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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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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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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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VLEO Satellite Constellation Design for Regional Coverage of Aviation and Marine Users

VLEO Satellite Constellation Design for Regional Coverage of...

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IEEE Conference on Global Communications (IEEE GLOBECOM) - Intelligent Communications for Shared Prosperity

作者： Chen, Guoquan Wu, Shaohua Deng, Yajing Jiao, Jian Zhang, Qinyu Harbin Inst Technol Shenzhen Dept Elect & Infor mation Engn Shenzhen 518055 Peoples R China Harbin Inst Technol Shenzhen Guangdong Prov Key Lab Aerosp Commun & Networking Shenzhen 518055 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

ISBN: (纸本)9798350310900

Recently, the Space-Air-Ground-Sea Integrated Network (SAGSIN) attracts great attention due to its ability to provide high-speed communication services to aviation users (AUs) and marine users (MUs), with Low Earth Orbit (LEO) satellites play an essential role. However, the available space in LEO is nearly saturated and full of massive space junks, which, combined with the ultra-low latency requirements for future 6G, presents a significant challenge. To address this issue, we propose designing a Very Low Earth Orbit (VLEO)-based satellite network that efficiently serves AUs and MUs. We first create and analyze the heat maps based on actual collected data of Chinese aviation and marine communication traffic, and generate a benchmark observation point model with grid point method. Then we propose an implicit multi-objective continuous multi-variate optimization problem to achieve the maximum average coverage with minimum VLEO satellites. To solve this problem, we build a satellite constellation simulation system, using the idea of decomposition and polymerization combined with the elite strategic genetic algorithm (ESGA) of swarm intelligence optimization algorithm. Many simulation results are obtained, including the indication that the optimal VLEO constellation has the deployment features of large altitude and low inclination, and has better coverage performance for longitudinal distributed business. The design process in this work is highly migratory.

关键词： SAGSIN VLEO satellite constellation swarm intelligence optimization algorithm ESGA

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The analysis and re-optimization of food systems by using intelligent optimization algorithms and machine learning

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ALL LIFE 2022年第1期15卷 656-677页

作者： Wang, Xu Jilin Univ Changchun 130000 Peoples R China

Facing the serious concern of global food security, this study focuses on the feasibility of improvements to food systems and predicts future changes. This study proposes a coordination evaluation index incorporating diverse indicators of different food systems, analyzes the changes after modification, and predicts the equilibrium time and the critical point. In this regard, we pre-processed the data using the entropy method, variance contribution rate, and normalization. Instead of utilizing single-element linear forecasting and economic income and expenditure models, we innovatively developed a multivariate evaluation system for population, cultivated land, and food systems in three major directions. Meanwhile, after conducting a cross-sectional comparison of the prediction effects of various algorithms, we finally selected Gaussian process regression and a neural network to build a prediction model to develop food systems of different sizes. After establishing the evaluation index and development prediction model, we fitted the three-dimensional surface of the developmental change using thin-plate interpolation. We adopted the swarm intelligence optimization algorithm to search for the balance and critical points after the change. We also compared various swarm intelligence optimization algorithms, such as the particle swarm optimization algorithm, salp swarm algorithm, and whale optimization algorithm.

关键词： Re-optimizing food systems Multiple Regression Analysis coupling degree evaluation model swarm intelligence optimization algorithm Neural Network

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A Deep Reinforcement Learning-Based algorithm for Multi-Objective Agricultural Site Selection and Logistics optimization Problem

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APPLIED SCIENCES-BASEL 2024年第18期14卷 8479页

作者： Liu, Huan Zhang, Jizhe Zhou, Zhao Dai, Yongqiang Qin, Lijing Gansu Agr Univ Coll Informat Sci & Technol Lanzhou 730070 Peoples R China

The challenge of optimizing the distribution path for location logistics in the cold chain warehousing of fresh agricultural products presents a significant research avenue in managing the logistics of agricultural products. The goal of this issue is to identify the optimal location and distribution path for warehouse centers to optimize various objectives. When deciding on the optimal location for a warehousing center, various elements like market needs, supply chain infrastructure, transport expenses, and delivery period are typically taken into account. Regarding the routes for delivery, efficient routes aim to address issues like shortening the overall driving distance, shortened travel time, and preventing traffic jams. Targeting the complex issue of optimizing the distribution path for fresh agricultural products in cold chain warehousing locations, a blend of this optimization challenge was formulated, considering factors like the maximum travel distance for new energy trucks, the load capacity of the vehicle, and the timeframe. The Location-Route Problem with Time Windows (LRPTWs) Mathematical Model thoroughly fine-tunes three key goals. These include minimizing the overall cost of distribution, reducing carbon emissions, and mitigating the depletion of fresh agricultural goods. This study introduces a complex swarm intelligence optimization algorithm (MODRL-SIA), rooted in deep reinforcement learning, as a solution to this issue. Acting as the decision-maker, the agent processes environmental conditions and chooses the optimal course of action in the pool to alter the environment and achieve environmental benefits. The MODRL-SIA algorithm merges a trained agent with a swarm intelligence algorithm, substituting the initial algorithm for decision-making processes, thereby enhancing its optimization efficiency and precision. Create a test scenario that mirrors the real situation and perform tests using the comparative algorithm. The experimental findings indic

关键词： deep reinforcement learning Vehicle Routing Problem location problem swarm intelligence optimization algorithm fresh agricultural product supply chain

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Dynamic Gaussian mutation beetle swarm optimization method for large-scale weapon target assignment problems

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APPLIED SOFT COMPUTING 2024年 162卷

作者： Xu, Han Zhang, An Bi, Wenhao Xu, Shuangfei Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China

The weapon target assignment is a crucial issue for firepower resources optimization in modern warfare. Such a problem is complicated, multi-constrained, strongly nonlinear, NP-complete and the existing studies did not consider the suitability between different weapons and targets. In this paper, a novel weapon target assignment model is established that involves the weapon-target suitability and is closer to the real combat scenarios. Then, in view of that the conventional weapon target assignment methods are difficult to be applied in the large-scale problems efficiently, this work proposes a dynamic Gaussian mutation beetle swarm optimization algorithm with rule-based chaotic initialization. With the assistance of the dynamic parameter adjustment strategies and Gaussian mutation, the improved algorithm has fast convergence speed and high convergence accuracy, and it can solve the weapon target assignment problems with excellent optimization capabilities. Besides, the rulebased chaotic initialization strategy is embedded in this algorithm to generate high-quality population with better diversity. Finally, two comparative simulation cases of different initialization methods and algorithms for solving the large-scale weapon target assignment problems are designed. The results demonstrate that the proposed approach can provide more superior assignment schemes than its competitors with enhanced efficiency.

关键词： Weapon target assignment swarm intelligence optimization algorithm Beetle swarm optimization Nonlinear integer programming

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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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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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A microbial treatment population dynamics optimization approach

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021年第2期41卷 4035-4049页

作者： Gao, Jinding Hunan Int Econ Univ Acad Affairs Off Changsha 410205 Peoples R China

In order to solve some function optimization problems, Population Dynamics optimization algorithm under Microbial Control in Contaminated Environment (PDO-MCCE) is proposed by adopting a population dynamics model with microbial treatment in a polluted environment. In this algorithm, individuals are automatically divided into normal populations and mutant populations. The number of individuals in each category is automatically calculated and adjusted according to the population dynamics model, it solves the problem of artificially determining the number of individuals. There are 7 operators in the algorithm, they realize the information exchange between individuals the information exchange within and between populations, the information diffusion of strong individuals and the transmission of environmental information are realized to individuals, the number of individuals are increased or decreased to ensure that the algorithm has global convergence. The periodic increase of the number of individuals in the mutant population can greatly increase the probability of the search jumping out of the local optimal solution trap. In the iterative calculation, the algorithm only deals with 3/500 similar to 1/10 of the number of individual features at a time, the time complexity is reduced greatly. In order to assess the scalability, efficiency and robustness of the proposed algorithm, the experiments have been carried out on realistic, synthetic and random benchmarks with different dimensions. The test case shows that the PDO-MCCE algorithm has better performance and is suitable for solving some optimization problems with higher dimensions.

关键词： swarm intelligence optimization algorithm population dynamics environmental pollution microbial control

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