CN113221051A - Web service combination optimization method based on improved crow search algorithm - Google Patents

Web service combination optimization method based on improved crow search algorithm Download PDF

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CN113221051A
CN113221051A CN202110558924.3A CN202110558924A CN113221051A CN 113221051 A CN113221051 A CN 113221051A CN 202110558924 A CN202110558924 A CN 202110558924A CN 113221051 A CN113221051 A CN 113221051A
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crow
optimal position
new
calculating
service combination
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李子文
陈世展
冯志勇
薛霄
吴洪越
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Tianjin University
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/958—Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/957—Browsing optimisation, e.g. caching or content distillation
    • G06F16/9577—Optimising the visualization of content, e.g. distillation of HTML documents
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Abstract

The invention relates to a Web service combination optimization method based on an improved crow search algorithm, which comprises the following steps: determining a service combination space and an initial space position of the crow; taking initial space position of crow as initial memory M of crowi(0) (ii) a Evaluating a service combination path fitness value corresponding to each crow position; adaptively reducing the perception probability in the iteration process; generating a new location for each individual; calculating the relative change rate of the optimal position dimension component; sorting the relative change rates; calculating an interval break variable; calculating new upper and lower bounds; calculating a new value of the w-dimension component of the current optimal position; evaluating the fitness of the new position after the mutation, and reserving a more optimal position compared with the current optimal position; evaluating the fitness value of the new location; updating memory of the crow; enter the next generation until termination is reachedAnd (4) taking the optimal position of the fitness value as an optimal service combination path.

Description

Web service combination optimization method based on improved crow search algorithm
Technical Field
The invention belongs to the field of intelligent computing application, relates to application of a swarm intelligence optimization algorithm in Web, particularly relates to a problem of optimizing Web service combination by the technology, and particularly relates to a Web service combination optimization method based on an improved crow search algorithm.
Background
Under the rapid development of the internet, many enterprises aim to integrate different applications on heterogeneous platforms and fully utilize the infrastructure of the internet to provide a complete service for users. Service oriented computing (SOA) is a solution to building, invoking, maintaining, and enhancing distributed applications, and has been considered as the next generation framework for building agile distributed applications over the Internet. In an SOA, applications are provided in the form of Web services, and once a Web service is available, research and development are rapidly shifted to composite services to complete a combination of tasks, i.e., a combination of services. The generation of composite services that perform better, are less costly, and achieve higher customer satisfaction has been the focus of research for over a decade.
The service composition goal is to search for the best set of services for creating a new service and get the best quality of service (QoS) under the constraints of the user or service designer. Web services provide the basic unit for building such systems, representing a particular business activity or function. Once the services are exposed and available for other services and resources on the network, they can build more complex structures and interactions, providing new value-added aggregated services. The Web service combination is not simple random combination, and at the moment, proper services are selected from a large number of services with similar functions and different qualities distributed on different platforms, and then the services are combined according to certain business logic and sequence to construct a telescopic loosely-coupled combined service. As the size and complexity of Web services increase, QoS has become a key challenge for SOC performance. Therefore, a proper QoS-based Web service combination evaluation model is selected, an algorithm with better performance is provided to realize the task request of the user, the wide application of Web service is facilitated, the requirement of the user is more effectively met, and the continuous and healthy development of the Internet is finally promoted.
The crow search algorithm is a new group intelligent optimization algorithm, is simple and easy to understand due to few parameters and high in convergence rate, and is widely applied to different fields of medicine, energy, feature selection, image processing and the like. However, the algorithm has the problems of low solving precision, poor global searching capability, fixed parameters and the like in most group intelligent optimization algorithms.
Disclosure of Invention
In order to solve the problem of Web service combination, the invention improves the deficiency of the crow search algorithm, provides an improved crow search algorithm called MACSA, and provides a Web service combination optimization method based on the improved crow search algorithm. In order to achieve the purpose, the invention adopts the technical scheme that:
a Web service combination optimization method based on an improved crow search algorithm is characterized by comprising the following steps:
step 1: in the service composition space [1, n ]m]Selecting k points as initial space position X of crowi(0) N is the number of candidate services, m is the number of tasks, and k is the number of populations;
step 2: taking initial space position of crow as initial memory M of crowi(0)；
And step 3: evaluating a service combination path fitness value corresponding to each crow position;
and 4, step 4: adaptively reducing the perception probability in an iterative process, wherein the formula of the adaptive perception probability is as follows:
Figure BDA0003078362920000021
where AP is the perceptual probability, APmaxIs the maximum perceptual probability, APminIs the minimum perceptual probability, iterate is the current iteration number, tmaxIs the maximum number of iterations;
and 5: generating a new location for each individual;
step 6: calculating the relative change rate of the optimal position dimension component; as shown in formula:
Figure BDA0003078362920000022
in the formula, betaiFor the relative change rate, x, of the ith dimension of the optimal positions of two adjacent generationsiFor the current generation of the best individual dimension i component, miThe ith dimension component of the previous generation optimal position;
and 7: sorting the relative change rates;
and 8: calculating the interval mutation quantity, wherein the formula is as follows:
Δx＝mu*[UB(w)-LB(w)]
in the formula, Δ x is an interval mutation, mu is a variation factor, ub (w) is an upper bound of the w-dimension component of the current optimal position, and lb (w) is a lower bound of the w-dimension component of the current optimal position;
and step 9: calculating new upper and lower bounds, and the formula is as follows:
ub＝XCurrent(w)+Δx
lb＝XCurrent(w)-Δx
wherein ub is a new upper bound of the w-dimension component, lb is a new lower bound of the w-dimension component, and XCurrent (w) is a value of the w-dimension component of the current optimal position;
step 10: calculating a new value of the w-dimension component of the current optimal position; the following were used:
XMutation(w)＝random*(ub-lb)+1b
wherein, XMutation (w) is the new value of the w-dimension component of the current optimal position after being changed;
step 11: evaluating the fitness of the new position after the mutation, and reserving a more optimal position compared with the current optimal position;
step 12: evaluating the fitness value of the new location;
step 13: updating memory of the crow;
step 14: and entering the next generation until a termination condition is reached, and taking the optimal position of the fitness value as an optimal service combination path.
Compared with the prior art, the invention has the beneficial effects that:
1. the Web service combination optimization method can find a high-quality available solution within an acceptable time, and has better effectiveness in solving the Web service combination optimization problem.
2. The Web service combination optimization method can obtain shorter execution time than the Web service combination optimization method using a particle swarm algorithm and a differential evolution algorithm.
Drawings
FIG. 1 is a flowchart of an original crow searching algorithm according to an embodiment of the present invention.
FIG. 2 is a flow chart of an embodiment of the present invention.
Fig. 3 run time comparison.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings.
Referring to fig. 1 and 2, the method for optimizing a Web service combination based on an improved crow search algorithm provided by the invention is characterized by comprising the following steps:
step 1: in the service composition space [1, n ]m]Selecting k points as initial space position X of crowi(0) N is the number of candidate services, m is the number of tasks, and k is the number of populations.
Step 2: taking initial space position of crow as initial memory M of crowi(0)。
And step 3: and evaluating the service combination path adaptability value corresponding to each crow position.
And 4, step 4: adaptively reducing the perception probability in an iterative process, wherein the formula of the adaptive perception probability is as follows:
Figure BDA0003078362920000031
where AP is the perceptual probability, APmaxIs the maximum perceptual probability, APminIs the minimum perceptual probability, iterate is the current iteration number, tmaxIs the maximum number of iterations.
And 5: a new location for each individual is generated.
Step 6: and calculating the relative change rate of the optimal position dimension component. As shown in formula:
Figure BDA0003078362920000032
in the formula, betaiFor the relative change rate, x, of the ith dimension of the optimal positions of two adjacent generationsiFor the current generation of the best individual dimension i component, miThe ith dimension component of the optimal position of the previous generation.
And 7: the relative rates of change are ordered.
And 8: calculating the interval mutation quantity, wherein the formula is as follows:
Δx＝mu*[UB(w)-LB(w)]
in the formula, Δ x is an interval mutation, mu is a variation factor, ub (w) is an upper bound of the w-dimension component of the current optimal position, and lb (w) is a lower bound of the w-dimension component of the current optimal position.
And step 9: calculating new upper and lower bounds, and the formula is as follows:
ub＝XCurrent(w)+Δx
lb＝XCurrent(w)-Δx
in the formula, ub is the new upper bound of the w-th dimension component, lb is the new lower bound of the w-th dimension component, and XCurrent (w) is the value of the w-th dimension component of the current optimal position.
Step 10: and calculating a new value of the w-th dimension component of the current optimal position. The following were used:
XMutation(w)＝random*(ub-lb)+lb
wherein, XMutation (w) is the new value of the w-th dimension component of the current optimal position after being changed.
Step 11: and evaluating the fitness of the new position after mutation, and reserving a more optimal position compared with the current optimal position.
Step 12: the fitness value of the new location is evaluated.
Step 13: updating memory of the crow;
step 14: and entering the next generation until a termination condition is reached, and taking the optimal position of the fitness value as an optimal service combination path.
The invention uses the improved crow search algorithm to optimally solve the Web service combination problem, thereby quickly searching the optimal Web service combination for a given group of user requirements.
Furthermore, the convergence precision and the convergence speed are verified on the basis of a reference test function for improving the crow search algorithm, and the degree and the speed are smaller and better. The unimodal function results are shown in table 1, and the multimodal function results are shown in table 2, which show the optimal mean and convergence times (in parentheses, the convergence times) of the different algorithms when they were optimized under the benchmark test function. The results show that the improved crow search algorithm has better solving performance in a simulation experiment.
TABLE 1 unimodal function test results
Figure BDA0003078362920000051
TABLE 2 multimodal function test results
Figure BDA0003078362920000052
Secondly, the optimization performance of the Web service combination of the improved crow search algorithm (MACSA) under different candidate services is researched in the practical process. The results of the optimization accuracy are shown in table 1, the smaller the result is, the higher the accuracy is, and the run time result is shown in fig. 1. The above results show that the present invention has certain advantages over other methods in the problem of Web service composition optimization.
TABLE 1 optimization accuracy
10 20 30 40 50 60 70 80 90 100
PSO 0.15 0.61 1.07 1.2 0.78 1.1 0.73 1.3 0.68 0.82
DE 0.75 0.65 1.02 0.99 0.91 0.93 0.84 0.93 0.73 0.86
CSA 0.82 0.52 0.99 0.91 0.82 0.85 0.78 0.99 0.76 0.74
MACSA 0.14 0.5 0.95 0.85 0.64 0.8 0.65 0.76 0.56 0.65
It should be understood that the above description of the preferred embodiments is given for clarity and not for any purpose of limitation, and that various changes, substitutions and alterations can be made herein without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (1)

1. A Web service combination optimization method based on an improved crow search algorithm comprises the following steps:
step 1: in the service composition space [1, n ]m]Selecting k points as initial space position X of crowi(0) N is the number of candidate services, m is the number of tasks, and k is the number of populations.
Step 2: taking initial space position of crow as initial memory M of crowi(0)；
And step 3: evaluating a service combination path fitness value corresponding to each crow position;
and 4, step 4: adaptively reducing the perception probability in an iterative process, wherein the formula of the adaptive perception probability is as follows:
Figure FDA0003078362910000011
where AP is the perceptual probability, APmaxIs the maximum perceptual probability, APminIs the minimum perceptual probability, iterate is the current iteration number, tmaxIs the maximum number of iterations;
and 5: generating a new location for each individual;
step 6: calculating the relative change rate of the optimal position dimension component according to the following formula:
Figure FDA0003078362910000012
in the formula, betaiFor the relative change rate, x, of the ith dimension of the optimal positions of two adjacent generationsiFor the current generation of the best individual dimension i component, miThe ith dimension component of the previous generation optimal position;
and 7: sorting the relative change rates;
and 8: calculating the interval mutation quantity, wherein the formula is as follows:
Δx＝mu*[UB(w)-LB(w)]
in the formula, Δ x is an interval mutation, mu is a variation factor, ub (w) is an upper bound of the w-dimension component of the current optimal position, and lb (w) is a lower bound of the w-dimension component of the current optimal position;
and step 9: calculating new upper and lower bounds, and the formula is as follows:
ub＝XCurrent(w)+Δx
lb＝XCurrent(w)-Δx
wherein ub is a new upper bound of the w-dimension component, lb is a new lower bound of the w-dimension component, and XCurrent (w) is a value of the w-dimension component of the current optimal position;
step 10: calculating a new value of the w-dimension component of the current optimal position:
XMutation(w)＝random*(ub-lb)+lb
wherein, XMutation (w) is the new value of the w-dimension component of the current optimal position after being changed;
step 11: evaluating the fitness of the new position after the mutation, and reserving a more optimal position compared with the current optimal position;
step 12: evaluating the fitness value of the new location;
step 13: updating memory of the crow;
step 14: and entering the next generation until a termination condition is reached, and taking the optimal position of the fitness value as an optimal service combination path.
CN202110558924.3A 2021-05-21 2021-05-21 Web service combination optimization method based on improved crow search algorithm Pending CN113221051A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114027974A (en) * 2021-09-15 2022-02-11 苏州中科华影健康科技有限公司 Multi-focus endoscope path planning method, device and terminal
CN116911903A (en) * 2023-09-12 2023-10-20 福建福诺移动通信技术有限公司 Method and device for analyzing automatic parameter adjustment of user model

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114027974A (en) * 2021-09-15 2022-02-11 苏州中科华影健康科技有限公司 Multi-focus endoscope path planning method, device and terminal
CN114027974B (en) * 2021-09-15 2023-10-13 苏州中科华影健康科技有限公司 Endoscope path planning method, device and terminal for multiple lesion sites
CN116911903A (en) * 2023-09-12 2023-10-20 福建福诺移动通信技术有限公司 Method and device for analyzing automatic parameter adjustment of user model
CN116911903B (en) * 2023-09-12 2023-12-22 福建福诺移动通信技术有限公司 Method and device for analyzing automatic parameter adjustment of user model

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Application publication date: 20210806