CN107067075A - A kind of urban land ecological safety space exploration model based on artificial bee colony algorithm - Google Patents

Abstract

The invention discloses an urban land ecological security space detection model based on an artificial bee colony algorithm, including an artificial bee colony algorithm, an objective function, and a model structure design. With the rapid development of social economy, cities continue to expand to the surrounding area, engulfing the surrounding areas of the city Land with good ecology has led to increasingly prominent contradictions between man and land, and has caused a series of ecological security issues such as land resource shortage, soil erosion, ecological deterioration, and environmental pollution. As the ecosystem most disturbed by human activities, the maintenance of urban ecological security and stability is very important for the development of cities and regions. Therefore, the spatial detection and evaluation of urban land ecological security is of great significance for alleviating the contradiction between people and land in regions and cities, and coordinating the contradiction between population, resources and environment. The purpose of this invention is to provide new methods and methods for urban land security research. technical approach.

Description

A Spatial Detection Model of Urban Land Ecological Security Based on Artificial Bee Colony Algorithm

technical field

The invention relates to the technical field of ecological security, in particular to an urban land ecological security space detection model based on an artificial bee colony algorithm.

Background technique

The urban fringe area is the forerunner area of urban space expansion, and the sprawl and generation of urban fringe area is the main mode of urban space expansion. The urban fringe area, as the boundary zone between the urban ecosystem and the rural ecosystem, often produces system fringe effects. The transformation and coexistence of various land use types, the mixed living of urban and rural residents, and the mixed appearance of urban and rural landscapes are the most important factors in urbanization. However, there is no relatively mature theory on how to quantitatively describe and evaluate the marginal effects of urban ecosystems.

It is a well-known phenomenon that the formation and development of a city is closely related to environmental conditions. Its development depends on a good natural environment, and it also profoundly affects the natural environment. Scholars such as Fang Chuanglin and Jinchuan found that there is an interactive coupling relationship between the urbanization process and the ecological environment. The coupling relationship is the sum of the non-linear relationship between urbanization and ecological environment in the process of urbanization development. Wei Xiaojie and other scholars proposed to use the gray correlation method to evaluate and analyze the coupling relationship between the urbanization process and the ecological environment response of oasis cities in arid areas. Some scholars proposed the theory of using system coupling and coordination.

Contents of the invention

The purpose of the present invention is to provide a spatial detection model of urban land ecological security based on the artificial bee colony algorithm to solve the problems raised in the above-mentioned background technology.

In order to achieve the above object, the present invention provides the following technical scheme: a kind of urban land ecological safety space detection model based on artificial bee colony algorithm, including artificial bee colony algorithm, objective function, model structure design, food in the artificial bee colony algorithm The value of the source depends on many factors, such as the distance from the hive, the abundance of food, the difficulty of obtaining food, etc. For simplicity, the "yield" of the food can be expressed as a single value. When solving the optimization When asking questions, the food source represents various possible food sources in the spatial range. The bees are associated with the specific food source. The food source is the food source they are currently collecting. They carry information about the food source, distance, orientation and Yield” and share this information with other bees. Waiting bees are looking for food sources. There are two types of bees. Scout bees randomly search the environment around the hive to find new food sources. Follower bees wait in the hive and pass Harness the information shared by bees to find food sources.

Preferably, the standby bees in the artificial bee colony algorithm are scout bees, and then start to spontaneously look for food sources around the hive due to internal motivation or external reasons, and the standby bees observe the dance in the dance area as follower bees, and are thus detected. Recruited to be bee pickers.

Preferably, the urban ecological security spatial coupling coordination degree of the objective function represents the spatial coupling between the urban system and the ecological environment, and the relationship between the urban system and the ecological environment is relatively strong in urbanized and semi-urbanized areas, and urban ecological The safety comprehensive evaluation factor represents the spatial suitability evaluation of urban ecological security, and the objective function is established as follows;

F _NS (i, j)＝w _warer (1-d _{min warer} (i, j)+wslope(i, j)+w _rown (1-d min rown(i, j)(1-dmin road)

In the formula, it represents the comprehensive impact factors of nature and society, the shortest distance to important water sources, the distance to the town center, the distance to the main road, and the slope,

In the formula, represents the coupling coordination degree of ecological security space, and the calculation of the coupling coordination degree is given by Equation 4, which is calculated by using urban characteristics and ecological suitability factors;

When Au(i,j)≥Eco(i,j), the objective function is as follows:

In the formula, F is the objective function, W and W default to 0.5 for W and W respectively, N represents the population space of the guide bees, and the maximum average value of the guide bee colony is obtained through the objective function.

Preferably, the roles of the bees are divided into leading bees, scouting bees and following bees, and the bee structure type array is defined according to the bee types, and each bee element stores coordinate values, bee income values, bee types, maximum number of guidance, etc. Information, in the basic algorithm, it is generally stipulated that the number of scout bees is 1. In order to facilitate better grid space search, the model stipulates that the division of labor of bees is as follows: leading bees account for 50% of the total, and scout bees account for 20% of the total. %, and the rest are follower bees.

Preferably, the role and position of each bee is determined by establishing a taboo array and a role taboo matrix in the design of the model structure, and different bee roles stipulate different bee action rules, which are effectively realized under the control of the role taboo matrix. The swarm intelligence behavior of the bee colony, the length of the taboo table is determined by the size of the bee population, and a record in the table stores the position and role of a bee, indicating that the position has been occupied by this bee.

Preferably, a method for using the urban land ecological security space detection model based on the artificial bee colony algorithm, is characterized in that: comprising the following steps:

A. Initialize the bee colony population, assign the number of bees with different roles, and construct the bee taboo table and bee role taboo matrix.

B. Initialize the income value of bees, and assign roles according to the ranking of the income value.

C. Number of cycles.

D, the number of bees.

E. Picking bees go to the vicinity of the original grid position to find new honey sources. If the income value is lower than the income value of the original grid position, then return to the original position. When picking bees, the bees will become scout bees again. mode searches raster locations randomly.

F. Update the taboo table of bees, role taboo matrix and bee object array in sequence.

G. According to the grid position selected by the state transition probability formula, adopt the greedy algorithm to select the best grid position, and obtain the income value.

H. Update the taboo table of the following bees, the role taboo matrix and the bee object array in sequence.

I. The scout bees randomly search the grid position and obtain the income value.

J. Update the taboo table of scout bees, role taboo matrix and bee object array in sequence.

K. If there is a total amount of bees, go to the fourth step, otherwise go to the twelfth step.

L. Sorting according to the size of the bee's income value, and assigning the role of bees.

M. Calculate the current objective function value, and update the bee shared information pool.

N. If the end condition is met, that is, the number of cycles, just after the end of the cycle, and output the result of bee optimization in geospatial space, otherwise, jump to the third step to meet the end condition.

Compared with prior art, the beneficial effect of the present invention is:

(1) The objective function converges very quickly. Due to the use of the leading limit mechanism, the function curve oscillates periodically, and the first oscillation is very obvious. This is because when the model iterates to the second time, most of the guiding bees are changed to The behavioral pattern of the scout bees, therefore, causes a noticeable oscillation in the function curve, and as the model continues to run iterations, more bees have assumed the role of guide bees, so the oscillations gradually decrease as the next cycle approaches, The bee colony is easy to fall into the local optimum at the beginning of the iteration, and the model adopts the leading restriction mechanism. Therefore, the bees can quickly break away from the local optimal solution, the leading bees occupy the optimal position, and the follower bees occupy the suboptimal position around the leading bees. Location, the scout bees continuously conduct random searches in the grid space, maintaining the diversity of the bee colony.

Description of drawings

Fig. 1 is a flow chart of model calculation of the present invention.

detailed description

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

Please refer to Fig. 1, the present invention provides a kind of technical scheme: a kind of urban ecological safety space detection model based on artificial bee colony algorithm, including artificial bee colony algorithm, objective function, model structure design, the food in described artificial bee colony algorithm The value of the source depends on many factors, such as the distance from the hive, the abundance of food, the difficulty of obtaining food, etc. For simplicity, the "yield" of the food can be expressed as a single value. When solving the optimization When the question is asked, the food source represents various possible food sources in the spatial range. The bees and the bees are associated with the specific food source. The food source is the food source they are currently collecting. They carry the information of the food source, distance, orientation and food and share this information with other bees. Waiting bees are looking for food sources. There are two types of them. Scout bees randomly search the environment around the hive to find new food sources. Follower bees wait in the hive. And find food sources by mining the information shared by bees.

The worker bees in the artificial bee colony algorithm act as scout bees, and then spontaneously look for food sources around the hive due to internal motivation or external reasons.

The urban ecological security spatial coupling coordination degree of the objective function represents the spatial coupling between the urban system and the ecological environment. The relationship between the urban system and the ecological environment is relatively strong in urbanized and semi-urbanized areas. To evaluate the spatial suitability of urban ecological security, the objective function is established as follows:

F _NS (i, j)＝w _warer (1-d _{min warer} (i, j)+wslope(i, j)+w _rown (1-dmin rown(i, j)(1-dmin road)

In the formula, it represents the comprehensive impact factors of nature and society, the shortest distance to important water sources, the distance to the town center, the distance to the main road, and the slope,

In the formula, represents the coupling coordination degree of ecological security space, and the calculation of the coupling coordination degree is given by Equation 4, which is calculated by using urban characteristics and ecological suitability factors;

When Au(i,j)≥Eco(i,j), the objective function is as follows:

In the formula, F is the objective function, W and W default to 0.5 for W and W respectively, N represents the population space of the guide bees, and the maximum average value of the guide bee colony is obtained through the objective function.

The roles of bees are divided into leading bees, scouting bees and following bees. According to the type of bees, the array of bee structure types is defined. In each bee element, information such as coordinate values, bee income values, bee types, and maximum number of guidance are stored respectively. In the basic algorithm In this model, the number of scout bees is generally specified as 1. In order to facilitate a better grid space search, the model stipulates that the division of bees is as follows: leading bees account for 50% of the total, scout bees account for 20% of the total, and the rest are follower bees. bee.

In the design of the model structure, the role and position of each bee is determined by establishing a taboo array and a role taboo matrix. Different bee roles stipulate different bee action rules. Under the control of the role taboo matrix, the group of bee colonies is effectively realized. For intelligent behavior, the length of the taboo table is determined by the size of the bee population. A record in the table stores the position and role of a bee, indicating that the position has been occupied by this bee.

A method for using an urban land ecological security space detection model based on an artificial bee colony algorithm, characterized in that it includes the following steps:

A. Initialize the bee colony population, assign the number of bees with different roles, and construct the bee taboo table and bee role taboo matrix.

B. Initialize the income value of bees, and assign roles according to the ranking of the income value.

C. Number of cycles.

D, the number of bees.

E. Picking bees go to the vicinity of the original grid position to find new honey sources. If the income value is lower than the income value of the original grid position, then return to the original position. When picking bees, the bees will become scout bees again. mode searches raster locations randomly.

F. Update the taboo table of bees, role taboo matrix and bee object array in sequence.

G. According to the grid position selected by the state transition probability formula, adopt the greedy algorithm to select the best grid position, and obtain the income value.

H. Update the taboo table of the following bees, the role taboo matrix and the bee object array in sequence.

I. The scout bees randomly search the grid position and obtain the income value.

J. Update the taboo table of scout bees, role taboo matrix and bee object array in sequence.

K. If there is a total amount of bees, go to the fourth step, otherwise go to the twelfth step.

L. Sorting according to the size of the bee's income value, and assigning the role of bees.

M. Calculate the current objective function value, and update the bee shared information pool.

N. If the end condition is met, that is, the number of cycles, just after the end of the cycle, and output the result of bee optimization in geospatial space, otherwise, jump to the third step to meet the end condition.

Compared with prior art, the beneficial effect of the present invention is:

(1) The objective function converges very quickly. Due to the use of the leading limit mechanism, the function curve oscillates periodically, and the first oscillation is very obvious. This is because when the model iterates to the second time, most of the guiding bees are changed to The behavioral pattern of the scout bees, therefore, causes a noticeable oscillation in the function curve, and as the model continues to run iterations, more bees have assumed the role of guide bees, so the oscillations gradually decrease as the next cycle approaches, The bee colony is easy to fall into the local optimum at the beginning of the iteration, and the model adopts the leading restriction mechanism. Therefore, the bees can quickly break away from the local optimal solution, the leading bees occupy the optimal position, and the follower bees occupy the suboptimal position around the leading bees. Location, the scout bees continuously conduct random searches in the grid space, maintaining the diversity of the bee colony.

Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications and substitutions can be made to these embodiments without departing from the principle and spirit of the present invention. and modifications, the scope of the invention is defined by the appended claims and their equivalents.

Claims (6)

1. a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm, including artificial bee colony algorithm, mesh Scalar functions, model structure design, it is characterised in that：The value of food source in the artificial bee colony algorithm depend on it is many because Element, from a distance from honeycomb, the abundance of food, the complexity that food is obtained etc., for the sake of simplicity, " the income degree " of the food A single numerical value can be expressed as, when solving optimization problem, the various possible food sources of food source representation space scope, Gathering honey honeybee links together with specific food source, and food source is exactly the food source that they are currently gathering, and they carry food The information in source, " the income degree " of distance, orientation and food, and share this information with other honeybees, treat that worker bee treats worker bee just In search of food source, they have the environment around two types, investigation honeybee random search honeycomb to find new food source, follow honeybee Waited in honeycomb, and the information shared by gathering honey honeybee is come search of food source.

2. a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm according to claim 1, It is characterized in that：Worker bee is treated as investigation honeybee in the artificial bee colony algorithm, then due to internal motivation or transient cause Start the spontaneous search of food source around honeycomb, it is described to treat worker bee as honeybee is followed in the observation dancing of dancing area, so as to be recruited As gathering honey honeybee.

3. a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm according to claim 1, It is characterized in that：The urban ecological security Space Coupling degrees of coordination of the object function represents the sky of city system and ecological environment Between coupling, the city system, the interactively of ecological environment in urbanization and peri urban performance than stronger, The factor representation of urban ecological security overall merit sets up object function as follows to the space suitability evaluation of urban ecological security：

F_NS(i, j)=w_warer(1-d_{min warer}(i, j)+wslope (i, j)+w_rown(1-d min rown (i, j) (1-d min road)

In formula, F (i, j) represent nature and social synthesis's factor of influence, away from important water source minimum distance, away from Town Center away from From, away from a distance from main roads, the gradient,

In formula, ecological safety Space Coupling degrees of coordination is represented, coupling degree is calculated and provided by formula 4, utilizes urban characteristic and life The suitable sex factor of state, which is calculated, to be obtained；

As Au (i, j) >=Eco (i, j), object function is as follows：

In formula, F is object function, and W, W are defaulted as W, W and respectively take 0.5, N to represent to guide the population space of honeybee, obtained by object function The maximum average value of bee colony must be guided.

4. a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm according to claim 1, It is characterized in that：The role of the honeybee, which is divided into, to be led honeybee, investigate honeybee and follows honeybee, and honeybee structure class is defined according to honeybee species In type array, each honeybee element, stored coordinate values, honeybee financial value, honeybee species, maximum guiding number of times etc. are believed respectively Breath, in rudimentary algorithm, the quantity of general provision investigation honeybee is 1, is searched for for ease of more preferable grid space, model regulation honeybee Division of labor ratio it is as follows：Lead that honeybee is total amount 50%, investigation honeybee is the 20% of total amount, remaining as following honeybee.

5. a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm according to claim 1, It is characterized in that：Pass through in model structure design and set up taboo array and role avoids matrix, it is determined that every honeybee Role and position, different honeybee role's defines different honeybee rule of ac-tions, under the control that role avoids matrix, effectively The swarm intelligence behavior of bee colony is realized, the length of taboo list is determined by honeybee populations size, in table a record storage one The position of branch honeybee and role, represent that the position has become occupied by this branch honeybee.

6. realize a kind of urban land ecological safety space exploration model based on artificial bee colony algorithm described in claim 1 Application method, it is characterised in that：Comprise the following steps：

A, initialization bee colony population, distribute different role bee numbers, construction honeybee taboo list and honeybee role taboo matrix.

B, initialization honeybee financial value, role is distributed according to financial value size ranking.

C, cycle-index.

D, honeybee number.

E, gathering honey honeybee go to the new nectar source of neighbouring searching of former grid positions, if financial value is less than the financial value of former grid positions, that Original position is returned to, when gathering honey honeybee, then gathering honey honeybee becomes to investigate the pattern random search grid positions of honeybee again.

F, the taboo list that gathering honey honeybee is updated successively, role's taboo matrix and honeybee object array.

G, the grid positions selected according to state transition probability formula, optimum lattice position is selected using using greedy algorithm, and Obtain financial value.

H, successively renewal follow the taboo list of honeybee, role's taboo matrix and honeybee object array.

I, investigation honeybee random search grid positions, and obtain financial value.

J, the taboo list that investigation honeybee is updated successively, role's taboo matrix and honeybee object array.

If K, honeybee total amount, jump to the 4th step, the 12nd step is otherwise performed.

L, it is ranked up according to the financial value size of honeybee, distributes honeybee role.

M, calculating current goal functional value, and update the shared information pool of honeybee.

If N, meeting termination condition, i.e. cycle-index, just circulation terminates, and exports honeybee in geographical space optimum results, no Then, jump to the 3rd step and be met termination condition.