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Self-adaptive parallel community old-age service point site selection optimization method and self-adaptive parallel community old-age service point site selection optimization system

An optimization method and service point technology, applied in data processing applications, instruments, forecasting, etc., can solve problems such as manual site selection methods that are difficult to achieve scientific layout, so as to prevent falling into local optimum, reduce time complexity, and ensure population The effect of diversity

Pending Publication Date: 2022-04-15
NORTH CHINA UNIVERSITY OF TECHNOLOGY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the manual site selection method is difficult to achieve scientific layout and effective optimization. Therefore, it is necessary to use computer technology to scientifically select sites for the layout of community elderly care service points

Method used

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  • Self-adaptive parallel community old-age service point site selection optimization method and self-adaptive parallel community old-age service point site selection optimization system
  • Self-adaptive parallel community old-age service point site selection optimization method and self-adaptive parallel community old-age service point site selection optimization system
  • Self-adaptive parallel community old-age service point site selection optimization method and self-adaptive parallel community old-age service point site selection optimization system

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Experimental program
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Effect test

Embodiment 1

[0051] This embodiment provides a method for site selection optimization of community elderly care service points using an arithmetic optimization algorithm, which specifically includes the following steps:

[0052] With the goal of the shortest travel distance for the elderly, a multi-constraint community elderly care service site selection optimization model is constructed;

[0053] Use the arithmetic optimization algorithm to solve the model and get the site selection result;

[0054] The arithmetic optimization algorithm is used to solve the model, and the initial optimal site selection result is obtained.

[0055]This embodiment builds a model for site selection optimization of community elderly care service points. Community elderly care service points need to meet the following conditions:

[0056] The construction address of the community elderly care service point is as close as possible to the community with a large population density of the elderly;

[0057] The o...

Embodiment 2

[0071] On the basis of Embodiment 1, this embodiment additionally provides an arithmetic optimization algorithm, specifically as follows:

[0072] Randomly select N candidate points of service points to be built around the desired service cell, and the coordinates of all candidate points are expanded into an N×N matrix, specifically expressed as follows:

[0073]

[0074] When this embodiment uses the arithmetic optimization algorithm to solve the multi-constraint community elderly care service point site selection optimization model, the conversion function of the exploration stage and the development stage in the establishment of the algorithm is as follows:

[0075]

[0076] Among them, iter is the current number of iterations, Max_iter is the maximum number of iterations, and Max and Min are the maximum and minimum values ​​of the acceleration function.

[0077] When this embodiment uses the arithmetic optimization algorithm to solve the multi-constraint community el...

Embodiment 3

[0085] On the basis of Embodiment 1, this embodiment additionally provides an adaptive parallel arithmetic optimization algorithm, specifically as follows:

[0086] The self-adaptive parameter adjustment in this embodiment changes the sensitive parameter α by introducing self-adaptive adjustment, and the specific formula is as follows:

[0087]

[0088] α(iter)=1-α'(iter)+ε (10)

[0089] Among them, α max and alpha min are the maximum and minimum values ​​of sensitive parameters, f, f min , f max , f avg They are the fitness value, the minimum fitness value, and the maximum fitness value. In this embodiment, when using an adaptive parallel arithmetic optimization algorithm to solve the multi-constraint community elderly care service point location optimization model, multiple groups of parallel communication strategies are introduced. At the beginning, all candidate points are divided into multiple groups, and each group is updated iteratively according to different co...

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Abstract

The invention relates to the technical field of site selection optimization, in particular to an adaptive parallel community old-age service point site selection optimization method and system, and the method comprises the following steps: building a multi-constraint community old-age service point site selection optimization model with the shortest travel distance of old people as a target; solving the model by using an arithmetic optimization algorithm to obtain a site selection result; and solving the model by using an adaptive parallel arithmetic optimization algorithm to obtain a further optimized site selection result. The method has the advantages that for the problems that an arithmetic optimization algorithm is low in convergence speed and prone to falling into local optimum and the like, improvement is carried out through a parallel mechanism of self-adaptive parameter change and multiple sets of multiple strategies. According to the improved algorithm, the space complexity is increased to a certain extent, but the time complexity is reduced, the capability of jumping out of local optimum of the algorithm is improved through the method of adaptively changing parameters, the population diversity is ensured to a certain extent through a multi-group multi-strategy parallel mechanism, and the algorithm is suitable for being popularized and applied. The convergence speed of the algorithm is increased; and the optimization precision of the algorithm is improved.

Description

technical field [0001] The invention relates to the technical field of site selection optimization, in particular to an adaptive and parallel site selection optimization method and system for community elderly care service points. Background technique [0002] Since the 1990s, China's aging process has accelerated. The results of the seventh census show that the proportion of the population aged 60 and over in China has increased by 5.44 percentage points compared with the sixth census. The increase in the elderly population will inevitably lead to an increase in demand for elderly care services, especially community-based elderly care, which is welcomed by the elderly and their families due to its convenience and community integration. Although governments at all levels and social forces have done a lot of work in building community elderly care service points and achieved certain results, there is still an imbalance between the supply and demand of community elderly care ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/26
Inventor 王若宾王伟锋耿芳东徐琳
Owner NORTH CHINA UNIVERSITY OF TECHNOLOGY
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