检索结果-内蒙古大学图书馆

Design a new cascade controller PD-P-PID optimized by marine predators algorithm for load frequency control

SOFT COMPUTING 2023年第14期27卷 9551-9564页

作者： Halmous, Abdelkader Oubbati, Youcef Lahdeb, Mohamed Arif, Salem Univ Amar TELIDJI LACoSERE Lab Laghouat 03000 Algeria

For a stable system frequency without any problems, the generation and demand equation must always be balanced. But the difficulty of precisely predicting the changes in the load is a big problem. Here comes the responsibility of the load frequency control (LFC) loop to maintain the equality between supply and demand. The LFC loop's performance depends on the control technique or controller used. In our work, we propose a new cascade controller PD-P-PID that is applied in LFC applications for the first time. To obtain the optimum settings of our cascade controller, we used an optimization method called the marine predators algorithm (MPA). To verify the robustness of our controller, it was tested on three different systems: a single-area multi-source power system, a two-area multi-source power system with AC-DC tie-lines, and a three-area hydro-thermal power system with the generation rate constraints and Governor dead band taken into account for a more practical study. The obtained results are compared with many published works based on objective function integral time absolute error, settling time Ts, and overshoot/undershoot of frequency and tie-line power deviation. MPA-tuned PID-PD controllers offer superior performance to other literature as it gives a dynamic response with a shorter settling time and fewer frequency and tie-line power deviation oscillations when step loads are perturbed.

关键词： Load frequency control Cascade controller PD-P-PID marine predators algorithm Optimization Interconnected power system

来源：评论

学校读者我要写书评

暂无评论

A Robust Strategy Based on marine predators algorithm for Large Scale Photovoltaic Array Reconfiguration to Mitigate the Partial Shading Effect on the Performance of PV System

引用

IEEE ACCESS 2020年 8卷 112407-112426页

作者： Yousri, Dalia Babu, Thanikanti Sudhakar Beshr, Eman Eteiba, Magdy B. Allam, Dalia Fayoum Univ Fac Engn Elect Engn Dept Faiyum 63514 Egypt Univ Tenaga Nas Inst Power Engn Dept Elect Power Engn Kajang 43000 Malaysia Arab Acad Sci Technol & Maritime Transport Elect & Control Engn Dept Cairo 11311 Egypt

Large-scale solar photovoltaic (PV) plants play an essential role in providing the increasing demand for energy in recent time. Therefore, in the purpose of achieving the highest harvested power under the partial shading conditions as well as protecting the PV array from the hot-spot calamity, the PV reconfiguration strategy is established as an efficient procedure. This is performed by redistribution of PV modules according to their levels of shading. Motivated by this, the authors in this article have introduced a novel population-based algorithm that is known as marine predators algorithm (MPA) to restructure the PV array dynamically. Moreover, a novel objective function is introduced to enhance the algorithm performance rather than utilizing the regular weighted objective function in the literature. The effectiveness of the proposed algorithms based on the novel objective function is evaluated using several metrics such as fill factor, mismatch losses, percentage of power loss, and percentage of power enhancement. Besides, the obtained results are compared with a regular total-cross-tied (TCT) connection, manta ray foraging optimization (MRFO), harris hawk optimizer (HHO) and particle swarm optimizer (PSO) based reconfiguration techniques. Furthermore, to demonstrate the suitability of the proposed methods, large scale PV arrays of 16 x 16 and 25 x 25 are considered and evaluated. The results reveal that MPA enhanced the PV array power by percentage of 28.6 %, 2.7 % and 5.7 % in cases of 9 x 9, 16 x 16 and 25 x 25 PV arrays, respectively. The comprehensive comparisons endorse that MPA shows a successful shade dispersion;hence the number of multiple peaks in the PV characteristics has reduced, and high values of power have been harvested with least mean execution time in comparison with PSO, HHO and MRFO. Moreover, the Wilcoxon signed-rank test has been accomplished to confirm the reliability and applicability of the proposed approach for the PV large scale array

关键词： Integrated circuit modeling Linear programming Heuristic algorithms Optimization Maximum power point trackers Switches Renewable energy energy efficiency PV reconfiguration partial shading marine predators algorithm partial shading optimization

来源：评论

学校读者我要写书评

暂无评论

3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution

引用

EGYPTIAN INFORMATICS JOURNAL 2024年 28卷

作者： Tu, Binbin Wang, Fei Han, Xiaowei Shenyang Univ Coll Intelligent Syst Sci & Engn Shenyang Peoples R China Shenyang Univ Coll Informat Engn Shenyang Peoples R China Shenyang Univ Inst Innovat Sci & Technol Shenyang Peoples R China

3D path planning is a critical requirement for the autonomy of unmanned aerial vehicle (UAV) navigation systems. In this paper, we propose a novel approach, the differential evolution combined marine predators algorithm (DECMPA), specifically tailored to address the UAV 3D path planning problem in complex scenarios. To handle stringent constraints, DECMPA adopts a multi-step approach. Initially, it utilizes quasi-oppositional learning to generate a more uniformly distributed initial population. Throughout the iterative process, it probabilistically generates quasi-oppositional populations and merges them with existing populations, selecting the superior individuals for the next generation to mitigate local optima. Additionally, DECMPA integrates the variance, crossover, and selection processes of differential evolution into the marine predators algorithm iterations. The greedy selection of test and target vectors further accelerates population convergence. Numerical optimization experiments are conducted using the 10 and 20-dimensional CEC 2021 test suite. Compared to other algorithms, DECMPA exhibits superior solution accuracy and convergence speed, resulting in substantial performance enhancement, thus establishing itself as an efficient algorithm. Furthermore, the adoption of spherical vector coordinates in solution encoding ensures higher quality and facilitates the production of feasible solutions. To verify the effectiveness and practicality of the proposed algorithm, comprehensive path planning simulation experiments are conducted. DECMPA is compared against nine other state-of-the-art heuristic algorithms across six 3D scenarios of varying complexity. The experimental results demonstrate DECMPA's superiority in terms of optimal cost acquisition, solution quality, and stability. In conclusion, DECMPA presents a promising solution for addressing the challenges of UAV 3D path planning in complex environments. Its innovative approach and superior performance und

关键词： 3D Path Planning UAV marine predators algorithm Differential Evolution Quasi-oppositional Learning Spherical Vector Coordinates

来源：评论

学校读者我要写书评

暂无评论

An Efficient marine predators algorithm for Feature Selection

引用

IEEE ACCESS 2021年 9卷 60136-60153页

作者： Abd Elminaam, Diaa Salama Nabil, Ayman Ibraheem, Shimaa A. Houssein, Essam H. Benha Univ Fac Comp & Artificial Intelligence Banha 13518 Egypt Misr Int Univ Fac Comp & Informat Cairo 611310 Egypt Higher Technol Inst Comp Sci Dept 10th Ramadan City 32987 Egypt Minia Univ Fac Computers & Informat Al Minya 61519 Egypt

Feature Selection (F.S.) reduces the number of features by removing unnecessary, redundant, and noisy information while keeping a relatively decent classification accuracy. F.S. can be considered an optimization problem. As the problem is challenging and there are many local solutions, stochastic optimization algorithms may be beneficial. This paper proposes a novel approach to dimension reduction in feature selection. As a seminal attempt, this work uses binary variants of the recent marine predators algorithm (MPA) to select the optimal feature subset to improve classification accuracy. MPA is a new and novel nature-inspired metaheuristic. This research proposes an algorithm that is a hybridization between MPA and k-Nearest Neighbors (k-NN) called MPA-KNN. K-Nearest Neighbors (k-NN) is used to evaluate the selected features on medical datasets with feature sizes ranging from tiny to massive. The proposed methods are evaluated on 18 well-known UCI medical dataset benchmarks and compared with eight well-regarded metaheuristic wrapper-based approaches. The core exploratory and exploitative processes are adapted in MPA to select the optimal and meaningful features for achieving the most accurate classification. The results show that the proposed MPA-KNN approach had a remarkable capability to select the optimal and significant features. It performed better than the well-established metaheuristic algorithms we tested. The algorithms we used for comparison are Grey Wolf Optimizer (GWO), MothFlame Optimization algorithm (MFO), Sine Cosine algorithm (SCA), Whale Optimization algorithm (WOA), Slap Swarm algorithm (SSA), Butterfly Optimization algorithm (BFO), and Harris Hawks Optimization (HHO). This paper is the first work that implements MPA for Feature Selection problems. The results ensure that the proposed MPA-KNN approach has a remarkable capability to select the optimal and significant features and performed better than several metaheuristic algorithms. MPA-KNN achi

关键词： Feature selection marine predators algorithm metaheuristics k-nearest neighbors exploitation phase

来源：评论

学校读者我要写书评

暂无评论

A Hybrid marine predators algorithm with Particle Swarm Optimization Using Renewable Energy Sources for Energy Scheduling Problem-Based IoT

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第9期49卷 12655-12678页

作者： Makhadmeh, Sharif Naser Al-Betar, Mohammed Azmi Abasi, Ammar Kamal Al-Redhaei, Anessa Alomari, Osama Ahmad Kouka, Shaimaa Univ Petra Dept Data Sci & Artificial Intelligence Amman 11196 Jordan Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr AIRC Univ St Ajman 00000 U Arab Emirates Mohamed Bin Zayed Univ Artificial Intelligence MBZ Machine Learning Dept Abu Dhabi U Arab Emirates Abu Dhabi Univ Coll Engn Dept Comp Sci & Informat Technol Abu Dhabi U Arab Emirates

The energy scheduling problem (ESP) is a complex NP-hard optimization scheduling problem of finding the best schedule for the energy consumed through a time horizon by smart appliances according to several constraints and dynamic pricing scheme(s). The primary goal of addressing ESP is to optimize electricity bills for users, the highest energy consumption through a time horizon, and the comfort level of users. Several communication and controlling techniques were utilized to interconnect and centralize the smart appliances to be scheduled, where the Internet of Things (IoT) technique is the most helpful. Several studies have proposed different optimization methods to address the ESP optimally. Unfortunately, due to the low performance of the utilized methods and the high number of the problem's constraints, the best schedules were not achieved in many cases. In this paper, a new improved optimization method on the basis of a hybridization approach between two well-established optimization methods, called the marine predators algorithm and particle swarm optimization, is proposed to handle ESP optimally. The proposed method is mainly established and utilized to enhance the schedules with the worse fitness values to improve them to be more acceptable. Furthermore, to avoid the high complexity of the ESP constraints, Renewable Energy Sources (RESs) based on real-world dataset is utilized along the proposed method. Also, the IoT technique is utilized to establish the connection and build the scheduling system. Finally, to optimize the ESP objectives simultaneously, ESP is formulated as a multi-objective problem. In the evaluation stage, the proposed hybrid method with RESs is tested among four different comparison studies and phases, including the adapted methods with and without RESs, hybrid methods with and without RESs, and based state-of-the-arts. The proposed hybrid method with RESs proves its efficiency and high performance in all comparison phases, where it achi

关键词： Energy scheduling problem marine predators algorithm Particle swarm optimization Renewable energy sources Multi-objective approach

来源：评论

学校读者我要写书评

暂无评论

Discrete marine predators algorithm for Symmetric Travelling Salesman Problem

引用

EVOLUTIONARY INTELLIGENCE 2024年第5-6期17卷 3833-3848页

作者： Kumar, Manish Panwar, Karuna Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Indian Inst Technol Roorkee Mehta Family Sch Data Sci & Artificial Intelligenc Roorkee 247667 Uttarakhand India

This paper addresses the well-known NP-hard problem, the Symmetric Travelling Salesman Problem (STSP), which has numerous applications in logistics and graph theory. To solve this complex problem, a discrete version of the marine predators algorithm (D-MPA) is introduced. Although MPA has been extensively studied for various optimization problems and real-world applications, its use in routing problems, particularly TSP, remains relatively unexplored. To adapt MPA for STSP, the positions of individuals are initially updated using the classical MPA algorithm. The continuous values generated by the classical MPA are then converted to discrete values, followed by the application of a permutation operator to maintain diversity in the population. In addition, a local search algorithm, 2-opt, is applied to further improve the results for STSP instances. The effectiveness of the proposed algorithm is demonstrated on STSP benchmark instances of sizes ranging from 14 to 439. The performance of D-MPA is compared against several existing algorithms, including the Genetic algorithm (GA), Bat algorithm (BA), Discrete Firefly algorithm (DFA), Evolutionary Simulated Annealing (ESA), Island-Based Distributed Genetic algorithm (IDGA), Discrete Imperialist Competitive algorithm (DICA), and Discrete Grey Wolf Optimization (DGWO). To ensure an unbiased and rigorous comparison, descriptive statistics such as mean and standard deviation are used, and a Wilcoxon Signed-Rank test is conducted for statistical validation. The results demonstrate that D-MPA not only competes, but also consistently outperforms these algorithms, making it a valuable resource for the research community.

关键词： Travelling Salesman Problem Metaheuristic algorithm marine predators algorithm 2-opt algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of the marine predators algorithm for optimizing the performance of water supply reservoirs

引用

ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY 2024年 1-22页

作者： Moradi-Far, Shirin Ashofteh, Parisa-Sadat Loaiciga, Hugo A. Univ Qom Dept Civil Engn Qom Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93016 USA

Optimal reservoir operation involves complex decision making. The marine predators algorithm (MPA) is herein applied and evaluated with several mathematical functions and with the optimized operation of the Aydogmush reservoir (East Azerbaijan province, Iran) that minimizes the deficit of agricultural water supplied to downstream lands. Reservoir operation covers a baseline period (1987-2000) and a period of climate change (2026-2039). The MPA's reservoir operation results are compared those obtained with the genetic algorithm (GA). The calculated operating policies are evaluated based on indexes of reliability, resiliency and vulnerability. A comparison of reservoir water releases, water supply deficit and reservoir storage from five runs under baseline and climate change periods indicates better performance of the MPA-calculated reservoir operation than the GAs in meeting downstream water demand. The efficiency indexes show that, for example, the reliability of the operating policy obtained with the MPA is larger by 54 and 34% compared to the GAs in the baseline and the climate change periods, respectively.

关键词： Optimization Reservoir management marine predators algorithm Efficiency indexes Climate change

来源：评论

学校读者我要写书评

暂无评论

Identification of the Continuous-Time Hammerstein Models with Sparse Measurement Data Using Improved marine predators algorithm

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-26页

作者： Tumari, Mohd Zaidi Mohd Ahmad, Mohd Ashraf Mohamed, Zaharuddin Univ Tekn Malaysia Melaka Fac Elect Technol & Engn Melaka 76100 Malaysia Univ Malaysia Pahang Al Sultan Abdullah Ctr Adv Ind Technol Pekan 26600 Pahang Malaysia Univ Teknol Malaysia Fac Elect Engn Johor Baharu 81310 Johor Malaysia

In contemporary industrial applications, the complexity of systems often makes it challenging to create precise models using first-principle approaches. Consequently, researchers have turned to data-driven modeling, which offers the key advantage of developing a mathematical model of the system entirely from the input-output data captured from an actual plant. However, acquiring complete input-output data can be challenging in numerous industrial applications, where sparse measurement data frequently arise when identifying the model of these systems. Therefore, this study introduced data-driven modeling for continuous-time Hammerstein models in the presence of sparse measurement data. The analysis employed the random average marine predators algorithm (RAMPA) with a tunable step-size adaptive coefficient (CF) (RAMPA-TCF), which offers significant advantages over the conventional MPA by preventing stagnation in the local optima and enhancing the balance between the exploration and exploitation stages. Here, the structure of the unknown nonlinear subsystem was assumed to be a piecewise affine function. Meanwhile, the structure of the linear subsystem was represented by a continuous-time transfer function. Subsequently, we applied RAMPA-TCF to identify the parameters of one numerical example and a twin-rotor system (TRS) under various sparse measurement data cases. Our results demonstrated the superiority of RAMPA-TCF across several performance criteria, including the convergence curve, statistical analysis of the objective function, parameter deviation index, time- and frequency-domain responses, and Wilcoxon's rank sum test. Notably, RAMPA-TCF improved the objective function results by over 5% in the numerical example and achieved more than a 30% improvement in the TRS compared to the conventional MPA.

关键词： marine predators algorithm Hammerstein models Block-oriented models Metaheuristic algorithms System identification

来源：评论

学校读者我要写书评

暂无评论

Conceptual comparison of the ecogeography-based algorithm, equilibrium algorithm, marine predators algorithm and slime mold algorithm for optimal product design

引用

MATERIALS TESTING 2021年第4期63卷 336-340页

作者： Yildiz, Betul Sultan Patel, Vivek Pholdee, Nantiwat Sait, Sadiq M. Bureerat, Sujin Yildiz, Ali Riza Bursa Uludag Univ Bursa Turkey Pandit Deendayal Petr Univ Mech Engn Dept Gandhinagar India KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Bursa Uludag Univ Dept Automot Engn Bursa Turkey

Vehicle component design is crucial for developing a vehicle prototype, as optimum parts can lead to cost reduction and performance enhancement of the vehicle system. The use of metaheuristics for vehicle component optimization has been commonplace due to several advantages: robustness and simplicity. This paper aims to demonstrate the shape design of a vehicle bracket by using a newly invented metaheuristic. The new optimizer is termed the ecogeography-based optimization algorithm (EBO). This is arguably the first vehicle design application of the new optimizer. The optimization problem is posed while EBO is implemented to solve the problem. It is found that the design results obtained from EBO are better when compared to other optimizers such as the equilibrium optimization algorithm, marine predators algorithm, slime mold algorithm.

关键词： Structural design Ecogeography-based optimization algorithm Equilibrium optimization algorithm marine predators algorithm Slime mould algorithm

来源：评论

学校读者我要写书评

暂无评论

Firefighting multi strategy marine predators algorithm for the early-stage Forest fire rescue problem

引用

APPLIED INTELLIGENCE 2023年第12期53卷 15496-15515页

作者： Chen, Jiaming Luo, Qifang Zhou, Yongquan Huang, Huajuan Guangxi Univ Nationalities Coll Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Labs Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China

This paper proposes a firefighting multi strategy marine predators algorithm(FMMPA), which uses opposition-based learning (OBL) to make the initial population more uniform, introduces adaptive weight factors to balance exploration ability and exploitation ability, and combines DE/RAND/1 mutation mechanism to increase population diversity. Another purpose of our FMMPA is to add fire weighted selection, including flame edge suppression, combustion unit continuity and wind direction, as the rescue algorithm of forest fire rescue assemble to deal with the allocation of firefighting aircraft in forest fire rescue. We conduct this study based on real maps and the capabilities of the real firefighting aircraft. Compared with ten algorithms, the experimental results show that the proposed FMMPA has superior performance in reducing rescue time, controlling the spread speed of fire edge and minimizing loss cost.

关键词： marine predators algorithm Forest fire rescue Rescue assemble Rescue simulator Rescue algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：