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Current Bioinformatics

Author(s): Jianfu Xia, Zhennao Cai, Ali Asghar Heidari, Yinghai Ye*, Huiling Chen* and Zhifang Pan*

DOI: 10.2174/1574893617666220920102401

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Enhanced Moth-flame Optimizer with Quasi-Reflection and Refraction Learning with Application to Image Segmentation and Medical Diagnosis

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Abstract

Background: Moth-flame optimization will meet the premature and stagnation phenomenon when encountering difficult optimization tasks.

Objective: 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 to overcome shortcomings.

Methods: 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.

Keywords: Moth-flame optimization, global optimization, multilevel thresholding image segmentation, medical diagnosis, particle swarm optimization, ACO.

Graphical Abstract

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