Xiaobing Yu

Nanjing University of Information Science and Technology

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Wenguan Luo

Nanjing University of Information Science and Technology

Xu‐Ming Wang

Nanjing University of Information Science and Technology

Xianrui Yu

Beihang University

Yiqun Lu

Nanjing University of Information Science and Technology

Mei Cai

Nanjing University of Information Science and Technology

WangYing Xu

Nanjing University of Information Science and Technology

Xuejing Wu

Nanjing University of Information Science and Technology

Zhenpeng Hu

Nankai University

Jun Guo

Chongqing Public Health Medical Center

Chenliang Li

Wuhan University

Cooperative Institutions

Nanjing University of Information Science and Technology

Chinese Academy of Sciences

CHN Energy (China)

Wuhan University of Technology

Huazhong University of Science and Technology

University of Chinese Academy of Sciences

Nankai University

Shanghai Jiao Tong University

Chongqing University

Thermal Power Research Institute

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A novel hybrid multi-verse optimizer with queuing search algorithm

Journal of Intelligent & Fuzzy Systems (2023)

Yuan Wang Xiaobing Yu Xu‐Ming Wang

Multi-verse optimizer (MVO) is a novel nature-inspired algorithm that has been applied to solve many practical optimization problems. Nevertheless, the original MVO has problems of low convergence speed and accuracy of final solutions. Besides, the failure to strike a balance between exploration and exploitation and the easiness of falling into local optimum in the early stages makes MVO hard to converge. In this paper, we propose a novel hybrid algorithm called Hybrid Queuing Search algorithm with MVO (HQS-MVO) by introducing Queuing Search Algorithm (QSA) and Metropolis rule to overcome these shortcomings. The introduction of QSA is to improve the accuracy of final solutions. At the same time, the Metropolis rule is employed to prevent the algorithm from falling into the local optimum, thus improving the convergence speed of the original MVO. Then, we compare the performance of HQS-MVO on 30 benchmark functions of CEC2014 and 10 benchmark functions of CEC2019 with the other four related algorithms and three latest algorithms. The results show that HQS-MVO has the most accurate solutions in most cases compared with other seven algorithms in most cases, and gains the lowest standard deviations. Moreover, we make convergence curve of the eight algorithms. Compared with other algorithms, HQS-MVO shows outstanding performances and converge faster in general. Finally, we apply the proposed algorithm in a real engineering optimization problem and compare its performance with other algorithms, the results show that HQS-MVO is still the best one in problem of designing of gear train.

Benchmark (surveying)

10.3233/jifs-223369

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Citations (2)

Numerical Calculation of Wet Exhaust Gas Condensation in the Heat Exchanger

Journal of Physics Conference Series (2023)

Jingzhong Guo Yuheng Gu Tianshuai Zheng Shujie Cui A Ran Ye

Abstract Based on the background of waste heat recovery of gas-fired boiler, this paper studied wet exhaust gas condensation heat transfer using numerical methods. The calculated results indicated that with the exhaust gas Re number increases, heat flux, and Nu number gradually increase, while the Eu number decreases simultaneously. Besides, the variation trend of the Nu and Eu number gradually reduced with Re number increases. With inlet exhaust gas temperature increase, the Nu number increases, and the Eu number decreases. Meanwhile, Nu number and Eu number increases with the increase in wall temperature. On this basis, the empirical correlations for calculating Nu and Eu numbers are obtained, and the deviation between the calculated values and the numerical results is within ±8%. The conclusion of this study provides basis for heat exchanger design in wet exhaust gas waste heat recovery.

10.1088/1742-6596/2503/1/012090

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Citations (2)

Reinforcement learning-based multi-objective differential evolution algorithm for feature selection

Information Sciences (2024)

Xiaobing Yu Zhenpeng Hu Wenguan Luo Yu Xue

Differential Evolution

10.1016/j.ins.2024.120185

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Citations (11)

Exponential Rank Differential Evolution Algorithm for Disaster Emergency Vehicle Path Planning

IEEE Access (2021)

Xueying Zhang Xiaobing Yu Xuejing Wu

When disasters happen, victims suffer from lots of miseries. Vehicles are used to carry relief supplies for these victims. However, these vehicles are subject to threats as roads and bridges are often destroyed by these disasters. To dispatch vehicles safely and minimize the misery of victims, a disaster emergency vehicle path planning model is proposed by introducing the deprivation cost function and two constraints. One constraint is the risk of the route, and the other is the angle of the vehicle. A differential evolution algorithm based on the exponential selection is developed to optimize the model. The proposed algorithm uses the exponential ranking method, making individuals have exponential probability as parents enter into the mutation stage. The approach improves the exploitation capacity while maintain the exploration ability of the algorithm. Three disaster scenarios in three-dimension are used to do simulations. The experimental results have verified that our proposed model can deal well with the vehicle path planning problem in disaster emergency management. The proposed algorithm has acquired superior performances compared with differential evolution variant algorithms.

Differential Evolution

Emergency vehicle

10.1109/access.2021.3050764

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Citations (10)

A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model

Sustainability (2023)

Xuming Wang Jiaqi Zhou Xiaobing Yu Xianrui Yu

Due to the inappropriate or untimely distribution of post-disaster goods, many regions did not receive timely and efficient relief for infected people in the coronavirus disease outbreak that began in 2019. This study develops a model for the emergency relief routing problem (ERRP) to distribute post-disaster relief more reasonably. Unlike general route optimizations, patients’ suffering is taken into account in the model, allowing patients in more urgent situations to receive relief operations first. A new metaheuristic algorithm, the hybrid brain storm optimization (HBSO) algorithm, is proposed to deal with the model. The hybrid algorithm adds the ideas of the simulated annealing (SA) algorithm and large neighborhood search (LNS) algorithm into the BSO algorithm, improving its ability to escape from the local optimum trap and speeding up the convergence. In simulation experiments, the BSO algorithm, BSO+LNS algorithm (combining the BSO with the LNS), and HBSO algorithm (combining the BSO with the LNS and SA) are compared. The results of simulation experiments show the following: (1) The HBSO algorithm outperforms its rivals, obtaining a smaller total cost and providing a more stable ability to discover the best solution for the ERRP; (2) the ERRP model can greatly reduce the level of patient suffering and can prioritize patients in more urgent situations.

Optimization algorithm

10.3390/su15108187

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Citations (5)

Address wind farm layout problems by an adaptive moth-flame optimization algorithm

Applied Soft Computing (2024)

Xiaobing Yu Wen Zhang

Optimization algorithm

10.1016/j.asoc.2024.112462

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Citations (0)

An Adaptive Hybrid Algorithm Based on Particle Swarm Optimization and Differential Evolution for Global Optimization

The Scientific World JOURNAL (2014)

Xiaobing Yu Jie Cao Haiyan Shan Zhu Li Jun Guo

Particle swarm optimization (PSO) and differential evolution (DE) are both efficient and powerful population-based stochastic search techniques for solving optimization problems, which have been widely applied in many scientific and engineering fields. Unfortunately, both of them can easily fly into local optima and lack the ability of jumping out of local optima. A novel adaptive hybrid algorithm based on PSO and DE (HPSO-DE) is formulated by developing a balanced parameter between PSO and DE. Adaptive mutation is carried out on current population when the population clusters around local optima. The HPSO-DE enjoys the advantages of PSO and DE and maintains diversity of the population. Compared with PSO, DE, and their variants, the performance of HPSO-DE is competitive. The balanced parameter sensitivity is discussed in detail.

Local optimum

Differential Evolution

Adaptive mutation

Imperialist competitive algorithm

Global Optimization

10.1155/2014/215472

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Citations (43)

A ranking improved teaching-learning-based optimization algorithm for parameters identification of photovoltaic models

Applied Soft Computing (2024)

Haoyu Wang Xiaobing Yu

Identification

Optimization algorithm

Learning to Rank

10.1016/j.asoc.2024.112371

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Citations (1)

Evaluation of Many-Objective Evolutionary Algorithms by Hesitant Fuzzy Linguistic Term Set and Majority Operator

International Journal of Fuzzy Systems (2018)

Xiaobing Yu Yiqun Lu

Operator (biology)

10.1007/s40815-018-0488-1

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Citations (4)

Optimization of pump scheduling in waterworks considering load balancing using improved genetic algorithm

Journal of Intelligent & Fuzzy Systems (2023)

Baigang Du Dahu Zha Jun Guo Xiaobing Yu

The water transmission and distribution process of the water supply pump station of the water purification plant plays a key role in the entire urban water supply system. When the requirements of water supply quantity and water pressure are satisfied, the reduction of operating energy consumption of the pump set and improvement of its service life are urgent problems. Therefore, to reduce the cost of water supply pump station, a mathematical model is established to minimize the energy consumption of pump group, the number of pump switches and the load balancing in this paper. In order to solve the pump scheduling problem, a two-stage strategy based on genetic algorithm is proposed. In stage one, the frequency conversion ratio and the number of pumps needed to be turned on at the lowest energy consumption are calculated. In stage two, through the improved genetic algorithm and iterative way to reduce the number of pump switches and load balancing. Finally, a case study from a real waterworks in Suzhou, China is used to verify the validity of the proposed model. Numerical results reveal that the improved genetic algorithm outperforms the competing algorithms. In addition, a proper sensitivity analysis allows assessing the effects under different pump operating conditions.

Operating cost

10.3233/jifs-224245

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