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一种基于技术和职业教育培训的新型基于人类的元启发式算法,用于解决优化问题。

A New Human-Based Metaheuristic Algorithm for Solving Optimization Problems Based on Technical and Vocational Education and Training.

作者信息

Hubalovska Marie, Major Stepan

机构信息

Department of Technics, Faculty of Education, University of Hradec Kralove, CZ50003 Hradec Kralove, Czech Republic.

出版信息

Biomimetics (Basel). 2023 Oct 23;8(6):508. doi: 10.3390/biomimetics8060508.

Abstract

In this paper, a new human-based metaheuristic algorithm called Technical and Vocational Education and Training-Based Optimizer (TVETBO) is introduced to solve optimization problems. The fundamental inspiration for TVETBO is taken from the process of teaching work-related skills to applicants in technical and vocational education and training schools. The theory of TVETBO is expressed and mathematically modeled in three phases: (i) theory education, (ii) practical education, and (iii) individual skills development. The performance of TVETBO when solving optimization problems is evaluated on the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that TVETBO, with its high abilities to explore, exploit, and create a balance between exploration and exploitation during the search process, is able to provide effective solutions for the benchmark functions. The results obtained from TVETBO are compared with the performances of twelve well-known metaheuristic algorithms. A comparison of the simulation results and statistical analysis shows that the proposed TVETBO approach provides better results in most of the benchmark functions and provides a superior performance in competition with competitor algorithms. Furthermore, in order to measure the effectiveness of the proposed approach in dealing with real-world applications, TVETBO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite. The simulation results show that TVETBO provides an effective and superior performance when solving constrained optimization problems of real-world applications compared to competitor algorithms.

摘要

本文介绍了一种名为基于技术和职业教育培训的优化器(TVETBO)的新型基于人类的元启发式算法,用于解决优化问题。TVETBO的基本灵感来源于技术和职业教育培训学校向申请者传授与工作相关技能的过程。TVETBO的理论在三个阶段进行表达和数学建模:(i)理论教育,(ii)实践教育,以及(iii)个人技能发展。在CEC 2017测试套件上,针对问题维度等于10、30、50和100的情况,评估了TVETBO在解决优化问题时的性能。优化结果表明,TVETBO在搜索过程中具有很高的探索、利用能力,并能在探索和利用之间取得平衡,能够为基准函数提供有效的解决方案。将TVETBO得到的结果与十二种著名的元启发式算法的性能进行了比较。仿真结果和统计分析表明,所提出的TVETBO方法在大多数基准函数中提供了更好的结果,并且在与竞争算法的竞争中表现优异。此外,为了衡量所提出的方法在处理实际应用中的有效性,在CEC 2011测试套件中的二十二个约束优化问题上实现了TVETBO。仿真结果表明,与竞争算法相比,TVETBO在解决实际应用中的约束优化问题时提供了有效且卓越的性能。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1837/10604091/58b2f917fe72/biomimetics-08-00508-g001a.jpg

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