Improved hybrid chimpanzee optimization algorithm

An optimization algorithm, chimpanzee technology, applied in computing, computing models, manufacturing computing systems, etc., to achieve the effect of increasing diversity and improving possibilities

Pending Publication Date: 2022-06-28
GUIZHOU UNIV
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  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Benefits of technology

This new method uses better points or sets from existing methods that help improve individual differences between different populations while also balancing their searching power over developing capabilities. It suggests proposing a system where certain parts are more likely than others to be optimized together instead of just one part being searched separately. Additionally, it introduces a technique called Crawl-Walker's Algorithm(CWA), which helps optimize each person individually rather than collectively across all groups. Overall, this technology provides technical benefits such as increased efficiency and effectiveness in finding solutions through various techniques like genetic algorithms.

Problems solved by technology

Cloud manufacture involves planning tasks by assigning resources between workers who perform their assigned task or groups of paces during the execution phase of the project's overall schedule. While it may seem like doing everything together correctly without any errors, if certain parts have been done incorrectly due to factors such as delays caused by other activities within the factory (such as transportation) or human error, then these incorrect results could lead to disruptions in productivity. To solve this issue, the technical solution described in the patented text proposes developing a system called Job Shop Scheduler(WS).

Method used

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  • Improved hybrid chimpanzee optimization algorithm
  • Improved hybrid chimpanzee optimization algorithm
  • Improved hybrid chimpanzee optimization algorithm

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Embodiment 1

[0040] Example 1: as Figure 1-6 As shown, an improved hybrid chimpanzee optimization algorithm, the method is: the chimpanzee algorithm uses the good point set to initialize the population, the nonlinear convergence factor of the tangent function is used to balance the global search ability and local development ability of the algorithm, and the position updates A somersault foraging strategy was employed. The specific steps of the improved hybrid chimpanzee optimization algorithm are as follows:

[0041] S1. Initialize related parameters, including the size of the population N and the maximum number of iterations T max , space dimension dim, search for boundaries lb, ub, and set relevant parameters;

[0042] S2. Use the good point set to initialize the population of the chimpanzee algorithm;

[0043] The initial chimpanzee algorithm is random in the generation of the initial population individuals, so that the diversity of the population cannot be guaranteed. Using the best

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Abstract

The invention discloses an improved hybrid chimpanzee optimization algorithm, and the method comprises the steps: employing a chimpanzee algorithm to carry out the initialization of a population through employing a good point set, balancing the global search capability and local development capability of the algorithm based on a nonlinear convergence factor of a tangent function, and employing a flip foraging strategy at a position updating part. According to the improved hybrid chimpanzee optimization algorithm (DFSChOA) provided by the invention, population initialization is performed by using a good point set, the diversity of population individuals is improved, a foundation is laid for global optimization, and a given convergence factor based on a tangent function factor is used for balancing the search capability and the development capability of the algorithm; and a prying foraging strategy is provided at a position-changing position, so that the possibility that the algorithm falls into local optimum is improved.

Description

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Claims

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Application Information

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Owner GUIZHOU UNIV
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