A hybrid algorithm based on ABC algorithm and its application in Hpcf pump fuzzy control

The Artificial Bee Colony (ABC) algorithm excels in solving optimization challenges but faces slow convergence, low precision, and local optima issues.To mitigate these limitations, a novel hybrid optimization algorithm, termed the IABC algorithm, was developed to improve the global search capability and speed up convergence. Initially, the employed bee phase adopts a sine law-based dimension-changing strategy to boost search and exploitation capabilities, accelerate convergence, and prevent premature convergence; in the onlooker bee phase, it follows the global optimum, altering its step size—large for broad searches in the early stages and small for precise searches later on. Finally, in the scout bee phase, the hunting strategy from the SWO algorithm (Spider Bee Optimization) is utilized to strengthen global and local search abilities. Experimental conducted on the CEC2017 dataset confirm enhanced strategies' success and performance efficiency. Comparisons with other ABC algorithms and competitive algorithms show that the IABC significantly enhances the performance of the ABC. In nearly all test functions, the IABC attained the superior solution quality, quickest rate of convergence, and most robust performance. Its effectiveness is further proven by successful application in pulsation suppression for the HPCF(high-pressure constant flow) pump pulse systems, indicating its potential for solving diverse optimization problems.

• Publication year

  2024

• Venue

  International Conference on Computer Communication and Network Security

• Publication date

  2024-08-22

• Fields of study

  Computer Science, Engineering

• Identifiers
  DOI 10.1117/12.3038036
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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