Linear Weighted Sum Method Research Articles

Spatial Influence of Digital Economy on Carbon Emission Efficiency of the Logistics Industry across 30 Provinces in China

The logistics industry (LI) is a key pillar of the global economy, and its carbon emission efficiency (CEE) is crucial for achieving carbon neutrality. The rapid development of the digital economy (DE) has had a profound impact on the LI, but the spatial impact on its CEE is currently unclear and requires further research. Firstly, based on the collection of relevant data, we use the entropy weight method and linear weighted sum method to measure the level of development of the DE. Secondly, the SBM model is used to measure the CEE level of the LI. Using Moran’s I index model and OLS and GWR models, we analyze the impact and spatial distribution characteristics of the DE on the CEE of the LI and propose development strategies. The article uses statistical data from 30 provinces in China from 2013 to 2022 as an example to demonstrate the implementation process of the method. The results show that the DE has a positive impact on the CEE of the LI, and there are spatial differences. Based on this, this article proposes policy recommendations for the development of green and low-carbon logistics and digital logistics that are tailored to local conditions, providing theoretical and methodological support for low-carbon research in the LI, and providing reference for other countries and regions to explore the path of green and low-carbon transformation.

An Index System for the Evaluation of the Effectiveness of Forest Ecological Product Value Realization in China

Forest Ecological Product Value Realization (FEPVR) is an important way for China to promote the transition and development of forestry and realize common prosperity. It is critical to assess the effectiveness of FEPVR rationally to obtain a comprehensive understanding of the progress of ecological civilization. Based on the Driver–Pressure–State–Impact–Response (DPSIR) model, in this study, we developed an evaluation index system for FEPVR effectiveness containing five subsystems and 37 indicators and assessed the effectiveness of FEPVR in China from 2011 to 2022 by using the linear weighted sum method, the coupling degree of coordination model, and Spearman’s correlation analysis. The results showed that the composite index increased from 0.1980 in 2011 to 0.6501 in 2022, with a general upward trend, but there is still great potential for improvement. The main contribution was from the Response subsystem. The year 2017 was an important turning point for FEPVR in China because its development started to speed up; the status of coupling coordination between different subsystems was gradually improving but was still at a low level. The relationship between all indicators was dominated by a non-significant correlation (52.4%), and the overall synergistic effect (27.8%) was greater than the trade-off effect (19.8%). This study provides a new perspective for evaluating the effectiveness of FEPVR and a decision-making reference for clarifying the direction of FEPVR optimization.

Study on the influence of the properties of interfacial transition zones on the performance of recycled aggregate concrete

Applying recycled aggregate (RA) aligns with the development concept of green and environmental protection. Nevertheless, introducing RA has resulted in a complex interface structure, which will deteriorate the performance of recycled aggregate concrete(RAC). To investigate the influence of the properties of the interfacial transition zones(ITZs) on the performance of RAC, and the digital image correlation(DIC) technology was used to track the strain variation law of each interface during the failure process. The porosity and mechanical properties of the ITZs were quantitatively characterized by backscattered electron(BSE) imaging and microhardness testing. By controlling a single variable, the different ITZs of RAC were improved by targeted strengthening, including mortar and aggregate strengthening.The influence of the ITZs properties on the macroscopic properties and mesoscopic failure behavior of RAC was analyzed. The experimental results show that the mechanical properties of the ITZs were enhanced and the porosity was reduced. Strengthening treatment improved the strength of target interface, causing a change in the relative strength of the internal interface of concrete, and reducing the probability of failure of the strengthened interface. This is consistent with the results observed in SEM images, the more compact and stable the structure at the interface, the lower the probability of failure under load. A comprehensive evaluation of mortar strengthening and aggregate strengthening was carried out with the help of the linear weighted sum method, and the results showed that the mortar strengthening was more effective.

Decision-making for stochastic multi-objective dispatch of integrated electrical and heating systems

In the realm of modern energy systems, addressing the challenges of enhancing flexibility and efficiency under uncertain conditions is of paramount importance. This paper explores the stochastic multi-objective optimal multi-energy flow problem within the context of integrated electrical and heating systems (IEHS). First, the electrical network, the heating network, and the energy hubs were modeled in a completely linearized form. The linear weighted sum method with variable weights was used to transform the multi-objective problem into a single-objective problem and generate a large number of Pareto-optimal solutions. Second, the input stochastic variables were divided into multi-interval scenarios by employing the Cartesian product. For each interval scenario, the interval satisfaction degree level was proposed to convert the constraints involving interval numbers into deterministic ones. Third, a multiple attributes decision analysis (MADA) approach was proposed based on evidential reasoning theory. Six evaluation attributes, namely, the power purchase cost and pollution gas emissions of IEHS, the sum of power loss and sum of voltage deviation in the electrical system, the sum of temperature drop in the heating system, and the interval probability value of the multi-interval scenarios, were considered to rank the Pareto-optimal solutions collected from the multi-interval scenarios and determine the final dispatch solution (called the Utopia solution). Numerical simulations demonstrated that the Utopia solution can comprehensively evaluate various attributes, making it the most suitable option for meeting the operational requirements of IEHS.

Line Planning under the Operation Mode of Line Sharing between Metro and Suburban Railway

Due to high transport efficiency, reduced transfer time, and various other advantages, the joint operation of different rail transit systems emerges as the optimal choice for rail transit systems. This article mainly studies the line-planning problem under the line-sharing operation mode between metro and suburban railway. First, a complex multiobjective programming model is established to maximize the net profit of two operating companies and the time savings of passengers. The constraints of this model encompass passenger flow, available vehicles, line carrying capacity, station capacity, cross-line configuration, departure frequency, and variable value range. Second, the linear weighted sum method is introduced to consolidate three objective functions into a single one, while utilizing the improved artificial bee colony (IABC) algorithm to address the line-planning problem. Besides, the traditional artificial bee colony (TABC) algorithm and the simulated annealing (SA) algorithm are provided as comparison groups to solve the same numerical example problem. The results demonstrate significant reductions in travel time by adopting the line-sharing operation mode. In addition, the IABC algorithm exhibits better solution quality and higher efficiency than both the TABC and SA algorithms. The proposed method proves to be valuable in formulating and optimizing the line plan.

Enhanced mortar strength and resistance to seawater degradation in the presence of calcined coal-series kaolin

The hydration process of blended mortars and its resistance to seawater degradation in the presence of calcined coal-series kaolin (CCK) was investigated in this study. The TG tests were used to quantify the effect of calcination temperature on the chemical-bound water content of the blended pastes while the XRD tests were applied to analyze the main crystalline hydration phases of the blended pastes, which were further quantified using the Rietveld-XRD refinement. In addition, microcalorimetric tests were used to characterize the degree of hydration. The hydration process of the blended mortars was further investigated in detail via combining SEM and pozzolanic activity tests. Based on this, the resistance to seawater degradation of the blended mortars with added CCK were verified via the expansion and strength loss tests, and the mechanism was analyzed via the electric flux and the total Cl− content tests. The optimal percentage of CCK (12%) was determined via a linear weighted sum method. The CCK obtained at 800 °C had the highest pozzolanic activity, promoting hydration reactions to form new gels (C-S-H, C-A-H and C-A-S-H) at active nucleation sites in mortars. These further enhanced both the flexural and compressive strengths (the compressive strength reached ∼53.7 MPa) of the blended mortars. However, CCK reduced the generation of Mg(OH)2, AFt and Friedel’s salt, decreasing seawater degradation to the mortars. Therefore, this study provides a new strategy to prepare high-strength mortar with high resistance to seawater degradation.

Impact of Internal CFRP strips on the flexural behavior of heat-damaged reinforced concrete beams

Recent theoretical developments revealed that reinforced concrete (RC) structures are susceptible to deterioration risk upon exposure to high temperatures where the mechanical properties of their constituents are affected and therefore require upgrading their overall performance. However, the overall behavior could be improved by strengthing the RC beams using the well-known carbon fiber-reinforcement polymers (CFRP) materials where its efficiency is highly limited by detachment, and debonding problems appear as a result of the weakness in the bond between the concrete surface and the strengthening material or upon the stress concentration induced by the various anchoring systems. The CFRP sheets have been integrated as internal reinforcement in the maximum bending zone within the thermally damaged beams, a new technology used in this study. The suggested method was the first of its kind and did not need an adhesive to be applied where debonding problem is eliminated. In contrast to conventionally reinforced steel, CFRP composite materials are fully compatible with flexural steel and constrained concrete. A total of 40 RC with (150 × 200) mm2 and an overall length of 1100 mm concrete beams were cast, and the studied parameters were the CFRP length, position, and exposure temperature. The internal strengthening technique has been found to ensure the full utilization of the strengthening material where the externally-strengthened beams fail preceding the CFRP strain reached, and this was confirmed using the linear weighted sum method where the internal strengthening has the highest ranking based on the mechanical characteristics comparisons. Moreover, the internal CFRP reinforcement improves RC beam performance, strength, stiffness, toughness, and serviceability more than exterior CFRP sheets. However, the enhancement percentages are twice as much for internal strengthening as the external one. It has also been found that the reinforcement's location substantially impacted the number and length of flexural cracks and its failure mode. In addition, for every 1% reduction in concrete compressive strength in heat damage, the average ultimate load was reduced by 0.8%. The CFRP profitability indexes decrease as sheet number and temperature increase; the average toughness decrease at 150 °C, 250 °C, and 500 °C is 12%, 21%, and 47%, respectively.

Revisiting the Impact of Environmental Regulation on Green Total Factor Productivity in China: Based on a Comprehensive Index of Environmental Regulation from a Spatiotemporal Heterogeneity Perspective.

Promoting greener and sustainable development is one of the main goals of the most recent 14th Five-Year Plan (i.e., 2021-2025). Environmental regulation is seen as fundamental to green transformation and an important way for all of China to reach a high-quality and sustainable development mode. However, large spatial disparities exist across the different regions in China, so formulating region-oriented environmental regulatory policies to achieve regional high-quality and sustainable development is now a matter of great practical significance. In the present paper, we analyze this problem and begin by calculating the high development level measured through the Green Total Factor Productivity (GTFP) of 259 Chinese cities. Thereafter we construct a comprehensive index of environmental regulation through the linear weighted-sum method. Lastly, we investigate the spatiotemporal heterogeneity of the impact of environmental regulation on GTFP using a Geographically and Temporally Weighted Regression (GTWR) model. We find that: (1) From the spatial dimension perspective, the impact of environmental regulation of Chinese cities on GTFP is either linear (monotonically increasing or decreasing), non-linear (U-shaped or inverted U-shaped), or nonsignificant. Most cities have a U-shaped relationship, indicating that environmental regulation first inhibits GTFP at the early stage, but then promotes it. There are also significant differences among cities in the turning points of environmental regulation; (2) From the time dimension perspective, the number of cities is on the rise having monotonically decreasing impacts of environmental regulation on GTFP. Furthermore, even for the same city, the relationship between the two variables shows different characteristics in different years; (3) The impact of five control variables on GTFP may also vary from one city to another over the sample period, also presenting spatiotemporal heterogeneity effects. Consequently, the formulation and implementation of environmental regulatory policies should not only adapt to local conditions but also choose reasonable and effective measures to achieve high-quality development targets.

Two-phase COVID-19 medical waste transport optimisation considering sustainability and infection probability

A safe and effective medical waste transport network is beneficial to control the COVID-19 pandemic and at least decelerate the spread of novel coronavirus. Seldom studies concentrated on a two-phase COVID-19 medical waste transport in the presence of multi-type vehicle selection, sustainability, and infection probability, which is the focus of this paper. This paper aims to identify the priority of sustainable objectives and observe the impacts of multi-phase and infection probability on the results. Thus, such a problem is formulated as a mixed-integer programming model to minimise total potential infection risks, minimise total environmental risks, and maximise total economic benefits. Then, a hybrid solution strategy is designed, incorporating a lexicographic optimisation approach and a linear weighted sum method. A real-world case study from Chongqing is used to illustrate this methodology. Results indicate that the solution strategy guides a good COVID-19 medical waste transport scheme within 1 min. The priority of sustainable objectives is society, economy, and environment in the first and second phases because the total Gap of case No.35 is 3.20%. A decentralised decision mode is preferred to design a COVID-19 medical waste transport network at the province level. Whatever the infection probability is, infection risk is the most critical concern in the COVID-19 medical waste clean-up activities. Environmental and economic sustainability performance also should be considered when infection probability is more than a certain threshold.

Multiperiod Equitable and Efficient Allocation Strategy of Emergency Resources Under Uncertainty

Equitable and efficient allocation of emergency resources is critical to ensure the success of relief efforts. The challenge comes largely from two aspects: the resources available for allocation are usually limited in quantity, especially in the early period of emergency response; and a large amount of uncertain information in the relief process affects the decision making of resource allocation. A multiperiod allocation model of emergency resources that takes into account both efficiency and equity based on uncertain disaster information is proposed. Interval number and triangular fuzzy number are introduced to describe the different sources of uncertainty (for example, demand, transportation time, and maximum transport amount), and the loss caused by unmet demand is used to quantify equity. Then, the deterministic transformation method of uncertain parameters is designed and the linear weighted sum method is applied to solve the proposed model. Finally, a computational case based on the 2017 Jiuzhaigou earthquake in Sichuan Province, China was conducted to validate the proposed model. The results show that the proposed model is feasible in the multiperiod allocation of emergency resources among multi-disaster sites, and the findings can help emergency managers to allocate emergency resources more scientifically, equitably, and effectively under uncertainty.

Semi-quantitative risk analysis of high concrete face rockfill dams under seismic action

A dam failures cause immeasurable losses, but more concrete face rockfill dams (CFRDs) are being built in areas where earthquakes occur frequently. If the risk level of CFRDs under seismic action can be identified promptly and corresponding measures taken in advance, dam failure risks can be mitigated to protect life and property. However, no study has been conducted on the seismic risk analysis of CFRDs. This study aimed to meet public security needs; hence, the risk matrix approach was used for analyzing the semi-quantitative risk of CFRDs under seismic action. Considering the seismic failure modes of dam deformation and dam slope instability, we also proposed a calculation technique of seismic failure possibility for high CFRDs. According to the worst-case scenario, the linear weighted sum method was used to assess the comprehensive seismic risk loss of CFRDs in terms of life loss,and economic loss, and socio-environmental impact. The risk levels of CFRDs under seismic action were accurately evaluated, and corresponding risk management measures were proposed according to the “As Low As Reasonably Practicable” principle. The results of this work fill the gap in the risk analysis of CFRDs under seismic action. Additionally, the results provide a theoretical basis and technical support for future seismic designs, seismic risk assessments, and risk decision-making for high CFRDs and similar projects in strong seismic areas.

An Integrated Renewable Energy System for the Supply of Electricity and Hydrogen Energy for Road Transportation Which Minimizes Greenhouse Gas Emissions

Greenhouse gas emissions produced by the energy sector, including the transportation sector, are a problem that must be resolved. One way to solve this problem is to provide energy in the transportation sector in a sustainable way, by using renewable energy. An integrated renewable energy system has been implemented through an optimization model for the supply of electricity and hydrogen energy for road transportation. The proposed model is in the form of mixed-integer linear programming with two objective functions: planning costs and greenhouse gas emissions. The multi-objective model was solved using the linear weighted-sum method. In this article, three scenarios are developed, namely the business-as-usual scenario, the renewable energy scenario, and the renewable energy with energy storage system scenario. The business-as-usual scenario is used to analyze the supply of electricity and hydrogen by prioritizing the objective function of planning costs. The renewable energy scenario prioritizes the objective function of greenhouse gas emissions in the optimization calculation, but without an energy storage system. The optimization calculation with the renewable energy with energy storage system scenario prioritizes the objective function of greenhouse gas emissions by including the energy storage system. The proposed model in a multi-objective form is implemented in a case study of road transportation in the Province of Yogyakarta, Indonesia. The results obtained indicate that the renewable energy with energy storage system scenario produces the lowest emission level of 56.55 Mt CO2 Equivalent, but with the highest planning cost of 192.13 x 109 Billion USD.

Multiobjective Optimization Designs in Downlink NOMA Transmission Systems

To further balance the high-capacity transmission requirement and the scarce spectrum resources status quo, we investigate the joint optimization of energy efficiency (EE) and spectral efficiency (SE) in a downlink multiuser NOMA system. While ensuring the user quality of service (QoS), a power allocation algorithm based on two-layer optimization is proposed by transforming the original multiobjective optimization problem into a univariate problem through the linear weighted sum method. Based on the optimal user power allocation coefficient solution obtained from the inner layer, the outer layer applies the bisection principle to search for the optimal system transmit power according to the uniqueness of the maximum value of pseudo-concave functions. Simulation results show that the proposed algorithm always converges within fewer iterations and can achieve the flexible tradeoff between EE and SE. This is of great significance for improving the energy utilization of actual scenarios with time-varying communication demands.

A Load Forecasting Algorithm for Power Internet of Things Using Training Data Dimension Expansion and Ensemble Learning Technique

With the development of the power internet of things (IOT), load forecasting will play an important role the power system. It can optimize the power generation planning and improve the economical operation of power IOT. In this paper, a new loading forecasting algorithm for power IOT is proposed using training data dimension expansion and ensemble learning. In the offline phase, the obtained meteorological data and time information is normalized to remove the unit effect at first. Then, the Hampel filter is used to cope with the outliers of the meteorological data from sensors. Through the preprocessing, the fingerprint of the training data is constructed. Next, the matrix multiplication method is proposed to expand the dimension of training data fingerprint information. Finally, the ensemble learning combining multiple long short-term memory (LSTM) networks are proposed to obtain multiple power load forecasting models and corresponding weight coefficients. In the online phase, the obtained meteorological data and time information are preprocessed to form the input of and power load forecasting models. The final power load forecasting is obtained by linear weighted sum method with intermediate forecasting result. In the proposed algorithm, more features of training data can be obtained by the data dimension expansion. Moreover, the ensemble learning using LSTM can make fully use of the timing sequence of training and improve the generalization performance of offline training. Experiment results illustrate that the proposed algorithm has better forecasting performance than existing methods.

A Framework for Comparing Multi-Objective Optimization Approaches for a Stormwater Drainage Pumping System to Reduce Energy Consumption and Maintenance Costs

Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such as particle swarm optimization (PSO), multi-objective particle swarm optimization (MOPSO), and non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, a lack of comparison between these approaches poses a challenge to the selection of optimization approach for stormwater drainage pumping stations. In this paper, a new framework for comparing multi-objective approaches is proposed. Two kinds of evolutionary approaches, single-objective optimization and multi-objective optimization, are considered. Three approaches representing these two types are selected for comparison, including PSO with linear weighted sum method (PSO-LWSM), MOPSO with technique for order preference by similarity to an ideal solution (MOPSO-TOPSIS), and NSGA-II with TOPSIS (NSGA-II-TOPSIS). Four optimization objectives based on the number of pump startups/shutoffs, working hours, energy consumption, and drainage capacity are considered, of which the first two are new ones quantified in terms of operational economy in this paper. Two comparison methods—TOPSIS and operational economy and drainage capacity (E&C)—are used. The framework is demonstrated and tested by a case in China. The average values of the TOPSIS comprehensive evaluation index of the three approaches are 0.021, 0.154, and 0.375, respectively, and for E&C are 0.785, 0.813, and 0.839, respectively. The results show that the PSO-LWSM has better optimization results. The results validate the efficiency of the framework. The proposed framework will help to find a better optimization approach for pumping systems to reduce energy consumption and maintenance costs.

Deviation-based calibration for progressive damage analysis in pultruded glass fiber reinforced composites

This paper presents a systemic calibration methodology to efficiently simulate progressive damage evolution in four different pultruded glass fiber reinforced polymer (GFRP) composites using the strain-based COMposite DAMage Model (CODAM2) in the commercial finite element software LS-DYNA. In particular, Compact Tension (CT), scaled-up CT, and wide CT tests are simulated to find the best set of input parameters by considering four distinct indicators obtained from experimental and numerical load vs displacement data. By combining these indicators into a physically meaningful equivalent deviation value via a linear weighted-sum method, the results show that the most suited input damage variables yield physically accurate crack length predictions which underlines the robustness and accuracy of the proposed method. Furthermore, it is shown that the incorporation of bi-linear softening laws improves CODAM2 simulation results by up to 90%, however it also increases the number of parameters to be calibrated.

Spatio-temporal variation and coupling coordination relationship between urbanisation and habitat quality in the Grand Canal, China

Urbanisation leads to dramatic changes in regional land use, which significantly affects habitat quality. Research on the coupling coordination relationship between urbanisation and habitat quality is conducive to promoting regional ecological environment improvement and urban sustainable development. In this article, we adopted the InVEST model and linear weighted sum method separately to evaluate the spatio-temporal variation characteristics of habitat quality and urbanisation level of the Grand Canal. Then, we used the coupling coordination degree model (CCDM) to investigate the interactive coercing relationship between urbanisation and habitat quality. The purpose was to provide reference for future implementation of ecological conservation work along the Grand Canal and formulation of sustainable urban development strategies. The results showed that habitat quality has continued to decline from 1990 to 2018. After becoming a world cultural heritage site, the habitat quality of the partial reaches has improved. The comprehensive urbanisation level and the four subsystem urbanisation levels, including demographic urbanisation, spatial urbanisation, economic urbanisation, and social urbanisation, of 35 cities along the Grand Canal have all steadily increased from 1990 to 2018. The social urbanisation level was obviously lower than the urbanisation levels of the other three dimensions in most cities along the Grand Canal, indicating that the quality of urbanisation seriously lags behind the speed. The coupling coordination degree (CCD) between the urbanisation level of each city and the habitat quality of the reaches in the corresponding city exhibited a fluctuating but increasing relationship. Most cities have developed in a more coordinated direction and very few cities have deteriorated. Meanwhile, the coupling coordination characteristics between urbanisation level and habitat quality have constantly changed, from the urbanisation lagged type to the urbanisation-habitat synchronised type and the habitat quality lagged type. This article has important value for implementing the ecological conservation and restoration projects of the Grand Canal, optimizing land management policies, and improving the sustainable development level of cities along the Grand Canal.

An optimized complementary prediction method based on data feature extraction for wind speed forecasting

Wind speed prediction has a great significance to energy development. However, accurate wind speed prediction is a challenge owing to characteristics of wind series: randomness, fluctuation and nonlinearity. In this study, an optimized decomposed and ensemble multi-feature complementary forecasting method for wind speed is proposed, that mainly includes feature extraction and reconstruction, complementary prediction and nonlinear integration optimization. Firstly the Stacked Autoencoder (SAE) algorithm reconstructs characteristic series of wind speed sequence decomposed by the Variational Mode Decomposition (VMD), and then the Cuckoo Search (CS) algorithm finds weights of Linear Weighted Sum Method (LWSM) which integrates fitting results of the Support Vector Regression (SVR) and Bidirectional Long Short-term Memory (BiLSTM), finally the Bidirectional Gated Recurrent Unit (BiGRU) integrates the results of the Linear Weighted Sum Method, which corrects the errors of the complementary prediction results to optimize the prediction results. Two wind speed sequences are used to verify the proposed method obtained from Hexi Corridor area, China. Compared with other models, the model proposed in this paper has higher accuracy and stronger generalization ability than others.

The Impact of Resource Optimization on the Economic Development of the Marine Industry

The traditional impact analysis method for the economic development of marine industries does not consider the impact weight of different marine industries, which leads to an unsatisfactory effect on sustainable development after the optimized management of resources. Therefore, a new method is designed to solve the above problems; by analyzing the scale, hierarchical system, and optimizing the spatial resource framework of the marine industry, the influence of different marine industries is comprehensively measured, and the final demand dependency of the marine industry economy is obtained. The comprehensive index system is designed to evaluate the economic development of the marine industry, and the linear weighted sum method is used to calculate the sustainable development degree of marine industry economy in different regions. The results show that the final evaluation score of the marine biological industry in Shandong Province is 64.97, which is affected by comprehensive factors. The scores of capital and environmental governance are between 84.03 and 82.87, respectively, which are the main factors to achieve sustainable development. After the optimization of this method, the demand dependence of relevant regions is reduced, and the sustainable development level is effectively improved, indicating that the sustainable state of this method is better. The resource optimization path given by the research can be further applied to the construction process of marine and industrial economic development in other provinces and cities so as to comprehensively improve the quality and effect of marine economic growth.

Multi-strategy fusion differential evolution algorithm for UAV path planning in complex environment

The path planning of Unmanned Aerial Vehicle (UAV) is a real-world optimization problem, and even develops into a hard optimization problem with many objectives and constraints when UAVs work in a complex environment. In a complex environment, the resulting constraints can lead to decrease the quantity of the feasible solutions, so that can bring difficulties to plan routes for UAVs. Therefore, it is necessary to design a high-quality planner for a UAV in a complex environment. In this work, we have proposed a Multi-Strategy Fusion Differential Evolution algorithm (MSFDE). The proposed algorithm integrates the multi-population strategy, the novel self-adaptive strategy and the ensemble of the interactive mutation strategy in order to balance the exploitation and exploration capabilities. The multi-population strategy is used to divide the whole population into the three indicator subpopulations and a reward subpopulation for maintaining the diversity of the whole population; the novel self-adaptive strategy is introduced to control the parameters F and CR based on the teaching-learning-based optimization method; the ensemble of the interactive mutation strategy is to exchange the information among the three indicator subpopulations on each generation for boosting the population diversity. The constraints in the UAV path planning are transformed into the objective functions by the linear weighted sum method. Scenario 1, 2, 3, and 4 are designed with different complex level, and other eight algorithms are introduced to be compared with MSFDE. The simulation results confirm that MSFDE has an outstanding performance for the UAV three-dimensional path planning in the complex environment.