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This chapter presents a Bees AlgorithmBees algorithm (BA) OptimisationOptimisation Toolkit developed in LabVIEWLabVIEW. The BA is an effective optimisationOptimisation algorithmAlgorithms that mimics the nectar-foraging behaviour of honey bees. LabVIEWLabVIEW is a powerful program for data acquisition and control applications that is very popular in industry. There are tools within the scope of optimisationOptimisation within LabVIEWLabVIEW, but there is no toolkit for the BA. In this chapter, the preparation of the BA in LabVIEWLabVIEW is explained step by step. The toolkit has two parts. OptimisationOptimisation can be performed on standard continuous test functionsTest functions with the first part, and new functions and problems can be defined and solved. It has been observed that the minimum values of complex continuous functions can be reached quickly in experimental studies. With the second part, combinatorial optimisationCombinatorial optimisation can be conducted on travelling salesman problemsTravelling salesman problem (TSPs), and new TSPs can be defined and solved. It has been shown experimentally that minimum-cost solutions can be found in a small number of iterations using this toolkit.

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Development of the Bees Algorithm Toolkit for Optimisation in LabVIEW

  • 章节

  • 发表日期：

    2025年

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  • Murat Sahin,
  • D. T. Pham

摘要

This chapter presents a Bees AlgorithmBees algorithm (BA) OptimisationOptimisation Toolkit developed in LabVIEWLabVIEW. The BA is an effective optimisationOptimisation algorithmAlgorithms that mimics the nectar-foraging behaviour of honey bees. LabVIEWLabVIEW is a powerful program for data acquisition and control applications that is very popular in industry. There are tools within the scope of optimisationOptimisation within LabVIEWLabVIEW, but there is no toolkit for the BA. In this chapter, the preparation of the BA in LabVIEWLabVIEW is explained step by step. The toolkit has two parts. OptimisationOptimisation can be performed on standard continuous test functionsTest functions with the first part, and new functions and problems can be defined and solved. It has been observed that the minimum values of complex continuous functions can be reached quickly in experimental studies. With the second part, combinatorial optimisationCombinatorial optimisation can be conducted on travelling salesman problemsTravelling salesman problem (TSPs), and new TSPs can be defined and solved. It has been shown experimentally that minimum-cost solutions can be found in a small number of iterations using this toolkit.