Gaining-Sharing Knowledge Based Algorithm with Adaptive Parameters Hybrid with IMODE Algorithm for Solving CEC 2021 Benchmark Problems

The initiative to introduce new benchmark problems has drawn attention to the development of new optimization algorithms. Recently, a set of constrained benchmark problems has been developed as a addition to CEC benchmark series. This paper proposed a hybrid variant of gaining sharing knowledge base...

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Veröffentlicht in:2021 IEEE Congress on Evolutionary Computation (CEC) S. 841 - 848
Hauptverfasser: Mohamed, Ali Wagdy, Hadi, Anas A., Agrawal, Prachi, Sallam, Karam M., Mohamed, Ali Khater
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 28.06.2021
Schlagworte:
Benchmark testing
CEC 2021
Convergence
Evolutionary computation
GSK algorithm
Knowledge based systems
LSHADE algorithm
Numerical Optimization
Optimization
Probability
Robustness
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Zusammenfassung:The initiative to introduce new benchmark problems has drawn attention to the development of new optimization algorithms. Recently, a set of constrained benchmark problems has been developed as a addition to CEC benchmark series. This paper proposed a hybrid variant of gaining sharing knowledge based algorithm with adaptive parameters and improved multi-operator differential evolution (IMODE) algorithm, called APGSK-IMODE. It enhanced the performance of recently developed adaptive gaining sharing knowledge based algorithm. The performance of APGSK-IMODE has been tested on CEC2021 benchmark problems which contains 10 test functions with dimensions 10 and 20. The results obtained from the proposed algorithm have been compared with those obtained from the rival algorithms. The results elaborate the superiority of APGSK-IMODE. APGSK-IMODE outperforms the competing algorithms with regard to quality of solution, robustness and convergence.
DOI:10.1109/CEC45853.2021.9504814