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Whale optimization and sine-cosine optimization algorithms with cellular topology for parameter identification of chaotic systems and Schottky barrier diode models

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SOFT COMPUTING 2021年第2期25卷 1365-1409页

作者： Turgut, Mert Sinan Sagban, H. Muzaffer Turgut, Oguz Emrah Ozmen, Ozge Tuzun Ege Univ Fac Engn Dept Mech Engn Izmir Turkey Duzce Univ Fac Arts & Sci Dept Phys Duzce Turkey Izmir Bakircay Univ Dept Ind Engn Fac Engn & Architecture Izmir Turkey Izmir Bakircay Univ Dept Fundamental Sci Fac Engn Izmir Turkey

This research study aims to enhance the optimization accuracy of the two recently emerged metaheuristics of whale and sine-cosine optimizers by means of the balanced improvements in intensification and diversification phases of the algorithms provided by cellular automata (CA). Stagnation at the early phases of the iterations, which leads to entrapment in local optimum points in the search space, is one of the inherent drawbacks of the metaheuristic algorithms. As a favorable solution alternative to this problem, different types of cellular topologies are implemented into these two algorithms with a view to ameliorating their search mechanisms. Exploitation of the fertile areas in the search domain is maintained by the interaction between the topological neighbors, whereas the improved exploration is resulted from the smooth diffusion of the available population information among the structured neighbors. Numerical experiments have been carried out to assess the optimization performance of the proposed cellular-based algorithms. Optimization benchmark problems comprised of unimodal and multimodal test functions have been applied and numerical results have been compared with those found by some of the state-of-the-art literature optimizers including particle swarm optimization, differential evolution, artificial cooperative search and differential search. Cellular variants have been outperformed by the base algorithms for multimodal benchmark problems of Levy and Penalized1 functions. Then, the proposed cellular algorithms have been applied to two different parameter identification cases in order to test their efficiencies on real-world optimization problems. Extensive performance evaluations on different parameter optimization cases reveal that incorporating the CA concepts on these algorithms not only improves the optimization accuracy but also provides considerable robustness to acquired solutions.

关键词： Cellular automata Chaotic systems Parameter identification Schottky barrier diodes sine-cosine algorithm Whale optimization

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Bio-inspired Red Fox-sine cosine optimization for the feature selection of SSVEP-based EEG signals for BCI applications

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2023年第Part1期80卷 000-000页

作者： Bhuvaneshwari, M. Kanaga, E. Grace Mary Anitha, J. Karunya Inst Technol & Sci Coimbatore Tamilnadu India

Background: Advancements in Brain-Computer Interface (BCI) have led to the development of various neuro-dysfunctional human assistive tools. Despite having various human-assistive tools, communication is one of the prime barriers for paralytic people. Preliminary investigations to resolve the communication problem of the paralytic people have revealed that the Steady State Visually Evoked Potential (SSVEP) signal classification has the potential to decode the needs of the paralytic people. In that, optimal feature selection of SSVEP-based EEG signals has a dominant role to make the signal classification more effective in terms of accuracy and computation time. The optimal feature selection is one of the critical and time-consuming tasks hence it has been formulated as an optimization problem. New Methods: To select the optimal features subset a hybrid Red Fox and sine-cosine Optimization algorithm (RFO_SCA) is proposed in this paper. The error minimization function of the classifier is used as a fitness function for RFO_SCA. For local optimal subset selection, sine-cosine algorithm (SCA) is employed to search for the best local optimal solution. This strategy improves the local search efficiency of Red Fox Optimization (RFO). The selected optimal features are further classified using k-NN, decision tree, and random forest classifier. Results: To validate the efficiency of RFO_SCA in feature optimization, the experiment has been carried out in two datasets such as (i) the acquired dataset and (ii) EEG SSVEP dataset III from the MAMEM database. The experimental results show that RFO_SCA outperforms other optimization algorithms with an accuracy of 98.74% and 92.14% for the acquired and standard datasets respectively. Also, the feature selection using RFO_SCA has reduced the feature size by 50% and shows a high convergence rate when compared to other optimization algorithms.

关键词： BCI EEG SSVEP Red Fox Optimization sine-cosine algorithm Optimal Feature Selection

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Real-time validation of optimal energy management in DC microgrids by using modified rejection controller based improved sparrow search algorithm

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e-Prime - Advances in Electrical Engineering, Electronics and Energy 2024年 7卷

作者： Anjaiah, Kanche Dash, P.K. Bisoi, Ranjeeta Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

For optimal energy management in the DC microgrid, there are two major challenges, such as minimization of operational cost and balancing the power flow. Most of the benchmark techniques are used to develop energy management systems (EMS) by demonstrating either cost optimization or availability of power optimization. Unlike, a new optimal energy management (OEM) approach is introduced in this paper by considering both the operational cost and power flow during the grid-connected scenario in the DC ring microgrid (DCRM). To attain the OEM in the DCRM, a multi-objective optimization model is developed by utilizing major constraints, such as minimization of operational cost and power availability through a modified active disturbance rejection controller (M-ADRC) based improved sparrow search algorithm (ISSA). The improved nature of SSA is obtained by hybridizing the sine-cosine and Tent chaotic functions with SSA, i.e., SC-CSSA to avoid premature convergence in SSA. On the other hand, the disturbance rejection controller is modified by replacing the extended state observer (ESO) with a communication delay in non-linear ESO to enhance the robustness of the proposed DCRM. The main role of this controller is to regulate the fluctuations by maintaining stability in the voltage profile during various operating conditions. Further, the proposed M-ADRC based SC-CSSA algorithm results are validated over benchmark techniques to evidence the superiority of the proposed algorithm. To ensure realistic performance and robustness, the algorithm is validated in a real-time environment through hardware-in-the-loop (HIL) operation with the dSPACE DS1104 embedded processor. This testing approach guarantees simplicity, practicality, and effectiveness. © 2024 The Author(s)

关键词： Active disturbance rejection control Chaotic functions Cost DC ring microgrid (DCRM) Energy management system (EMS) Power sine-cosine algorithm Sparrow search algorithm (SSA) State of charge

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UAV Path Planning algorithm Based on Improved Harris Hawks Optimization

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SENSORS 2022年第14期22卷 5232-5232页

作者： Zhang, Ran Li, Sen Ding, Yuanming Qin, Xutong Xia, Qingyu Dalian Univ Sch Informat Engn Dalian 116622 Peoples R China Dalian Univ Commun & Network Lab Dalian 116622 Peoples R China

In the Unmanned Aerial Vehicle (UAV) system, finding a flight planning path with low cost and fast search speed is an important problem. However, in the complex three-dimensional (3D) flight environment, the planning effect of many algorithms is not ideal. In order to improve its performance, this paper proposes a UAV path planning algorithm based on improved Harris Hawks Optimization (HHO). A 3D mission space model and a flight path cost function are first established to transform the path planning problem into a multidimensional function optimization problem. HHO is then improved for path planning, where the Cauchy mutation strategy and adaptive weight are introduced in the exploration process in order to increase the population diversity, expand the search space and improve the search ability. In addition, in order to reduce the possibility of falling into local extremum, the sine-cosine algorithm (SCA) is used and its oscillation characteristics are considered to gradually converge to the optimal solution. The simulation results show that the proposed algorithm has high optimization accuracy, convergence speed and robustness, and it can generate a more optimized path planning result for UAVs.

关键词： flight path planning Harris Hawks optimization Cauchy mutation strategy adaptive weight sine-cosine algorithm unmanned aerial vehicle system

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Smart coordinated management of distribution networks with high penetration of PEVs using FLC

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第3期14卷 476-485页

作者： Zare, Mohsen Chabok, Hossein Niknam, Taher Azizipanah-Abarghooee, Rasoul Jahrom Univ Dept Elect Engn Fac Engn Jahrom Iran Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Natl Grid ESO Warwick CV34 6DA England

This paper is intended to give an effective coordination method between high penetration of plug in electric vehicles (PEVs) and optimal operation management (OOM) of distribution network problem. An effective fuzzy logic controller (FLC) is proposed which focuses on coordinated smart charge/discharge control of high penetration of PEVs with OOM in an iterative optimization process. In each iteration, the proposed FLC receives the voltage of the connection node of the PEVs, the remaining time to the peak load and the available energy of the PEVs while appropriately specifies the amount of absorbed or injected active power of the PEVs. This coordination leads to the minimization of the electric network losses, voltage flattening and reducing the produced pollutant. A modified version of sine-cosine algorithm (MSCA) is used to minimize the objective functions while the problem constraints are satisfied. An appropriate 33-bus distribution network with 1000 PEVs in 100 groups is considered in order to show the authenticity and efficiency of this work. The results show that the proposed FLC-based coordination procedure could effectively adjust the exchange power of the PEVs with the OOM decision variables in an acceptable time duration.

关键词： distributed power generation losses fuzzy control reactive power control optimisation battery powered vehicles power distribution control iterative methods distribution networks effective coordination method optimal operation management distribution network problem effective fuzzy logic controller electric network losses 33-bus distribution network PEV FLC-based coordination procedure smart coordinated management plug-in electric vehicles coordinated smart charge discharge control iterative optimisation process voltage flattening sine-cosine algorithm switched capacitors

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A comparative performance study of a closed-loop boost converter with classical and advanced controllers using simulation and real-time experimentation

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2020年第10期30卷 1-20页

作者： Ghosh, Arnab Banerjee, Subrata NIT Rourkela Dept Elect Engn Rourkela 769008 India NIT Durgapur Dept Elect Engn Durgapur India

Direct current (DC)-DC switched mode power converters are well recognized for their usefulness with wide-ranging applications and Boost converter is considered to be the most prominent one. In almost all applications, control system plays an important role and successful design of controllers is essential for good closed-loop converter's performance. In this work, different control algorithms like classical proportional integral derivative (PID) controller, sliding mode controller (SMC), internal model controller (IMC), sine-cosine algorithm (SCA) based optimized PID and IMC have been implemented for finding out suitable control option to get better regulations and closed-loop performance of proposed Boost converter. A comparative performance analysis is carried out to ascertain the most effective controller for proposed Boost converter. All the aforesaid designed controllers have been implemented by dSPACE real-time controller to justify the proposed work. It is observed that SCA-IMC exhibits better controlling action compared to PID, SMC, IMC, and SCA-PID in terms of transient and steady-state responses.

关键词： classical proportional integral derivative controller internal model controller optimized controller real-time implementation sine-cosine algorithm sliding mode controller switched-mode boost converter

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Whale Optimization Controller for Load Frequency Control of a Two-Area Multi-source Deregulated Power System

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INTERNATIONAL JOURNAL OF FUZZY SYSTEMS 2020年第1期22卷 122-137页

作者： Kumar, Rajiv Sharma, V. K. Bhagwant Univ Elect Engn Dept Ajmer 305004 Rajasthan India

This article discusses a Whale Optimization (WO) controller for design analysis, stability management, application, and performance analysis of a deregulated two-area multi-source energy system. Within the power grid, the Automatic Generation Control (AGC) maintains the power balance and strengthens the sudden frequency disturbance of the grid. Various researchers are targeted many kinds of fast-acting energy-storing devices to dampen space frequency oscillations and diversion of the tie line because of unexpected load variation. To regulate the power flow between multi-areas, load frequency control (LFC) is used by keeping frequency constant. But, to design a controller for minimizing the issues of LFC remains a challenge within the deregulated atmosphere. The main objective of the proposed methodology is to design a controller for minimizing the issues in LFC within the deregulated atmosphere. A two-area deregulated hydrothermal power system is employed to analyze many LFC problems in the deregulated power grid. During this modeling, the Thyristor-Controlled Phase Shifters (TCPS) and Capacitive Energy Storage unit (CES) are placed in the tie line and its control space. To mitigate the various issues of LFC in a two-area deregulated hydrothermal power system, the CES-TCPS combination is placed between the two control areas and the transient performance of constant is evaluated. The WO controller will increase the system power size to focus on the better performance of low-frequency management tasks. In addition, the effectiveness of the WO algorithm in tuning the proportional integral controller is also compared in a different contract (unilateral, bilateral and contract violation cases) scenarios of the proposed deregulated power system. Finally, the simulation is carried out in MATLAB/Simulink platform and the results obtained for the WO controller is compared with the normal and fuzzy PI controllers.

关键词： Automatic generation control Capacitive energy storage Deregulation Load frequency control Multi-source power system sine-cosine algorithm Whale optimization algorithm

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A New Hybrid Metaheuristic algorithm for Multiobjective Optimization Problems

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 920-940页

作者： Farag, M. A. El-Shorbagy, M. A. Mousa, A. A. El-Desoky, I. M. Menoufia Univ Fac Engn Dept Basic Engn Sci Shibin Al Kawm Egypt Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Al Kharj Dept Math Al Kharj 11942 Saudi Arabia Taif Univ Fac Sci Math & Stat Dept At Taif Saudi Arabia

The elitist nondominated sorting genetic algorithm (NSGA-II) is hybridized with the sine-cos le algorithm (SCA) in this paper to solve multiobjective optimization problems. The proposed hybrid algorithm is named nondominated sorting sine-cosine genetic algorithm (NS-SCGA). The main idea of this algorithm is the following: NS-SCGA integrates the merits of exploitation capability of NSGA-II and exploration capability of SCA for a better search ability and speeds up the searching process. 'Elie performance of NS-SCGA is tested on the set of benchmark functions provided for CEC09. The NS-SCGA results are compared with other recently developed multiobjective algorithms in terms of convergence, spacing, and spread of the obtained nondominated solutions to the true Pareto front. The statistical analysis of the results obtained is performed by nonparametric Friedman and Wilcoxon signed-rank tests. The results prove that NS-SCGA is superior to or competitive with other multiobjective optimization algorithms considered in the comparison. Furthermore, the economic emission dispatch problem (EEDP) is solved by NS-SCGA. operating cost (fuel cost) and pollutant emission of the standard IEEE 30-bus network with six generating units are minimized simultaneously by the NS-SCGA considering the losses. The results show the superiority of NS-SCGA and confirm its ability in solving EEDE TOESIS technique is applied to choose the best compromise solution from the obtained Pareto-optimal solutions of EEDP according to the decision maker's preference. (C) 2020 The Authors. Publishedby Atlantis Press SARL.

关键词： sine-cosine algorithm Nondominated sorting genetic algorithm Multiobjective optimization problems The economic emission dispatch problem TOPSIS

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Intelligent coordinators for automatic voltage regulator and power system stabiliser in a multi-machine power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2020年第23期14卷 5480-5490页

作者： Khezri, Rahmat Oshnoei, Arman Yazdani, Amirmehdi Mahmoudi, Amin Flinders Univ S Australia Coll Sci & Engn Adelaide SA Australia Shahid Beheshti Univ Fac Elect & Comp Engn Tehran Iran Murdoch Univ Coll Sci Hlth Engn & Educ Perth WA 6150 Australia

This study presents the design of intelligent coordinators for the automatic voltage regulator (AVR) and power system stabiliser (PSS) in a multi-machine power system. The intelligent coordinators are designed to update the gains of AVR and PSS in severe disturbances to guarantee the stability of the studied power system. Three potent intelligent coordinators are proposed: (a) fuzzy logic coordinator, (b) artificial neural network coordinator, and (c) brain emotional learning coordinator. Since the intelligent coordinators are based on the knowledge of the experts, desirable scaling factors are considered in the output signals of the coordinators to achieve optimal results. The scaling factors are optimised using a new evolutionary approach known as the sine-cosine algorithm. To evaluate the efficiency of the proposed intelligent approaches, the performances of coordinators are analysed on a two-area four-machine power system. A range of power system signals, such as rotor speed, terminal voltages, acceleration power and rotor angle of generators are demonstrated to approve and compare the performance of the intelligent coordinators. The simulation results indicate that the intelligent coordinators can guarantee the stability of the power system and satisfy performance objectives, such as desired transient and steady-state errors.

关键词： control system synthesis voltage control learning (artificial intelligence) genetic algorithms voltage regulators power system control rotors neurocontrollers fuzzy control power system stability multimachine power system artificial neural network coordinator brain emotional learning coordinator two-area four-machine power system power system signals intelligent coordinators automatic voltage regulator power system stabiliser PSS AVR fuzzy logic coordinator sine-cosine algorithm scaling factor rotor speed terminal voltages acceleration power transient error steady-state error performance objectives

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SCANM: A Novel Hybrid Metaheuristic algorithm and its Comparative Performance Assessment

ELECTRICA

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ELECTRICA 2022年第2期22卷 143-159页

作者： Kayri, Murat Ipek, Cengiz Izci, Davut Eker, Erdal Van Yuzuncu Yil Univ Dept Comp & Instruct Technol Educ Van Turkey Van Yuzuncu Yil Univ Dept Stat Van Turkey Batman Univ Dept Elect & Automat Batman Turkey Mus Alparslan Univ Vocat Sch Social Sci Mus Turkey

This paper proposes a novel sine-cosine and Nelder-Mead (SCANM) algorithm which hybridizes the sine-cosine algorithm (SCA) and Nelder-Mead (NM) local search method. The original version of SCA is prone to early convergence at the local minimum. The purpose of the SCANM algorithm is to overcome this issue. Thus, it aims to overcome this issue with the employment of the NM method. The SCANM algorithm was firstly compared with the SCA algorithm through 23 well-known test functions. The statistical assessment confirmed the better performance of the proposed algorithm. The comparative convergence profiles further demonstrated the significant performance improvement of the proposed SCANM algorithm. Besides, a non-parametric test was performed, and the results that showed the ability of the proposed approach were not by coincidence. A popular and well-performed metaheuristic algorithm known as grey wolf optimization was also used along with the recent and promising two other algorithms (Archimedes optimization and Harris hawks optimization) to comparatively demonstrate the performance of the SCANM algorithm against well-known classical benchmark functions and CEC 2017 test suite. The comparative assessment showed that the SCANM algorithm has promising performance for optimization problems. The non-parametric test further verified the better capability of the proposed SCANM algorithm for optimization problems.

关键词： sine-cosine algorithm Nelder-Mead simplex search method hybrid algorithm benchmark functions

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