CN116432748A - Atmospheric pollutant tracing method based on improved JAYA algorithm - Google Patents
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Abstract
The invention discloses an atmospheric pollutant tracing method based on an improved JAYA algorithm, which creatively combines the JAYA algorithm with an unmanned aerial vehicle group and introduces the JAYA algorithm into an atmospheric pollutant tracing task. The self-evaluation theory in psychology is integrated into a JAYA algorithm, a self-evaluation two-dimensional model is established, each unmanned aerial vehicle can provide reliable basis for subsequent flight according to the difference of the self states, the unmanned aerial vehicle is facilitated to make a next decision, the social evaluation thought in the social emotion optimization algorithm is organically integrated with the unmanned aerial vehicle group, the unmanned aerial vehicle is divided into clumsy individuals, common individuals and smart individuals according to the social evaluation rules, and the position update is carried out according to the update rules of different individuals. Simulation results show that the improved JAYA algorithm has the characteristics of high convergence rate, difficult incidence of local optimum and the like, and has higher tracing efficiency and tracing success rate in the problem of tracing the atmospheric pollutants.
Description
Technical Field
The invention relates to an atmospheric pollutant tracing method based on an improved JAYA algorithm, and belongs to the field of multi-rotor unmanned aerial vehicle and atmospheric environment monitoring.
Background
Currently, the industrialization and urbanization processes are continuously accelerated, and the ecological environment problem of the city is increasingly prominent. With the rapid development of economy, the problem of atmospheric pollution is also continually aggravated. Because of the development of society and the improvement of living standard of people, people have higher requirements on air quality, so China also pays more attention to the improvement of air quality and the prevention and treatment of environmental pollution. At present, the problems of small coverage, lag in monitoring time and the like of the traditional atmosphere pollutant monitoring means generally exist, and the current environmental protection requirement cannot be met. In recent years, along with development of unmanned aerial vehicle technology and perfection of related laws and regulations, unmanned aerial vehicle technology has been widely applied in aspects of urban pollution monitoring, urban traffic management, forest fire prevention and the like. In the pollution monitoring field, the unmanned aerial vehicle has the advantages of low cost, flexibility, rapidness, high efficiency and the like, so that the unmanned aerial vehicle becomes an important means for monitoring and treating urban pollution at present, and the development of a multi-unmanned aerial vehicle collaborative traceability technology provides a new technical support for monitoring the atmospheric pollutants in a complex environment.
In the research of monitoring atmospheric pollutants, intelligent algorithms are increasingly applied, and common methods include particle swarm optimization algorithms, ant colony optimization algorithms, genetic algorithms and the like. However, the algorithm has the defect of easy local optimum, the performance of the algorithm is obviously affected after the control parameters are set and improved, meanwhile, the calculated amount of the algorithm is increased geometrically due to parameter adjustment, and if the parameters are improperly selected, the algorithm is also caused to be in local optimum, so that the convergence is poor and even unstable. The JAYA algorithm is a novel meta-heuristic optimization algorithm proposed by Rao in 2016, and has the biggest advantages of no need of any algorithm-specific control parameters, simple structure, high solving speed, strong global searching capability and the like compared with other intelligent optimization algorithms. The JAYA algorithm is based on the principle of trending away, namely, the algorithm is close to excellent individuals and is far away from poor individuals, so that the quality of a solution is continuously improved, but the diversity of the population is possibly reduced along with the increase of the convergence speed of the algorithm, so that the population falls into a local optimal area, and when the individuals are optimal solutions or worst solutions, the second term or the third term in the position update formula is 0, the algorithm is brought into an early state in advance, so that algorithm search is stagnated. Aiming at the problems existing in the basic JAYA algorithm, the invention provides the multi-unmanned aerial vehicle atmospheric pollutant tracing method based on the improved JAYA algorithm, which endows the unmanned aerial vehicle with the characteristics of people, introduces a self-evaluation theory in psychology, establishes a self-evaluation two-dimensional model, provides more reliable basis for the next action of the unmanned aerial vehicle, and improves the searching capability of the algorithm and the efficiency and accuracy of the multi-unmanned aerial vehicle collaborative tracing. And the method combines the thought of social evaluation in a social emotion optimization algorithm, carries out social evaluation on each unmanned aerial vehicle according to the searched concentration, divides the unmanned aerial vehicle into three individuals according to the social evaluation rule, and carries out different treatments on the unmanned aerial vehicle so as to increase population diversity and improve local development capability.
Disclosure of Invention
The invention aims to make up for the defects existing in the existing atmospheric pollutant tracing method. By combining a multi-unmanned aerial vehicle collaborative tracing system with an improved JAYA algorithm, an atmospheric pollutant tracing method based on the JAYA algorithm is provided. According to the method, self evaluation and social evaluation are introduced on the basis of the traditional method, so that the convergence speed and optimizing capability of an algorithm are improved, the attention to the individual state of the unmanned aerial vehicle is increased, and the more accurate and more efficient tracing effect of the atmospheric pollutants is realized.
An atmospheric pollutant tracing method based on an improved JAYA algorithm, the flow of which is shown in figure 1, comprises the following steps:
step 1: setting n unmanned aerial vehicles i (i=1..n) in a region to be monitored;
step 2: initializing and setting, namely setting an initial position of the unmanned aerial vehicle;
step 3: tracing the atmospheric pollutants by adopting an improved JAYA algorithm;
step 4: updating the positions of all unmanned aerial vehicles;
step 5: judging whether the tracing of the atmospheric pollutants is successful, if so, executing the step 6, otherwise, turning back to the step 3;
step 6: outputting the position of the atmospheric pollution source.
The process of the improved JAYA algorithm proposed in the step 3 is shown in fig. 2, and comprises the following steps:
step 3.1: initializing settings including the number n of unmanned aerial vehicles, the initial position of each unmanned aerial vehicle and the maximum iteration times;
step 3.2: obtaining a self-evaluation two-dimensional model and a self-evaluation factor according to the internal state of each unmanned aerial vehicle;
step 3.3: dividing unmanned aerial vehicles into three individuals according to the social evaluation rules by taking the detected concentration of each unmanned aerial vehicle as a basis, and calculating the social evaluation factors;
step 3.4: introducing the self-evaluation factors and the social evaluation factors into a position updating rule, and updating the position and the searched pollutant concentration according to the corresponding rule of the individual;
step 3.5: judging whether the iteration times are reached, if the iteration times are reached, turning to the step 3.6, and if the iteration times are not reached, adding 1 to the iteration times, turning to the step 3.2;
step 3.6: and outputting the optimizing result, and ending the algorithm.
The self-evaluating two-dimensional model mentioned in step 3.2 of the improved JAYA algorithm is shown in fig. 3, which consists of an X-axis and a Y-axis, describing the state of the unmanned aerial vehicle itself;
the X axis shows that the unmanned plane detects the distance situation between the unmanned plane and the teammate, when the unmanned plane is far away from the teammate, the unmanned plane is more confident, the execution command of the fruit break is the largest possible to finish the tracing task, and when the unmanned plane is near to the teammate, the unmanned plane tends to reduce the stride or return to the stride to avoid collision;
the Y-axis represents the electric quantity condition of the unmanned aerial vehicle, when the electric quantity is more, the unmanned aerial vehicle has the capability of executing corresponding tasks, when the electric quantity is lower, the unmanned aerial vehicle is easier to fatigue, and the unmanned aerial vehicle has certain difficulty in carrying out the next step of tracing task;
in this self-evaluation two-dimensional model, each coordinate value determination rule is as follows:
wherein a is i To self-evaluate the value of the X-axis of the two-dimensional model, b i For self-evaluation of the numerical value of the Y axis of the two-dimensional model, n is the population scale, namely the number of unmanned aerial vehicles, w i For unmanned plane i to be spaced apart from other teammates by more than a safe distance d min Number, Q of i Is the self electricity percentage of the unmanned aerial vehicle.
Unmanned aerial vehicle self-evaluation factor P self，i Can be expressed as:
the social evaluation rules mentioned in step 3.3 of the improved JAYA algorithm divide the drone into three individuals, the social evaluation rules of which are as follows:
wherein c i For the pollutant concentration value searched by the unmanned aerial vehicle i at the current position, the average pollutant concentration searched by the unmanned aerial vehicle group is as follows
Classifying the individuals with the concentration value higher than the average concentration as high-quality individuals, calculating the average concentration of the high-quality individuals and marking as c gmean ；
Social evaluation factor P of unmanned aerial vehicle social，i The calculation formula is as follows:
wherein, c max The highest concentration value searched in the unmanned aerial vehicle group.
In step 3.4 of the JAYA algorithm improvement, it is mentioned that the unmanned aerial vehicle should update the position according to the update rule of different individuals, when the concentration detected by the unmanned aerial vehicle is lower than the overall average concentration, the search effect of the clumsy individual at the current position is proved not to be great, and the search space of the individual needs to be expanded, and the position update rule is as follows:
wherein X is i Is the current search position of the unmanned aerial vehicle, X i+1 Is the updated search position of the unmanned aerial vehicle, X best X is the position of the unmanned aerial vehicle with the highest concentration of the searched pollutants worst R is the position where the unmanned aerial vehicle with the lowest concentration of the searched pollutants is located 1 Is [0,1]Random numbers in between;
when the concentration detected by the unmanned aerial vehicle is higher than the overall average concentration but lower than the average concentration of the high-quality individuals, the searching of the common individuals at the current position is proved to have a certain effect, but the improvement is still needed, and the position updating rule is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
is the average position of unmanned aerial vehicle population, which is r 2 Is [0,1]Random numbers in between;
when the concentration detected by the unmanned aerial vehicle is higher than the average concentration of the high-quality individuals, the searching of the smart individuals at the current position is very effective, and the position updating rule is as follows:
wherein r is 3 、r 4 Is [0,1]The random number between the two is 1, n]Random integer in between, and l, m are not equal to i.
The invention has the beneficial effects that:
the invention creatively combines the multi-unmanned aerial vehicle atmospheric pollutant tracing task with the JAYA algorithm, improves the JAYA algorithm, and provides an atmospheric pollutant tracing method based on the improved JAYA algorithm.
The invention provides a self-evaluation two-dimensional model which is used for describing the internal state of the unmanned aerial vehicle, and in the psychological concept, a person with higher self-evaluation level can grasp the self-evaluation, so that the self-evaluation is carried out according to the internal state of the unmanned aerial vehicle, and the method is beneficial to deciding the advancing direction of the next step. The positive self-evaluation indicates that the current state is good, so that the unmanned aerial vehicle has stronger upper core, and tends to pursue a successful target, and the negative self-evaluation enables the unmanned aerial vehicle individual to avoid risk and protect the unmanned aerial vehicle individual.
In this case, the unmanned aerial vehicle has preliminarily provided anthropomorphic emotion, on the basis of which, in order to more accurately reflect the emotion condition of the unmanned aerial vehicle, the invention simulates the thought of social evaluation in a social emotion optimization algorithm, and utilizes the social evaluation rule to divide the unmanned aerial vehicle into three categories of clumsy individuals, ordinary individuals and smart individuals, wherein the emotion condition of the individual with higher social evaluation is better, and the next stepping behavior is selected based on the emotion condition.
In the original JAYA algorithm, the random number is adopted in the position updating rule, and the social evaluation factor and the self-evaluation factor are introduced into the position updating rule, so that the next behavior of the unmanned aerial vehicle is more dependent through judging the self state and the emotion condition of the unmanned aerial vehicle.
With the increase of the iteration times, most individuals gather around the current optimal individual to perform local search, while other few lagged individuals are far away from the current optimal individual to perform global exploration tasks. In the searching process, the average position of the current population always moves, so that the average value of the current position is introduced into the position updating rule of the common individual, and the algorithm has the opportunity of escaping from local optimum, thereby improving the searching performance of the population.
The current optimal individual in the smart individual is considered as a leading person in the unmanned aerial vehicle group, the current optimal individual is taken as a guide, the convergence speed of the JAYA algorithm is increased, and a random disturbance term is added in the position updating rule of the smart individual, namely, two unmanned aerial vehicles are randomly selected to utilize the current positions of the unmanned aerial vehicles, so that the situation that the position cannot be updated when the current individual is the optimal individual and the second term of the formula is 0 is avoided.
Drawings
FIG. 1 is a flow chart of an atmospheric contaminant tracing method based on the modified JAYA algorithm;
FIG. 2 is a flowchart of an improved JAYA algorithm;
FIG. 3 is an exemplary diagram of a self-evaluating two-dimensional model;
FIG. 4 is a simulated roadmap under a Gaussian steady-state plume concentration field artificially built in MATLAB software with an improved JAYA algorithm;
FIG. 5 is a graph comparing the optimizing efficiency under a Gaussian steady-state plume concentration field of the improved JAYA algorithm and the standard JAYA algorithm;
the specific embodiment is as follows:
the invention is implemented as follows:
step 1: setting n unmanned aerial vehicles i (i=1..n) in a region to be monitored, wherein 5 unmanned aerial vehicles are set in the simulation;
step 2: initializing and setting, namely setting an initial position of the unmanned aerial vehicle;
step 3: tracing the atmospheric pollutants by adopting an improved JAYA algorithm;
step 4: updating the positions of all unmanned aerial vehicles;
step 5: judging whether the tracing of the atmospheric pollutants is successful, if so, executing the step 6, otherwise, turning back to the step 3;
step 6: outputting the position of the atmospheric pollution source.
The improved JAYA algorithm proposed in step 3 comprises the following steps:
step 3.1: initializing settings, including the number n=5 of unmanned aerial vehicles, the initial position of each unmanned aerial vehicle and the maximum iteration times;
step 3.2: obtaining a self-evaluation two-dimensional model and a self-evaluation factor according to the internal state of each unmanned aerial vehicle;
step 3.3: dividing unmanned aerial vehicles into three individuals according to the social evaluation rules by taking the detected concentration of each unmanned aerial vehicle as a basis, and calculating the social evaluation factors;
step 3.4: introducing the self-evaluation factors and the social evaluation factors into a position updating rule, and updating the positions and the searched pollutant concentrations according to the updating rules of different individuals;
step 3.5: judging whether the iteration times are reached, if the iteration times are reached, turning to the step 3.6, and if the iteration times are not reached, adding 1 to the iteration times, turning to the step 3.2;
step 3.6: and outputting the optimizing result, and ending the algorithm.
In the self-evaluating two-dimensional model mentioned in step 3.2 of the modified JAYA algorithm, each coordinate value determination rule is as follows:
unmanned aerial vehicle self-evaluation factor P self，i Can be expressed as:
improving the social evaluation rules and social evaluation factors mentioned in step 3.3 of the JAYA algorithm:
social evaluation rules:
social evaluation factor P of unmanned aerial vehicle social，i The calculation formula is as follows:
in step 3.4 of the improved JAYA algorithm, it is mentioned that the unmanned aerial vehicle should perform position update according to update rules of different individuals;
the awkward individual location update rules are:
the location update rules of the common individuals are:
the location updating rule of the smart individual is as follows:
the individual paths of the unmanned aerial vehicle group in the simulation experiment of the Gaussian steady-state smoke plume concentration field are shown in a graph in fig. 4, the optimization efficiency graph of the basic JAYA algorithm and the improved JAYA algorithm is shown in a graph in fig. 5, and simulation experiment results show that the improved JAYA algorithm has good searching performance and good convergence, so that the efficiency of the multi-unmanned aerial vehicle collaborative tracing of atmospheric pollutants is improved.
The present invention is not limited to the above-mentioned embodiments, and any person skilled in the art should make changes or substitutions within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (4)
1. An atmospheric pollutant tracing method based on an improved JAYA algorithm is characterized by comprising the following steps:
step 1: setting n unmanned aerial vehicles i (i=1..n) in a region to be monitored;
step 2: initializing and setting, namely setting an initial position of the unmanned aerial vehicle;
step 3: the method adopts an improved JAYA algorithm to trace the source of the atmospheric pollutants, and specifically comprises the following steps:
step 3.1: initializing settings including the number n of unmanned aerial vehicles, the initial position of each unmanned aerial vehicle and the maximum iteration times;
step 3.2: obtaining a self-evaluation two-dimensional model and a self-evaluation factor according to the internal state of each unmanned aerial vehicle;
step 3.3: dividing unmanned aerial vehicles into three individuals according to the social evaluation rules by taking the detected concentration of each unmanned aerial vehicle as a basis, and calculating the social evaluation factors;
step 3.4: introducing the self-evaluation factors and the social evaluation factors into a position updating rule, and updating the positions and the searched pollutant concentrations according to the updating rules of different individuals;
step 3.5: judging whether the iteration times are reached, if the iteration times are reached, turning to the step 3.6, and if the iteration times are not reached, adding 1 to the iteration times, turning to the step 3.2;
step 3.6: outputting an optimizing result, and ending the algorithm;
step 4: updating the positions of all unmanned aerial vehicles;
step 5: judging whether the tracing of the atmospheric pollutants is successful, if so, executing the step 6, otherwise, turning back to the step 3;
step 6: outputting the position of the atmospheric pollution source.
2. The atmospheric pollutant tracing method based on the improved JAYA algorithm as claimed in claim 1, wherein the self-evaluation two-dimensional model and the self-evaluation factor mentioned in the step 3.2 in the step 3 improved JAYA algorithm are introduced into the self-evaluation theory in psychology, so that a more reliable basis is provided for the next action of the unmanned aerial vehicle;
the determination rule of each coordinate value of the self-evaluation two-dimensional model is as follows:
unmanned aerial vehicle self-evaluation factor P self，i Can be expressed as:
wherein a is i To self-evaluate the value of the X-axis of the two-dimensional model, b i For self-evaluation of the numerical value of the Y axis of the two-dimensional model, n is the population scale, namely the number of unmanned aerial vehicles, w i For unmanned aerial vehicle i and other unmanned aerial vehicles interval exceeds safe distance d min Number, Q of i For unmanned aerial vehicle self electric quantity percentage, this self-evaluation two-dimensional model comprises X axle and Y axle, the self state of unmanned aerial vehicle has been described, the X axle represents that this unmanned aerial vehicle detects the distance condition with the teammate, when unmanned aerial vehicle and teammate are far away, the more confident that unmanned aerial vehicle represents, the task of tracing to the source is accomplished to the biggest possible completion of the execution command of fruit break, when unmanned aerial vehicle and teammate are nearer, then can tend to reduce stride or avoid the collision of moving backwards, the Y axle represents the electric quantity situation of this unmanned aerial vehicle, unmanned aerial vehicle has the ability to go to carry out corresponding task when the electric quantity is more, unmanned aerial vehicle then can be tired out more easily when the electric quantity is lower, it has certain difficulty to carry out the task of tracing to the source of next step.
3. The atmospheric pollutant tracing method based on the improved JAYA algorithm according to claim 1, wherein the step 3 is to improve the social evaluation rule and the social evaluation factor mentioned in the step 3.3 in the JAYA algorithm, combine the concept of social evaluation in the social emotion optimization algorithm, divide the unmanned plane into three individuals and calculate the social evaluation factor;
the social evaluation rules are as follows:
social evaluation factor P of unmanned aerial vehicle social，i The calculation formula is as follows:
wherein, c i For the pollutant concentration value searched by the unmanned aerial vehicle i at the current position, the average pollutant concentration searched by the unmanned aerial vehicle group is as follows
Average concentration of high-quality individuals c gmean ，c max The highest concentration value searched in the unmanned aerial vehicle group.
4. An atmospheric contaminant traceability method based on an improved JAYA algorithm according to claim 1, wherein the updating rules of the different individuals mentioned in step 3.4 in the improved JAYA algorithm of step 3 are as follows:
clumsy individual location update rules:
location update rules for common individuals:
location update rules for smart individuals:
wherein X is i Is the current search position of the unmanned aerial vehicle, X i+1 Is the updated search position of the unmanned aerial vehicle, X best X is the position of the unmanned aerial vehicle with the highest concentration of the searched pollutants worst For the location of the unmanned aerial vehicle with the lowest concentration of the searched pollutants,
is the average position of unmanned aerial vehicle population, r 1 、r 2 、r 3 、r 4 Is [0,1]The random number between the two is 1, n]Random integer in between, and l, m are not equal to i.
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