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Leader selection based Multi-Objective flow direction algorithm (MOFDA): A novel approach for engineering design problems

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RESULTS IN ENGINEERING 2025年 25卷

作者： Khodadadi, Nima Ehteram, Mohammad Karami, Hojat Nadimi-Shahraki, Mohammad H. Abualigah, Laith Mirjalili, Seyedali Univ Miami Dept Civil & Architectural Engn Coral Gables FL USA Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Al Al Bayt Univ Comp Sci Dept Mafraq 25113 Jordan Torrens Univ Ctr Artificial Intelligence Res & Optimisat Adelaide Australia Obuda Univ Univ Res & Innovat Ctr EKIK H-1034 Budapest Hungary VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava 70080 Czech Republic

Addressing complex real-world issues with conflicting objectives is a significant challenge in optimization. Practical algorithms must balance these objectives, mainly when decision-maker preferences are unclear. This paper introduces a multi-objective adaptation of the flow direction algorithm (FDA) to address the shortcomings of traditional evolutionary and meta-heuristic optimization methods in multi-objective optimization (MOO). These conventional methods often fail to find Pareto optimal solutions and to represent all objectives fairly. Building on the FDA's success in single-objective tasks, we expanded its application to MOO, creating the MultiObjective flow direction algorithm (MOFDA). MOFDA incorporates new mechanisms to accurately and uniformly find optimal solutions for MOO challenges. It features a fixed-size external archive to maintain Pareto optimal solutions, uses a grid mechanism to improve non-dominated solutions within this archive, and implements a leader selection process to guide searches in the multi-objective space. These strategies enable MOFDA to discover superior solutions and ensure extensive coverage of the Pareto front. We validated MOFDA's effectiveness by testing it against 27 diverse problems using seven performance metrics. The results show MOFDA's ability to outperform well-known algorithms, achieving significant convergence and broad coverage, thus demonstrating its advanced capability in multi-objective optimization. The MOFDA source code is available at: https://***/algorithms-%2B-codes.

关键词： Multi-objective optimization flow direction algorithm Pareto optimality

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flow direction algorithm for optimal coordination of directional overcurrent relays considering arc flash

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RESULTS IN ENGINEERING 2025年 25卷

作者： Mahmoud, Dina K. Aleem, Shady H. E. Abdel Ibrahim, Ahmed M. Sayed, Mahmoud M. Abdelfattah, Wael El Shorouk Acad Higher Inst Engn El Shorouk City Dept Elect Power & Machines Engn Cairo 11837 Egypt Inst Aviat Engn & Technol Dept Elect Engn Giza 12658 Egypt Cairo Univ Fac Engn Elect Power Dept Giza Egypt

This paper presents a novel meta-heuristic optimization method called flow direction algorithm (FDA) to achieve optimal coordination of directional Overcurrent Relays (DOCRs) while considering the impact of Arc Flash (AF). The coordination between relays plays a significant role in the protection scheme of modern power systems, and achieving it is one of the most important challenges facing power system engineers. There are impacts such as incident energy (IE) caused by arc flash (AF) which must not exceed the threshold according to IEEE 1584 and NFPA 70E standards to increase workers' safety and reduce the danger of equipment damage. The objective of this study is to reduce the overall operation time of primary relays under certain constraints and thus reduce the amount of IE . The FDA is designed to address the highly nonlinear optimization problem of DOCRs coordination. The decision variables of this optimization problem are considered as the time dial setting and the pickup current or the plug setting. The effectiveness of the FDA method is verified by comparing it with well-known methods such as Gravitational Search algorithm, Imperialistic Competition algorithm, Sine Cosine algorithm, and Harmony Search algorithm. The method was tested on 8-bus and 15-bus systems, showing substantial improvements in optimal coordination, with FDA successfully limiting the IE to 40 cal/cm2. The FDA outperforms the other algorithms, achieving improvements of up to 73.18 %.

关键词： Arc flash directional overcurrent relays flow direction algorithm Incident energy Optimal relay coordination Optimization problem Relay setting

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Enhanced cascaded frequency controller optimized by flow direction algorithm for seaport hybrid microgrid powered by renewable energies

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APPLIED ENERGY 2024年 374卷

作者： Ansari, Zafar Ayub Raja, G. Lloyds Natl Inst Technol Patna Elect Engn Dept Patna 800005 Bihar India Loknayak Jai Prakash Inst Technol LNJPIT Dept Elect & Elect Engn Chapra Bihar India

With the rapid depletion of fossil fuels and its detrimental environmental concerns, renewable energy sources (RES) have been widely adopted in the ship power system for improving the energy flexibility at the seaport and making it easier to create a green maritime transit corridor. However, the problem of load frequency control in RES-based micro-grid systems is inescapable because of its low inertia, sporadic characteristics of RES and random load demand. Therefore, this paper presents a novel fractional order proportional integral-one plus tilt- derivative PI lambda-(1 lambda-(1 + TD) cascade controller for frequency regulation of seaport hybrid micro-grid (SHMG) system consisting of diverse RES including bio-diesel generator, fuel cell, organic Rankine cycle-based solar thermal power, wind turbine generator, sea wave energy and energy storage system. This study introduces a flow direction algorithm (FDA) to optimize the suggested hybrid cascaded PI lambda-(1 lambda-(1 + TD) controller with integral time square error serving as the objective function. The dominance of the FDA-optimized PI lambda-(1 lambda-(1 + TD) controller is proved over proportional-integral-derivative (PID), fractional-order PID (FOPID), tilt-integral-derivative (TID) and cascaded PI-PD control scheme by examining the dynamic behavior of SHMG when a variety of sea-shore loads are used as significant disruption to the considered system. Apart from a multi-step variation of load and renewable energy generation, various realistic scenarios such as random load variations, wind speed fluctuations, solar irradiance changes, availability uncertainty of RES and generating/storage units, communication time-delay and +/- 30% model perturbations are also considered to analyze the efficacy and resiliency of PI lambda-(1 lambda-(1 + TD) controller. The hybrid electrical energy source, flywheel and plug-in hybrid electric vehicles are used to support the SHMG for meeting the transient and steady-state powe

关键词： Seaport micro-grid Renewable energy sources flow direction algorithm Cascaded controller Energy storage system

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flow direction algorithm-based optimal power flow analysis in the presence of stochastic renewable energy sources

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ELECTRIC POWER SYSTEMS RESEARCH 2023年 216卷

作者： Maheshwari, Ankur Sood, Yog Raj Jaiswal, Supriya Natl Inst Technol Hamirpur Hamirpur 177005 Himachal Prades India NIT Hamirpur Dept Elect Engn Hamirpur India

In recent years, power systems have expanded significantly, especially with the incorporation of various renewable energy sources (RESs). The optimal operation of the existing modern power system is an opportunity to maximize energy efficiency by increasing stochastic RESs participation in the power grid. Generally, optimal power flow (OPF) is a complex, non-linear optimization problem, the complexity of which increases when sto-chastic RESs are integrated into the network. Therefore, this research article presents a new physics-based optimization method, namely, the flow direction algorithm (FDA), inspired by the movement of the flow directed toward the drainage basin outlet to solve OPF problems. The FDA algorithm find optimal solutions with more precision by strategically allocating a portion of the search process to global search and the remainder to local search. Three distinct RESs are considered in the proposed OPF model, solar photovoltaic, wind, and small hydropower generators. Uncertainties in wind speed and solar irradiation are addressed using Monte Carlo simulation, whereas small hydro unit is treated as a fixed power generating source. The FDA algorithm is validated on IEEE 30, 57, and 118-bus systems, and the results have been compared with the state-of-the-art algorithms. It is found that FDA provides better OPF solutions when compared to other recent existing methods.

关键词： Optimal power flow Uncertainty Metaheuristic algorithm flow direction algorithm Renewable energy sources Monte Carlo simulation

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flow direction algorithm and Biogeography-based optimization via fuzzy neural network approach for the prediction of high-performance concrete strength property

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ENGINEERING RESEARCH EXPRESS 2022年第4期4卷 045033-045033页

作者： Lin, Fang Jiangsu Urban & Rural Construct Vocat Coll Changzhou 213147 Jiangsu Peoples R China

Concrete is one of the most useful materials in the construction industry. Conventional concrete comprises additives such as cement, water, and aggregates. This concrete cannot be used for very important and sensitive structures. For this reason, high-performance concrete (HPC) has been used to achieve the desired and more suitable compressive strength by employing some additives. The additional variable is cement, fly ash, blast furnace slag, superplasticizer, fine aggregate, and coarse aggregate. On the other hand, to obtain a mixture of these materials, laboratory work is not economical and saves time. Therefore, soft-based modeling is the order of the day to solve this problem. The adaptive network fuzzy inference system model is one of the ways to achieve compressive strength close to the laboratory model, which is a smart modeling move. This model has to be optimized to get better and more satisfying results, which is done by two optimizers, Biogeography-Based Optimization (BBO) and flow direction algorithm (FDA), which have bright created and powerful for better performance. Furthermore, in the outputs of these two models, BBO-ANFIS and FDA-ANFIS, certain errors and desired percentages are used to select the most suitable and ideal model for the desired output, i.e., the compressibility of concrete in high-performance concrete. In the relevant modeling, the number of evaluators in the ANFISBBO combined model is R-2 = 0.8926, RMSE = 5.0406, MAE = 3.7145, A(20-index) = 0.8382 and U-95 = 13.881, and in ANFISFDA, R-2 = 0.912, RMSE = 4.7294, MAE = 3.5367, A(20-index) = 0.8414, and U-95 = 13.054 is obtained. According to the obtained numbers, it is clear that the ANFISFDA combined model has been able to get better results than the BBO-ANFIS model.

关键词： high-performance concrete compressive strength adaptive network fuzzy inference system flow direction algorithm biogeography-based optimization

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flow direction algorithm-based machine learning approaches for the prediction of high-performance concrete strength property

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ENGINEERING RESEARCH EXPRESS 2022年第3期4卷 035032-035032页

作者： He, Deng Zong-Wei, He Jie, Xu Changsha Social Work Coll Sch Software Changsha 410000 Peoples R China Jiaozuo Univ Sch Civil Construct & Engn Jiaozuo 412000 Peoples R China

The main significance of utilizing high-performance concrete as an effective item in the construction industry is the compressive strength assessment which requires a vast investigation of the design mix with calculated relevant compressive strength. Through the intelligence approaches, planning an accurate relationship between high-performance concrete different mix designs and their compressive strength is obtainable with the lowest cost of time and finance. Two models based on support vector regression methods are developed in this regard. The optimal output is calculated by tuning support vector regression key constraints by flow direction and biography-based optimization algorithm. The data set collected from the literature is divided into the training, and the testing phase, where the training data is used to develop the models, and the testing data is utilized to validate the accuracy of the models. The results showed a higher accuracy of the FDA_SVR method than the BBO_SVR method, with R-2 values of 0.9939 and 0.9755, respectively. moreover, the U95

关键词： compressive strength high-performance concrete flow direction algorithm biography-based optimization algorithm support vector machine

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Construction management multiple-objective trade-off problems using the flow direction algorithm (FDA)

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Asian Journal of Civil Engineering 2024年第4期25卷 3415-3429页

作者： Son, Pham Vu Hong Tri, Bui Nhat Faculty of Civil Engineering Ho Chi Minh City University of Technology (HCMUT) Vietnam National University Ho Chi Minh 700000 Viet Nam

The flow direction algorithm (FDA), a novel physics-based optimization method initially introduced by Karami (Karami et al., Computers & Industrial Engineering 156, 2021), is the focal point of this research. The FDA approaches the optimization problem by tracking the direction of the flow toward the drainage basin’s exit. Time, money, and quality (or safety, environment, etc.) are typically the most essential factors in project success. Due to the fact that most conflicting factors cannot coordinate concurrently to execute a project, optimizing time, cost, and quality is typically a must before completing a project. The optimization of trade-offs between time, money, quality, and other factors associated with construction projects is the focus of the current research. Then, by contrasting the algorithm with previous optimal findings, the method's ability to solve the optimal problem may be verified. After that, I use the study findings to inform the first decisions I make when working on a real project I am involved in, with the goal of optimizing time, cost, quality, or other factors of a construction project. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Artificial intelligence flow direction algorithm Optimization

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A two-stage method for model parameter identification based on the maximum power matching and improved flow direction algorithm

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ENERGY CONVERSION AND MANAGEMENT 2023年第1期278卷

作者： Chen, Xiang Ding, Kun Zhang, Jingwei Yang, Zenan Liu, Yongjie Yang, Hang Hohai Univ Coll Mech & Elect Engn Changzhou 213022 Jiangsu Peoples R China Changzhou Key Lab Photovolta Syst Integrat & Prod Changzhou 213022 Jiangsu Peoples R China

Parameter identification of the photovoltaic (PV) model is essential to research in the PV field. A two -stage method of model parameter identification based on maximum power matching (MPM) and improved flow direction algorithm (IFDA) is proposed. The two-stage method, i.e., MPM-based rough extraction and IFDA-based precise identification. The quality of I-V data dramatically impacts the PV model's parameter identification accuracy. At first, the measured I-V curves are preprocessed. The process includes outlier removal, curve fitting, and sparsification of I-V data. Then the MPM is used for the rough extraction of model parameters from the preprocessed I-V data. Finally, the rough extraction results are used as the initial values of the iterations in the precise identification using IFDA. The root mean square error (RMSE) between the measured and calculated currents is used as the fitness function of IFDA. The experimental section compares sixteen methods. IFDA presents the highest accuracy with the smallest RMSE at 0.0024 A. Five methods with outstanding performance are selected for 1000 repetitive experiments. The IFDA is the most stable with RMSE in the range of 0.002 A to 0.003 A.

关键词： Photovoltaic model Parameter identification Preprocessing Maximum power matching flow direction algorithm

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FDA_CPR: An efficient improved flow direction algorithm with cellular topological structure, potential energy concept and rockfall strategy

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AIN SHAMS ENGINEERING JOURNAL 2024年第10期15卷

作者： Chen, Haoran Wang, Yukun Li, Zhongfeng Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Yingkou Inst Technol Sch Elect Engn Yingkou Liaoning Peoples R China

Aiming at the problems of flow direction algorithm (FDA), such as premature convergence and tendency to fall into local optimum, this paper proposes an efficient improved version named FDA_CPR. Firstly, the embedding of the topological structure of cellular automata to increase the population diversity of the algorithm. Then, L & eacute;vy flight is used to replace the D8 algorithm of FDA and a position update strategy based on Sine Cosine algorithm to slow down the convergence of the algorithm. Furthermore, a new concept of "potential energy" is introduced to calculate the energy score of the flow and determine the new direction of movement to improve the search efficiency of the algorithm. Finally, a "rockfall strategy" based on dynamic opposite learning is designed to help the algorithm jump out of the local optimum. FDA_CPR is compared with eight well-known algorithms on CEC2017 and several feature selection problems to evaluate its performance. Qualitative analysis, Friedman's test and Wilcoxon signed rank test confirmed the effectiveness of the improvement in different ways. Various experimental results show that FDA_CPR has considerable advantages in solving both discrete and continuous problems, and its comprehensive performance is better than that of FDA and other comparison algorithms

关键词： flow direction algorithm Exploration and exploitation Cellular topological structure Potential energy Rockfall strategy

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An Improved flow direction algorithm That Considers Mass Conservation for Sediment Transport Simulations

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WATER 2023年第23期15卷 4111页

作者： Wei, Hong Dai, Wen Wang, Bo Zhu, Hui Zhao, Fei Jiao, Haoyang Li, Penghui Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomatics Engn Nanjing 210044 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Resou Nanjing 210023 Peoples R China Nanjing Univ Informat Sci & Technol Sch Geog Sci Nanjing 210044 Peoples R China Beijing Normal Univ Fac Geog Sci Beijing 100875 Peoples R China

The sediment transport process in watersheds is an important research component of geomorphology and surface dynamics. Previous work has inferred the spatial distribution of the sediment transport rate (STR) by the flow direction algorithm and measured topographic variation;however, the simple application of the flow direction algorithm contributes to mass non-conservation during a simulation. This study designs an improved flow direction algorithm for a sediment transport process simulation by judging the mass conservation situation in the simulation process. The specific implementation is to evaluate the existence of negative values for the STR;if they exist, the negative values of the STR are reset to stop the propagation of the negative values downstream. Experiments are conducted to improve the classical D8, MFD-se, and MFD-md flow algorithms in this paper, and the experimental results show that the method in this paper can effectively improve the simulation effect of STR. The STR simulations of the three models, D8, MFD-se, and MFD-md, improved by 1.26%, 4.17%, and 4.54%, respectively. Moreover, the MFD-se model is more suitable for the simulation of the STR when comparing the three models. The improved flow algorithm can be used to simulate the STR, sediment content, and pollutant migration in watersheds, providing a new method for the fine-grained characterization of surface processes in watersheds.

关键词： flow direction algorithm sediment transport rate multiple flow direction DEM

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