Ant Algorithm Research Articles

An empirical study of improved ant colony clustering algorithm in English composition review

An empirical study of improved ant colony clustering algorithm in English composition review

Application of Blockchain Technology in Energy Sector for Sustainable Future

In old energy control system, there was no freedom for users to sale or purchase energy with their own choice and rate because system was centralized and also there were many issues such as electricity theft cases and losses and burden of these losses directly inculdded in public bills. Now after the introduction of blockchain these issues has been resolved such as the blockchain technology give freedom to user to sale or purchase energy according to the own rate with secure transaction system. The conventional energy system with centralizedcontrol technique fails to resolve many issues such as freedom to user’s to sale or purchase energy in network in secure way and combination of blockchain and Microgrid has all feature to resolve all present isssues. In this paper, the blockchain technology features and application of blockchain with clean energy system will be investigate to provide cheap electricity to users by using Microgrid because Blockchain provide complete solutions for energy distribution and trading. In addition, somemajor present issues will be focus such as low efficiency, losses and ant algorithm model to distribute the energy in the network efficiently.

Data-driven Game-theoretic Model Based on Blockchain for Managing Resource Allocation and Vehicle Routing in Modular Integrated Construction

ABSTRACT In modular integrated construction (MiC), the resource allocation problem (RAP) adopted by construction sites and vehicle routing problem (VRP) adopted by logistics companies are highly interdependent. However, this interdependence has been overlooked in the literature. Thus, the plans determined by each problem cannot be achieved practically. Moreover, there is no existing VRP model suitable for the MiC application. This study aims to propose a VRP model suitable for MiC, investigate the interdependence between the RAP and proposed VRP model, and develop a blockchain system to secure information sharing between the RAP and VRP. The first two objectives are achieved by developing a coordinated system formulated as a leader-follower Stackelberg game model (LFSGM). This model is presented as a bi-level optimisation model and solved using a nested ant colony optimisation-based algorithm. The effectiveness of the LFSGM is validated using a real case study. The results show that using the traditional approach (i.e. a separate RAP and proposed VRP) succeeds in providing a plan for the construction sites but not for the logistics company. In contrast, the LFSGM offers applicable plans for both the construction sites and logistics company. Lastly, some managerial implications are identified and discussed.

The Information Technology for Determining Vehicle Route Based on Ant Colony Algorithms

The article proposes on the information technology for determining vehicle route based on ant colony algorithms. The analysis of specialized software and information technologies for determining vehicle route was carried out. Existing information technologies for determining the route of vehicles are not able to find an accurate solution or require an unreasonably long time to find effective solutions. The relevance of the article is due to the great complexity and dimension of routing problems, as well as the emergence of its new verifications. In this regard, there is a need to develop new information technologies for solving problems of this class. The proposed information technology is based on ant colony algorithms. This information technology allows to reduce the time of searching for solutions in problems of large dimensions, and at the same time improve the quality of the solutions obtained. Information technology for determining the route of the vehicle will be considered in the form of a set of functions. This set of functions are related to each other in a certain way. This set of functions implement techniques, methods and methods that ensure the receipt, storage, processing, transmission and use of information regarding the route of the vehicle. In the paper, we will use the IDEF0. This is required for visualization and further formal representation of the structure and composition of the information technology for determining the route of the vehicle. We will present the Determining Route Information Technology in its entirety: the tuple and the sets. The top-level context diagram and. the upper child diagram top-level context diagram are presented. When determining the route of the vehicle, we will use ant algorithms. The main modifications of the classic ant colony algorithm are presented: Max-Min Ant System, Elitist Ant System, Q-ant system, Rank ant system, Ant Colony System, Fast Ant System, Antabu

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

<span lang="EN-US">We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. </span>

A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multi-objective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient “multiple populations for multiple objectives” framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusion-based solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.

Methodical approach to increase the speed of decision-making in information systems

The relevance of the research lies in the need to increase the efficiency of the process of evaluating the object of evaluation while ensuring the given reliability, regardless of the hierarchical construction of the object of evaluation. The object of research is information systems. The subject of the study is the efficiency of the evaluation process. The hypothesis of the study is to increase the efficiency of the process at a given reliability. In the study, an improved methodology for increasing the efficiency of the evaluation process based on bio-inspired algorithms was proposed. In the course of the conducted research, the general provisions of the theory of artificial intelligence were used to solve the problem of analyzing the state of objects in intelligent decision support systems.
 The essence of improvement is to use the following procedures:
 − taking into account the type of uncertainty about the state of the object of evaluation;
 − taking into account the degree of the noise of the data on the state of the object of evaluation;
 − using the ant algorithm and the genetic algorithm to find the path metric when evaluating the state of the evaluation object;
 − deep learning of synthetic ants using evolving artificial neural networks.
 An example of the use of the proposed methodology is presented in the example of the assessment of a hierarchical object. The specified model showed a 15−22 % increase in data processing efficiency due to the use of additional improved procedures

Blending multiple algorithmic granular components: a recipe for clustering

Emerging trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are two broad types of hybridization: collaborative—individual algorithms tackle an instance of the problem sequentially or in parallel and exchange information accordingly while solving the problem; integrative—individual algorithms are dedicated to tackling different aspect(s) of the problem-solving process. In this research, we propose a schema for an enhanced form of integrative hybridization that blends granular algorithmic components from multiple algorithms to derive a new singular clustering algorithm. As a case study, we examine the ant clustering algorithm (a swarm intelligence algorithm that is based on the natural phenomenon of brood sorting in some species of ants); highlight the strengths and weaknesses of the algorithm; and present a blend of algorithmic components from Tabu search into the algorithm to improve its exploration strategy and solution quality. Empirical results from applying the blended algorithm to clustering benchmark datasets show improved clustering validation measures for the proposed blended hybrid algorithm compared to other hybridization of the same underlying individual algorithms. Besides, the quality of clusters uncovered by this hybrid algorithm competes favorably with those uncovered using popular clustering algorithms such as DBSCAN and mean shift.

OPTIMIZATION OF SWARM ROBOTICS ALGORITHMS

Context. Among the variety of tasks solved by robotics, one can single out a number of those for the solution of which small dimensions of work are desirable and sometimes necessary. To solve such problems, micro-robots with small dimensions are needed, the mass of which allows them to move freely in tight passages, in difficult weather conditions, and remain unnoticed. At the same time, the small dimensions of the microrobot also impose some indirect restrictions; therefore, it is better to use groups of microrobots for the solution of these problems. The efficiency of using groups of microrobots depends on the chosen control strategy and stochastic search algorithms for optimizing the control of a group (swarm) of microrobots.
 Objective. The purpose of this work is to consider a group of swarm algorithms (methods) belonging to the class of metaheuristics. The group of these algorithms includes, in particular, the ant colony algorithm, the possibilities of which were investigated to solve the traveling salesman problem, which often arises when developing an algorithm for the behavior of a group of microrobots.
 Method. At the first stage of the study, the main groups of parameters were identified that determine the flow and characterize the state at any time of the ant colony algorithm: input, control, disturbance parameters, output parameters. After identifying the main groups of parameters, an algorithm was developed, the advantage of which lies in scalability, as well as guaranteed convergence, which makes it possible to obtain an optimal solution regardless of the dimension of the graph. At the second stage, an algorithm was developed, the code of which was implemented in the Matlab language. Computer experiments were carried out to determine the influence of input, control, output, and disturbance parameters on the convergence of the algorithm. Attention was paid to the main groups of indicators that determine the direction of the method and characterize the state of the swarm of microrobots at a given time. In the computational experiment, the number of ants placed in the nodes of the network, the amount of pheromone, the number of graph nodes were varied, the number of iterations to find the shortest path, and the execution time of the method were determined. The final test of modeling and performance of the method was carried out.
 Results. Research has been carried out on the application of the ant algorithm for solving the traveling salesman problem for test graphs with a random arrangement of vertices; for a constant number of vertices and a change in the number of ants, for a constant number of vertices at different values of the coefficient Q; to solve the traveling salesman problem for a constant number of vertices at different values of the pheromone evaporation coefficient p; for a different number of graph vertices. The results showed that ant methods find good traveling salesman routes much faster than clear-cut combinatorial optimization methods. The dependence of the search time and the found optimal route on the values of control parameters are established using the example of test networks for a different number of graph vertices and iterations.
 Conclusions. The studies were carried out to make it possible to give recommendations on the application of the ant colony algorithm to control a group (swarm) of microrobots.

Early detection and classification of liver diseases in ultrasound images using hybrid machine learning techniques

Liver disease is a form of sickness that starts in the ovaries and is particularly dangerous for women. As a result, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a sort of risky improvement that impacts ovaries in ladies, and is hard to perceive at early phase because of which it stays as one of the guideline wellsprings of illness end. Unquestionable confirmation of intrinsic and typical parts is immense in making novel frameworks to perceive and ruin danger. Here, using hybrid machine learning approaches, we propose an early detection and classification of liver disease (EDCLD) in ultrasound images. This study is aimed at detecting and evaluating liver disease, which may be benign or malignant depending on the cancer type. To begin with, we use an improved ant swarm optimization algorithm for both high and low level feature extraction models. For liver disease classification, we used hybrid swallow swarm intelligent-deep neural network (HSSI-DNN) classifier to identify the disease stage. By using training and testing liver datasets, we are improving the accuracy of liver disease detection with several stages. Second, we present a search optimization for whales algorithm for cascaded and segmentation as a single fusion feature capable of representing both the texture patterns and semantic background distributed in the image. Third, the dividend yields are commitment to the HSSI-DNN classifier used to detect the “Normal” and “Diseases” from input image. The proposed HSSI-DNN classifier performance outperformed the existing classifier in terms of accuracy (0.98), specificity (0.98), and sensitivity (1). The proposed EDCLD technique implemented in MATLAB.

Optimal Method for Allocation of Tractors and Trailers in Daily Dispatches of Road Drops and Pull Transport

The domestic road drop and pull transportation system allows only tractors and semitrailers. In this mode, any tractors can only run with one semitrailer at a time or with no load. By optimizing the tractor scheduling plan, the no-load mileage of the tractor can be reduced, which can improve the efficiency and reduce the number of tractors. In this article, we have developed an optimization model for the tractor routing scheme to minimize the total cost of the drop and pull transportation system, which can limit the total number of tractors because the tractor can transport as many semitrailers to the destination as possible within the time window. Focusing on this mixed integer nonlinear problem, an improved ant search algorithm is designed. Finally, with Sichuan’s Anji Logistics Enterprise as the background, this tractor scheduling optimization model is applied to an ideal network and a real scenario. The results show that the optimized system reduces total cost by approximately18.7% and the ratio of tractors to semitrailers is approximately 1 : 3.31.

Enhancing Two-Phase Supply Chain Network Distribution via Three Meta-Heuristic Optimization Algorithms Subsidized by Mathematical Procedures

Supply Chain Networks Distribution (SCND) topology aims to find the best position and size for facilities to ensure optimal products flow based on the Matheuristic approach (i.e. decomposition meta-heuristics). This problem is a multi-objective function designed to reduce the transported costs and associated delivery times. The Matheuristic presents a brilliant hybridize between the meta-heuristics steps and mathematical procedures in solving large-size problems with the slightest deviation [Formula: see text]. This paper proposed an ant colony-based algorithm evolved by mathematical procedures called Mat-ACO, compared with SA “simulated annealing” and CA “Camel algorithm.” The authors deduced that the mathematical solution is limited as the instances grow, significantly if increased than 600 network hotspots. The Mat-ACO, SA, and CA results are close to counterparts obtained by LINGO, with a difference of 2.03%, 2.49%, and 3.75%, respectively, and continue to extract results from more than 1350 network hotspots. The main contribution is to find the optimum tuning parameters, which will reduce the deviation from the exact solution. This paper reveals that no feasible solution can catch the LINGO at large-size problems. At the same time, the CA is superior to SA in the large problem sizes, while Mat-ACO still presents preferred solutions in minimum time. The proposed methodology is classified as a closed-loop network strategy that targets green management.

A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem.

The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

An Ant Colony Optimization Behavior-Based MOEA/D for Distributed Heterogeneous Hybrid Flow Shop Scheduling Problem Under Nonidentical Time-of-Use Electricity Tariffs

This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical time-of-use electricity tariffs (DHHFSP-NTOU). The makespan and the total electricity charge are considered as the optimization objectives from the view of production and management. The DHHFSP-NTOU considers different processing capabilitie and time-of-use electricity tariffs for each factory. The mixed-integer linear programming (MILP) model of DHHFSP-NTOU is established. To solve the DHHFSP-NTOU, this article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D). A problem-specific ant colony behavior is presented to construct offspring individuals. Eight neighborhoods within the factory and between factories are adopted to improve the quality of the individuals in the archive set. A right-shift movement is used to reduce the electricity charge. A large number of numerical experiments and comprehensive investigations are carried out to test the efficiency and effectiveness of ACO_MOEA/D. The experimental results show that each component (e.g., ant colony behavior, neighborhoods move operators, right-shift movement) contributes to the performance of ACO_MOEA/D. The comparisons with several related algorithms show the superiority of ACO_MOEA/D for solving the DHHFSP-NTOU. Note to Practitioners—From the managers’ insights, the electricity charge is a large cost in the production. The scheduling is an economical approach to reduce the electricity charge. For the time-of-use (TOU) tariffs, the managers can adjust the schedule to reduce the idle time or move some operations to the interval period with a lower electric price. This article studies a distributed heterogeneous hybrid flow shop scheduling problem under nonidentical TOU (UTOU) electricity. This model can be used in many manufacturing enterprises that have several heterogeneous factories. This article proposes an ant colony optimization behavior-based multiobjective evolutionary algorithm based on decomposition (ACO_MOEA/D) to minimize the makespan and the total electricity charge. The ACO_MOEA/D can provide the economy and high-efficiency schedules for practitioners. The computational results confirm its effectiveness and efficiency.

Highly Secure Cryptography Algorithm Method to Safeguard Audios and Visuals

Internet hacking has become common now a days and is increasing day by day. It is a high time to safeguard our data. There are several cryptographic methods and algorithms that are evolved and already exist. How about additional protection makes us stress free? In this paper, I present a unique design of cryptographic algorithm which is specifically designed for Auditory cryptography and visual cryptography to make the encryption and decryption technique stronger. The purpose is to make it very difficult to decode the file when an unauthorized user accesses the data. This algorithm is a combination of multiple techniques such as Ant Algorithm, Logical Gates Technique, Dual authorization PINs, Indexed Arrays. Combination of these techniques makes the algorithm unique and strong to secure the data. This research was implemented on audio files, images and video files. The study of the result shows effective way of masking the data as it is hard to decode without PINs. Also, performance of the algorithm is efficient during encryption and decryption process.

Machine-Vision-Based Enhanced Deep Genetic Algorithm for Robot Action Analysis.

The machine-driven products processed by CNC machine tools are divided into multiple parts, which are often transmitted to unqualified processing crystals due to the error of the part's posture, thus affecting the projection effect. Aiming at handling this problem, we, in this work, propose a method for locating and correcting machine capabilities based on bicycle visual recognition. The robot obtains coordinates and offset points through the vision system. In order to satisfy the requirement of fast sorting of the robot parts, a robot species method (BAS-GA) that supports machine vision and improved genetic algorithm rules is proposed. The rank method first preprocesses the part copies, then uses the Sift feature twin similarity notification algorithm to filter the part idols, and finally uses in-law deformation to place the target parts. Afterward, an particular model is built for the maintenance of the nurtural skill, and the mathematical mold is solved using the BAS-GA algorithmic program authority. The close trail of the robot is maintained to manifest the marijuana of the robot. Experimental inference have shown that the BAS-GA algorithm rule achieves the optimal conclusion similar to the pseudo-annealing algorithmic program plant. Meanwhile, the genetic algorithm rule is modified ant settlement algorithm program. Knife sharpening was also reduced by 7%, inferring that this process can effectively improve the robot's action success rate.

A New Commodity Distribution Approach Based on Asymmetric Traveler Salesman Using Ant Colony Algorithm

The traveling salesman problem is one of the most well-known hybrid optimization problems. It is one of the (NP-complete) problems that its various applications have theoretically and operationally attracted the attention of researchers. Given that the existing optimization methods to solve such problems include many variables and constraints and reduce their practical efficiency in solving problems with larger dimensions, we have seen the use of algorithms in recent decades. In this research, after determining a linear programming model for the asylum seeker problem with asymmetric distances and solving it in Lingo software, I used two ant cloning algorithms and a forbidden search algorithm to solve the problem in large dimensions. By adjusting the parameters of the two algorithms using the Taguchi method to prove the efficiency of the two algorithms, we compared their results by solving the linear programming model in small-dimensional problems. Then, to compare the results and execution time of the two algorithms, we solved the problem in medium and large dimensions.

Search and Rescue in a Maze-like Environment with Ant and Dijkstra Algorithms

With the growing reliability of modern ad hoc networks, it is encouraging to analyze the potential involvement of autonomous ad hoc agents in critical situations where human involvement could be perilous. One such critical scenario is the Search and Rescue effort in the event of a disaster, in which timely discovery and help deployment is of utmost importance. This paper demonstrates the applicability of a bio-inspired technique, namely Ant Algorithms (AA), in optimizing the search time for a route or path to a trapped victim, followed by the application of Dijkstra’s algorithm in the rescue phase. The inherent exploratory nature of AA is put to use for faster mapping and coverage of the unknown search space. Four different AA are implemented, with different effects of the pheromone in play. An inverted AA, with repulsive pheromones, was found to be the best fit for this particular application. After considerable exploration, upon discovery of the victim, the autonomous agents further facilitate the rescue process by forming a relay network, using the already deployed resources. Hence, the paper discusses a detailed decision-making model of the swarm, segmented into two primary phases that are responsible for the search and rescue, respectively. Different aspects of the performance of the agent swarm are analyzed as a function of the spatial dimensions, the complexity of the search space, the deployed search group size, and the signal permeability of the obstacles in the area.

Construction of Computer Algorithms in Bioinformatics of the Fusion Genetic Algorithm

With the continuous in-depth exploration of life sciences, bioinformatics based on life sciences, computer algorithms, and statistics have gradually developed. The research of bioinformatics mainly focuses on the study of genes, and the structural characteristics of genes lead to a large amount of extremely complex data in the study of bioinformatics. Analyzing data in bioinformatics research requires accurate calculation by computer algorithms. However, common computer algorithms such as the dynamic programming algorithm and the genetic algorithm have the disadvantages of large memory or inaccurate optimization. Combining the ant colony algorithm (ACA) and GA can give the advantages of the two methods that should be fully utilized to efficiently analyze the biological information data. In this paper, the ant colony fusion genetic algorithm (ACA-GA), GA, and dynamic programming algorithm are used to compare and analyze the sensitivity, convergence speed, sequence alignment accuracy, and required memory space of gene sequences. The experimental results show that compared with ACA-GA, the dynamic programming algorithm has the advantage of finding the optimal alignment of 100%, but the memory required is too large, the memory required is more than 10 times that of the ant colony fusion genetic algorithm and the sensitivity is not as good as ACA-GA. The convergence speed of ACA-GA is faster than the gene comparison speed of GA and the accuracy is 2.6% better than that of GA on average. ACA-GA has the advantages of GA and ACA, which can improve the computational efficiency of biological data in bioinformatics.

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.