Abstract
Most of the reported metaheuristic methods need the control parameters except the essential population size and terminal condition. When these methods are used for solving an unknown problem, how to set the most suitable values for their control parameters to achieve the optimal solution is a great challenge. Group teaching optimization algorithm (GTOA) is a newly presented metaheuristic method, whose remarkable feature is that it only relies on the essential population size and terminal condition for optimization. However, GTOA may get trapped in the local optimal solutions for solving complex optimization problems due to the lack of communication between outstanding group and average group. In order to improve the performance of GTOA, this paper proposes a new variant of GTOA, namely group teaching optimization algorithm with information sharing (ISGTOA). Like GTOA, ISGTOA doesn’t introduce any other control parameters, which enhances the communication between outstanding group and average group by reusing the individuals in the built two archives. The performance of ISGTOA is investigated by CEC 2014 test suite, CEC 2015 test suite, and four challenging constrained engineering design problems. Experimental results prove the superiority of ISGTOA for expensive optimization problems with multimodal properties by comparing with GTOA and other powerful methods. The source codes of the proposed ISGTOA can be found in https://ww2.mathworks.cn/matlabcentral/fileexchange/98629-the-source-code-of-isgtoa and https://github.com/jsuzyy/The-source-code-of-ISGTOA-for-global-optimization.
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Abbreviations
- \(\delta\) :
-
Standard deviation
- \(N\) :
-
Population size
- \(D\) :
-
Dimension of the solved problem
- \(F\) :
-
Scale factor
- \(F_{\max }\) :
-
The maximum number of function evaluations
- \(F_{{{\text{current}}}}\) :
-
The current number of function evaluations
- \({\mathbf{l}}\) :
-
The lower bound of variables
- \({\mathbf{u}}\) :
-
The upper bound of variables
- \({\mathbf{X}}^{t}\) :
-
Population at time \(t\)
- \({\mathbf{X}}_{{\text{o}}}^{t}\) :
-
Outstanding group at time \(t\)
- \({\mathbf{X}}_{{\text{a}}}^{t}\) :
-
Average group at time \(t\)
- \({\mathbf{T}}^{t}\) :
-
Teacher at time \(t\)
- \({\mathbf{M}}^{t}\) :
-
Mean knowledge of outstanding group at time \(t\)
- \({\mathbf{x}}_{{{\text{first}}}}^{t}\) :
-
The first best individual of population at time \(t\)
- \({\mathbf{x}}_{{{\text{second}}}}^{t}\) :
-
The second best individual of population at time \(t\)
- \({\mathbf{x}}_{{{\text{third}}}}^{t}\) :
-
The third best individual of population at time \(t\)
- \({\mathbf{G}}^{t}\) :
-
The optimal solution at time \(t\)
- \(M_{{\text{o,cost}}}^{t}\) :
-
The mean fitness value of outstanding group
- \(M_{{\text{a,cost}}}^{t}\) :
-
The mean fitness value of average group
- \({\mathbf{A}}_{i}^{t}\) :
-
The ith individual in archive A at time \(t\)
- \({\mathbf{b}}_{{\text{m}}}^{t}\) :
-
The solution selected from archive B at time \(t\)
- \(L_{{\text{B}}}\) :
-
The length of archive B
- MEAN:
-
Mean value
- STD:
-
Standard deviation
- \(a,b,c,d,e,g\) :
-
Random numbers between 0 and 1 subject to uniform distribution
- \(\eta ,\xi\) :
-
Random numbers subject to standard normal distribution
- JAYA:
-
Jaya algorithm
- TLBO:
-
Teaching–learning optimization algorithm
- TSO:
-
Transient search algorithm
- SOA:
-
Seaguall optimization algorithm
- JSO:
-
Jellyfish search optimizer
- AOA:
-
Arithmetic optimization algorithm
- GTOA:
-
Group teaching optimization algorithm
- ISGTOA:
-
Group teaching optimization algorithm with information sharing
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Appendices
Appendix A: The download links of the applied algorithms
-
1.
ISGTOA: https://ww2.mathworks.cn/matlabcentral/fileexchange/98629-the-source-code-of-isgtoa.
- 2.
- 3.
- 4.
- 5.
- 6.
-
7.
TLBO: https://yarpiz.com/83/ypea111-teaching-learning-based-optimization.
Appendix B: Mathematical formulas of engineering design problems
Appendix B.1: Pressure vessel design problem
where \( 0 \le x_{i} \le 100, \, i = 1,2; \, 10 \le x_{i} \le 200, \, i = 3,4.\)
Appendix B.2: Tension/compression spring design problem
where \( 0.05 \le x_{1} \le 2, \, 0.25 \le x_{2} \le 1.30, \, 2.00 \le x_{3} \le 15.00.\)
Appendix B.3: Car side impact design problem
where \( 0.5 \le x_{i} \le 1.5, \, i = 1,2,3,4,5,6,7; \, 0.192 \le x_{i} \le 0.345, \, i = 8,9; \, - 30 \le x_{i} \le 30, \, i = 10,11. \)
Appendix B.4: Rolling element bearing design problem
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Zhang, Y., Chi, A. Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization. J Intell Manuf 34, 1547–1571 (2023). https://doi.org/10.1007/s10845-021-01872-2
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DOI: https://doi.org/10.1007/s10845-021-01872-2