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Hybrid-adaptive differential evolution with decay function applied to transmission network expansion planning with renewable energy resources generation

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第14期16卷 2829-2839页

作者： Zhang, Xuexia Wang, Xiaomei Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China

As the country implements the reform of the electricity market and a large number of renewable energy resources (RES) are connected to the grid, the model and algorithm of traditional transmission network expansion planning (TNEP) are not suitable. A model of TNEP is built, in which the investment cost of new lines and the market-based annual congestion surplus are selected as objective functions. A probabilistic DC power flow method is used to describe uncertainties of RES generation. A new algorithm, Hybrid-Adaptive Differential Evolution with Decay Function (HyDE-DF), is applied to solve the model of TNEP for the first time. Analysis and comparison in the IEEE 18-bus system and the 52-bus system in an area of Sichuan Province prove the feasibility, effectivity, and practicability of the model and solving algorithm. Some comparisons are performed using other variants of Differential Evolution (DE) and a swarm intelligence optimization algorithm to demonstrate the performance superiority of the new algorithm and new application. It is worth mentioning that in the 52-bus system of an area of Sichuan Province, the investment cost obtained by the new algorithm is at least one or two orders of magnitude lower than other algorithms.

关键词： electricity market TNEP market-based annual congestion surplus IEEE 18-bus system particle swarm optimisation power grids Hybrid-adaptive differential evolution DC power transmission renewable energy resources generation RES generation traditional transmission network expansion planning power markets power transmission planning solving algorithm grid swarm intelligence optimization algorithm probabilistic DC power flow method 52-bus system evolutionary computation investment cost investment swarm intelligence costing power engineering computing Sichuan province

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A microbial kinetic optimization approach

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第3期40卷 5697-5713页

作者： Ding, Xiong Lu, Yan Hunan Int Econ Univ Sch Informat & Mech Engn Changsha Peoples R China

In order to solve some optimization problems with many local optimal solutions, a microbial dynamic optimization (MDO) algorithm is proposed by the kinetic theory of hybrid food chain microorganism cultivation with time delay. In this algorithm, it is assumed that multiple microbial populations are cultivated in a culture system. The growth of microbial populations is not only affected by the flow of culture fluid injected into the culture system, the concentration of nutrients and harmful substances, but also by the interaction between the populations. The influence of culture medium which is injected regularly will suddenly increase the concentration of nutrients and toxic substances, it will suddenly increase the impact on the population. These characteristics are used to construct absorption operators, grabbing operators, hybrid operators, and toxin operators;the global optimal solution of the optimization problem can be quickly solved by these operators and the population growth changes. The simulation experiment results show that the MDO algorithm has certain advantages for solving optimization problems with higher dimensions.

关键词： swarm intelligence optimization algorithm microbial culture kinetics microbial population microbial dynamics optimization (MDO)

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Research and application based on the swarm intelligence algorithm and artificial intelligence for wind farm decision system

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RENEWABLE ENERGY 2019年 134卷 681-697页

作者： Zhao, Xuejing Wang, Chen Su, Jinxia Wang, Jianzhou Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China

Wind energy is an increasing concern for wind farm administrators. Effective wind energy potential analysis and accurate forecasting can reduce the operating cost of wind farms. However, many previous studies have been restricted to analyses of wind energy potential analysis and wind speed forecasting, which may result in poor decisions and inaccurate power scheduling for wind farms. This study develops a wind energy decision system based on swarm intelligence optimization and data preprocessing, which includes two modules: wind energy potential analysis and wind speed forecasting. In the wind energy potential analysis module, the parameters of the Weibull distribution are optimized by a multiple swarm intelligence optimization algorithm, which can provide better wind energy assessment results. In the wind speed forecasting module, the data preprocessing method can effectively eliminate the noise of the original wind speed time series, maintain the characteristics of the wind speed data, and improve the accuracy of the forecasting model. The numerical results show that the wind energy decision system not only provides an effective wind energy assessment, but can also satisfactorily approximate the actual wind speed forecasting. Therefore, it can serve as an effective tool for wind farm management and decision-making. (C) 2018 Published by Elsevier Ltd.

关键词： Wind energy potential analysis Weibull distribution Data preprocessing swarm intelligence optimization algorithm

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Analysis of photovoltaic array maximum power point tracking under uniform environment and partial shading condition: A review

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ENERGY REPORTS 2022年 8卷 13235-13252页

作者： Li, Jianlin Wu, Yiwen Ma, Suliang Chen, Mingxuan Zhang, Baoping Jiang, Bing North China Univ Technol Energy Storage Technol Engn Res Ctr Beijing 100144 Peoples R China China Three Gorges Technol Co Ltd Beijing 100032 Peoples R China

With the expansion of the scale of application of photovoltaic (PV) power generation, PV maximum power point tracking (MPPT) technology has been transformed from uniform environmental conditions (UEC) to partial shading conditions (PSC), but there are certain limitations in the application of existing technologies under PSC. At present, a large number of several different PV MPPT algorithms have been proposed. In the research on MPPT technology under UEC, the MPPT technology based on the output characteristics of PV cells is used and the MPPT tracking speed has reached 20 times, in another improvement, the MPPT technology based on PV cell model is used, and the MPPT tracking accuracy has reached 99.4428%. In the process of MPPT technology transformation, there is a lack of sufficient comparative analysis and systematic review of these methods. This paper focuses on the tracking problem of the Voltage-Power (U-P) single-peak curve under the UEC to the U-P multi-peak curve under the partial shading conditions (PSC), and discuss four key issues in the development of PV MPPT research: (1) Under UEC, the performance of MPPT algorithms based on PV output characteristics and PV cell model;(2) The failure phenomenon when traditional MPPT technology is applied under PSC;(3) Under the PSC, the application process of the traditional methods and the swarm intelligence optimization algorithm (SIOA);(4) The bottlenecks faced by SIOA applications under PSC and the analysis of solutions. Through the analysis and comparison presented in this study, researchers can comprehensively understand the development and the important changes brought by PSC to MPPT technology. This could lead to exploration and research into the applications of SIOA in MPPT technology under PSC. The relevant conclusions will provide important directions for future research in the field of MPPT under PSC. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Maximum power-point tracking Uniform environment condition Partially shaded conditions swarm intelligence optimization algorithm Photovoltaic mathematical model

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Guided learning strategy: A novel update mechanism for metaheuristic algorithms design and improvement

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KNOWLEDGE-BASED SYSTEMS 2024年 286卷

作者： Jia, Heming Lu, Chenghao Sanming Univ Sch Informat Engn Sanming 365004 Peoples R China

Meta-heuristic algorithms (MH) are naturally inspired global optimization algorithms. They are often relatively simple and can solve problems in a short period of time, with certain benefits. However, as the problem becomes more complex, the solution that the algorithm can obtain is often not the optimal solution to the problem, which limits its application scenarios. Therefore, improving the performance and solving accuracy of existing algorithms is crucial for expanding their application ability. In traditional optimization algorithms, there are often two concepts, namely exploration and exploitation. Exploration refers to a wide range of discrete search, used to avoid falling into local optima, and exploitation refers to a small range of focused exploration, used to improve algorithm accuracy. How to balance exploration and exploitation is the key to enhancing algorithm performance and problem adaptability. This paper proposes a brand new strategy named Guided Learning Strategy (GLS) to solve above problem. GLS obtains the dispersion degree of the population by calculating the standard deviation of the historical locations of individuals in recent generations, and infers what guidance the algorithm currently needs. When the algorithm is biased towards exploration, it will guide the algorithm to exploit. Otherwise, it will guide the algorithm to explore. It is precisely because this strategy can identify the current needs of the algorithm and provide assistance that it can improve the performance of most algorithms. This article improves three types of algorithms based on evolution (LSHADE_SPACMA), Stochastic Fractal Search (SFS), and Marine Predators algorithm (MPA) with better performance, and tests their performance on 57 constrained engineering problems and CEC2020. The effectiveness of this strategy has been confirmed and proved for optimization problem. The source codes of the proposed GLS (GLS_MPA) can be accessed by https://***/luchenghao2022/Guide

关键词： New strategies Intelligent balance exploration and exploitation swarm intelligence optimization algorithm

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A knee point-driven multi-objective artificial flora optimization algorithm

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WIRELESS NETWORKS 2021年第5期27卷 3573-3583页

作者： Wu, Xuehan Wang, Shafei Pan, Ye Shao, Huaizong Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Peoples R China

In recent days, swarm intelligent (SI) optimization algorithms have been proved to be a powerful framework for finding tradeoff solutions of multi-objective optimization problems (MOPs). Many researchers have proposed various SI optimization algorithms. Multi-objective artificial flora (MOAF) optimization algorithm is a recently proposed algorithm for solving MOPs. However, problems of decreased population diversity and uniformity of solutions distribution in the late evolutionary period is existed in the algorithm. Hence, this paper proposes a knee point-driven MOAF (kpMOAF) optimization algorithm to address the vulnerability of MOAF optimization algorithm. Knee points of the non-dominant solutions are taken by the proposed algorithm as criterion to guide the population evolution. Researchers have proved that select knee points equals to select a large hypervolume. Therefore, using it as criterion is an effective way to enhance the population convergence rate and maintain the diversity of solutions. In addition, adaptive neighborhood control method is introduced in the evolution process to improve the algorithm development capability. Simulation results on 10 benchmark functions demonstrate the competitiveness of kpMOAF optimization algorithm.

关键词： Multi-objective artificial flora optimization algorithm swarm intelligence optimization algorithm Knee points Elite strategy

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Development of a hybrid artificial intelligence model to predict the uniaxial compressive strength of a new aseismic layer made of rubber-sand concrete

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MECHANICS OF ADVANCED MATERIALS AND STRUCTURES 2023年第11期30卷 2185-2202页

作者： Mei, Xiancheng Li, Chuanqi Sheng, Qian Cui, Zhen Zhou, Jian Dias, Daniel Chinese Acad Sci Inst Rock & Soil Mech Wuhan 430071 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Grenoble Alpes Univ Lab 3SR CNRS UMR 5521 Grenoble France Cent South Univ Sch Resources & Safety Engn Changsha Peoples R China Hefei Univ Technol Sch Automot & Transportat Engn Hefei Peoples R China

This study, proposes the use of a novel rubber-sand concrete (RSC) material, which comprises rubber particles, sand, and cement, as an aseismic material in practical engineering construction. The uniaxial compressive strength (UCS) of damping materials is an important factor that directly affects the seismic activity in underground structures. To predict the UCS of RSC, artificial intelligence model back propagation neural network (BPNN), which is optimized through four swarm intelligence optimization (SIO) algorithms: particle swarm optimization algorithm (PSO), fruit fly optimization algorithm (FOA), lion swarm optimization algorithm (LSO), and sparrow search algorithm (SSA), is used. The dataset for the prediction models was obtained from uniaxial compression tests in the RSC laboratory. The performances of the four hybrid intelligence models were evaluated using six performance indicators: the root mean square error (RMSE), correlation coefficient (R), determination coefficient (R-2), mean absolute error (MAE), mean square error (MSE), and sum of square error (SSE).The prediction capability of these models was graded based on these indicators using a ranking system. The results show that the prediction ability of the LSO-BPNN hybrid model is better than that of the three other hybrid models, with RMSE of (1.0635, 1.2352), R of (0.9887, 0.9713), R-2 of (0.9776, 0.9165), MAE of (0.7257, 0.8243), MSE of (1.1352, 1.5256), SSE of (64.7074, 36.6151), and ranking score of (24, 24) in the training and testing phases, respectively. Therefore, the LSO-BPNN hybrid model is an efficient and accurate method for predicting the UCS of RSCs. Sensitivity analysis showed that rubber and sand were the most important elements that affected UCS prediction, followed by cement, with the lowest relative importance being RPZ. This study provides guidance for the extension and application of RSC materials to underground seismic engineering.

关键词： Rubber-sand concrete uniaxial compressive strength back propagation neural network swarm intelligence optimization algorithm

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A hybrid Aquila optimizer and its K-means clustering optimization

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2023年第3期45卷 557-572页

作者： Huang, Cheng Huang, Jinglin Jia, Youquan Xu, Jiazhong Harbin Univ Sci & Technol Dept Automat Harbin 150001 Peoples R China

Aiming at the defects of the Aquila optimizer (AO) in dealing with some complex optimization problems, such as slow convergence speed, low convergence accuracy, and easy to fall into local optimum, in this paper, a hybrid Aquila optimizer (HAO) algorithm based on Gauss map and crisscross operator is proposed. First, Gauss map is introduced to initialize the Aquila population to improve the quality of the initial population. Then use the crisscross operator to promote the exchange of information within the population and maintain the diversity of the population in each iteration, which not only enhances the ability of the algorithm to jump out of the local optimum but also accelerates the global convergence of the algorithm. The results of experiments using 21 classical benchmark functions indicate that HAO has better global search ability, faster convergence speed, and better stability than AO. The overall optimization performance of HAO in different dimensions is better than particle swarm optimization (PSO) algorithm, gray wolf optimization (GWO) algorithm, whale optimization algorithm (WOA), and crisscross optimization (CSO) algorithm. Finally, the results of K-means clustering optimization on six University of California (UCI) standard data sets demonstrate that HAO has significant advantages over three algorithms that are good at clustering optimization.

关键词： optimization swarm intelligence optimization algorithm chaotic map crossover operator

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Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping

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JOURNAL OF HYDROLOGY 2020年第0期581卷 124379-000页

作者： Quang-Thanh Bui Quoc-Huy Nguyen Xuan Linh Nguyen Vu Dong Pham Huu Duy Nguyen Van-Manh Pham VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & GIS CARGIS 334 Nguyen Trai Hanoi Vietnam VNU Univ Sci Fac Geog 334 Nguyen Trai Hanoi Vietnam

This study proposed and compared several novel hybrid models that combined swarm intelligence algorithms and Deep Learning Neural Network for flood susceptibility mapping. Lai Chau, a province in the northwest mountainous region of Vietnam was chosen as a case study since it had recently undergone severe flashflood in 2018. For this purpose, numerical predictor variables such as topographically derived factors (Digital Elevation Model, Aspect, Slope, Curvature, Topographic Wetness Index), climatic variables (Rain), and hydrological variables (stream density, stream power index, distance to river) and multiple remote sensing indices (Normalized Difference Vegetation Index, Normalized Difference Buildup Index) were used. These predictor variables were selected because they are globally collectible and reproducible. The performances of these models were evaluated by using common statistical indicators, namely Root Mean Square Error, Mean Absolute Error, Overall Accuracy and Area under Receiving Operating Characteristics, and the statistical test of differences. The results showed that the proposed swarm intelligence models outperformed benchmarked methods, namely Particle swarm optimization, Support Vector Machine, Random Forest in almost all comparing indicators. It is suggested that proposed models are more robust than the classifiers, which were used for benchmarking and they are good alternatives for flood susceptibility mapping given the availability of dataset.

关键词： swarm intelligence optimization algorithm Deep Learning Neural Network Flash flood

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Fireworks algorithm based on search space partition

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2022年第12期37卷 10783-10812页

作者： Li, Yifeng Tan, Ying Peking Univ Sch Artificial Intelligence Inst Artificial Intellignce Key Lab Machine Perceptron MOE Beijing 100871 Peoples R China

Fireworks algorithm is a novel swarm intelligence optimization framework which focuses on the potential of collaboration among multiple subpopulations with independent search ability. Although it has been proved to perform excellently in many tasks, the collaborative mechanism of fireworks is still quite undeveloped. In this paper, a theoretical model of fireworks algorithm based on search space partition is proposed, analyzed and implemented. The local search of each firework is replaced by covariance matrix adaptation evolution strategy for efficient exploitation. A coordination strategy inspired from Voronoi diagram is proposed to approximate the theoretical model for stable global exploration. Experimental results show that the proposed algorithm not only outperforms previous variants of fireworks algorithm significantly, but also achieves competitive results compared with state-of-the-art evolutionary algorithms, which are intensively fine-tuned on the objective functions.

关键词： fireworks algorithm global optimization meta-heuristic search space partition swarm intelligence optimization algorithm

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