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- W4296790069 abstract "Background: Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks. Objective: To overcome the above shortcomings, this paper presented a quasi-reflection moth-flame optimization algorithm with refraction learning called QRMFO to strengthen the property of ordinary MFO and apply it in various application fields. Method: In the proposed QRMFO, quasi-reflection-based learning increases the diversity of the population and expands the search space on the iteration jump phase; refraction learning improves the accuracy of the potential optimal solution. Results: Several experiments are conducted to evaluate the superiority of the proposed QRMFO in the paper; first of all, the CEC2017 benchmark suite is utilized to estimate the capability of QRMFO when dealing with the standard test sets compared with the state-of-the-art algorithms; afterward, QRMFO is adopted to deal with multilevel thresholding image segmentation problems and real medical diagnosis case. Conclusion: Simulation results and discussions show that the proposed optimizer is superior to the basic MFO and other advanced methods in terms of convergence rate and solution accuracy." @default.
- W4296790069 created "2022-09-23" @default.
- W4296790069 creator A5001593118 @default.
- W4296790069 creator A5010004403 @default.
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- W4296790069 creator A5028441474 @default.
- W4296790069 creator A5028845356 @default.
- W4296790069 creator A5067951554 @default.
- W4296790069 date "2023-02-01" @default.
- W4296790069 modified "2023-10-17" @default.
- W4296790069 title "Enhanced Moth-flame Optimizer with Quasi-Reflection and Refraction Learning with Application to Image Segmentation and Medical Diagnosis" @default.
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