysia

Accurate river streamflow prediction is pivotal for effective resource planning and flood risk management. Traditional river streamflow forecasting models encounter challenges such as nonlinearity, stochastic behavior, and convergence reliability. To overcome these, we introduce novel hybrid models that combine extreme learning machines (ELM) with cutting-edge mathematical inspired metaheuristic optimization algorithms, including Pareto-like sequential sampling (PSS), weighted mean of vectors (INFO), and the Runge-Kutta optimizer (RUN). Our comparative assessment includes 20 hybrid models across eight metaheuristic categories, using streamflow data from the Aswan High Dam on the Nile River. Our findings highlight the superior performance of mathematically based models, which demonstrate enhanced predictive accuracy, robust convergence, and sustained stability. Specifically, the PSS-ELM model achieves superior performance with a root mean square error of 2.0667, a Pearson's correlation index (R) of 0.9374, and a Nash-Sutcliffe efficiency (NSE) of 0.8642. Additionally, INFO-ELM and RUN-ELM models exhibit robust convergence with mean absolute percentage errors of 15.21% and 15.28% respectively, a mean absolute errors of 1.2145 and 1.2105, and high Kling-Gupta efficiencies values of 0.9113 and 0.9124, respectively. These findings suggest that the adoption of our proposed models significantly enhances water management strategies and reduces any risks.

关键词： River streamflow Forecasting model Extreme learning machine metaheuristic optimization algorithm Nature-inspired algorithms Nile river

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Chaotic Mapping Lion optimization algorithm-Based Node Localization Approach for Wireless Sensor Networks

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SENSORS 2023年第21期23卷 8699-8699页

作者： Motwakel, Abdelwahed Hashim, Aisha Hassan Abdalla Alamro, Hayam Alqahtani, Hamed Alotaibi, Faiz Abdullah Sayed, Ahmed Int Islamic Univ Malaysia Dept Elect & Comp Engn Kuala Lumpur 53100 Malaysia Prince Sattam Bint Abdulaziz Univ Coll Business Adm Hawtat Bani Tamim Dept Management Informat Syst Al Kharj 16278 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia King Khalid Univ Coll Comp Sci Ctr Artificial Intelligence Dept Informat SystUnit Cybersecur Abha 61421 Saudi Arabia King Saud Univ Coll Arts Dept Informat Sci POB 28095 Riyadh 11437 Saudi Arabia Future Univ Egypt Res Ctr New Cairo 11835 Egypt

Wireless Sensor Networks (WSNs) contain several small, autonomous sensor nodes (SNs) able to process, transfer, and wirelessly sense data. These networks find applications in various domains like environmental monitoring, industrial automation, healthcare, and surveillance. Node Localization (NL) is a major problem in WSNs, aiming to define the geographical positions of sensors correctly. Accurate localization is essential for distinct WSN applications comprising target tracking, environmental monitoring, and data routing. Therefore, this paper develops a Chaotic Mapping Lion optimization algorithm-based Node Localization Approach (CMLOA-NLA) for WSNs. The purpose of the CMLOA-NLA algorithm is to define the localization of unknown nodes based on the anchor nodes (ANs) as a reference point. In addition, the CMLOA is mainly derived from the combination of the tent chaotic mapping concept into the standard LOA, which tends to improve the convergence speed and precision of NL. With extensive simulations and comparison results with recent localization approaches, the effectual performance of the CMLOA-NLA technique is illustrated. The experimental outcomes demonstrate considerable improvement in terms of accuracy as well as efficiency. Furthermore, the CMLOA-NLA technique was demonstrated to be highly robust against localization error and transmission range with a minimum average localization error of 2.09%.

关键词： anchor nodes metaheuristic optimization algorithm node localization tent chaotic mapping wireless sensor networks

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Application of Particle Swarm optimization to a Hybrid H∞/Sliding Mode Controller Design for the Triple Inverted Pendulum System

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algorithmS 2024年第10期17卷 427页

作者： Shafeek, Yamama A. Ali, Hazem I. Univ Technol Iraq Control & Syst Engn Dept Baghdad Iraq

The robotics field of engineering has been witnessing rapid advancements and becoming widely engaged in our lives recently. Its application has pervaded various areas that range from household services to agriculture, industry, military, and health care. The humanoid robots are electro-mechanical devices that are constructed in the semblance of humans and have the ability to sense their environment and take actions accordingly. The control of humanoids is broken down to the following: sensing and perception, path planning, decision making, joint driving, stability and balance. In order to establish and develop control strategies for joint driving, stability and balance, the triple inverted pendulum is used as a benchmark. As the presence of uncertainty is inevitable in this system, the need to develop a robust controller arises. The robustness is often achieved at the expense of performance. Hence, the controller design has to be optimized based on the resultant control system's performance and the required torque. Particle Swarm optimization (PSO) is an excellent algorithm in finding global optima, and it can be of great help in automatic tuning of the controller design. This paper presents a hybrid H infinity/sliding mode controller optimized by the PSO algorithm to control the triple inverted pendulum system. The developed control system is tested by applying it to the nominal, perturbed by parameter variation, perturbed by external disturbance, and perturbed by measurement noise system. The average error in all cases is 0.053 deg and the steady controller effort range is from 0.13 to 0.621 N.m with respect to amplitude. The system's robustness is provided by the hybrid H-infinity/sliding mode controller and the system's performance and efficiency enhancement are provided by optimization.

关键词： metaheuristic optimization algorithm particle swarm optimization engineering design H-infinity sliding mode control triple inverted pendulum robust control uncertainty torque

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Regression based stochastic energy management strategy for a grid connected microgrid using Artificial Electric Field algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 8卷

作者： Patty, Sukriti Rani, Nibha Malakar, Tanmoy Electrical Engineering Department National Institute of Technology Silchar India

Recent decade has witnessed steep technological advancement in the renewable energy sector due to growing concern of climate change and emission cut target. The lack of availability of fossil fuels and the high price of the same have made the use of Renewable Energy Sources (RES) as a promising approach for generating clean and sustainable energy at local level. This persuades the implementation of Microgrid (MG) supported with RES, to cater various types of load demand. However, planning and optimizing a MG operation have become challenging due to the intermittent nature of RES and uncertain loads. To address this issue, this paper introduces a Stochastic Energy Management Strategy (SEMS) for a grid connected MG comprising Photovoltaic, Wind Turbine, Fuel Cell, Micro Turbine, Battery Energy Storage and electrical as well as heat energy demand. The volatilities of RES power output and energy demand are modeled via Gaussian Process Regression (GPR) and Monte Carlo Simulation (MCS) respectively. The problem is framed as non-linear (NL) dynamic stochastic optimization problem and solved using ‘Artificial Electric Field algorithm (AEFA)’. Here, minimization of expected value of operational and emission cost are taken as objective functions;while various practicality aspects are modeled as constraints in the problem. Further, the efficacy of the proposed approach is validated around the first central moment of the load. Various case studies are performed to estimate the day ahead optimum operating schedule of the Distributed Energy Resources (DER) and to assess the impact of load uncertainties on DER operation and total MG operation cost. Further, the efficacy of the GPR is verified through comparison with MCS based result. © 2024

关键词： Energy management metaheuristic optimization algorithm Microgrid Regression model Renewable energy source

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A comparative analysis of global optimization algorithms for surface electromyographic signal onset detection

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Decision Analytics Journal 2023年 8卷

作者： Alam, Shafiq Zhao, Xin Niazi, Imran Khan Ayub, Muhammad Sohaib Khan, Muhammad Asad School of Management Massey University Auckland New Zealand School of Computing Whitireia Community Polytechnic Auckland New Zealand New Zealand College of Chiropractic //AUT//AAU Auckland New Zealand Department of Computer Science Lahore University of Management Sciences Lahore Pakistan Department of Telecommunication Hazara University Mansehra Pakistan

Surface Electromyography (sEMG) is a technique for measuring muscle activity by recording electrical signals from the surface of the body. It is widely used in fields such as medical diagnosis, human–computer interaction, and sports injury rehabilitation. The detection of the onset and offset of muscle activation is a longstanding challenge in sEMG analysis. This study pioneers the implementation, configuration, and evaluation of Particle Swarm optimization (PSO) against other optimization algorithms for sEMG signal detection, including Genetic algorithms (GA), Simulated Annealing (SA), Ant Colony optimization (ACO), and Tabu Search (TS). The results show that the PSO algorithm achieves the highest median accuracy and F1-Score and is the fastest among the selected algorithms but has lower stability compared to Genetic algorithms and Ant colony optimization. The design and value of the cost function had a significant impact on the results, with optimal results obtained when the cost value was between 0.1203 and 0.1384. The use of these algorithms improved detection efficiency and reduced the need for manual parameter adjustment. To the best of our knowledge, no published studies have utilized Simulated Annealing, Ant colony optimization, and Tabu search meta-heuristic algorithms to detect sEMG signal onsets. © 2023 The Authors

关键词： Ant colony optimization Genetic algorithm metaheuristic optimization algorithm Onset detection Particle swarm optimization Simulated annealing Surface electromyography Tabu search

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