Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Rahman, Chnoor M.; Rashid, Tarik A.; Ahmed, Aram Mahmood; Mirjalili, Seyedali

doi:10.1007/s00521-021-06811-z

Cited by 20 publications

(2 citation statements)

References 37 publications

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“…A multiobjective metaheuristic optimizer was used in this investigation. In multi-objective optimization problems [27][28][29][30], there are two or more optimization goals that compete with each other. This means that reaching one goal will make it harder to reach another.…”

Section: Related Workmentioning

confidence: 99%

NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer

Rashid,

Majidpour,

Thinakaran

et al. 2024

IEEE Access

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Immunohistochemistry (IHC) slides are graded for breast cancer based on visual markers and morphological characteristics of stained membrane regions. The usage of whole slide images (WSIs) from histology in digital pathology algorithms for computer-assisted evaluations has increased recently. Human epidermal growth factor receptor 2 (HER2)-stained microscopic images are challenging, time-consuming, and error-prone to evaluate manually. This is due to different staining, overlapped regions, and huge, nonhomogeneous slides. Additionally, the classification of HER2 images by the selection of fundamental features must be used to capture the difficult elements of the images, such as the irregular cell structure and the coloring of the tissue of the cells. To solve the above problems, a transfer learning model-based, trainable metaheuristic method for choosing the best features is suggested in this paper. Moreover, the suggested model is efficient in reducing model complexity and computational costs as well as avoiding overfitting. The four main components of the proposed cascaded design are: (a) converting WSIs to tiled images and enhancing contrast with fast local Laplacian filtering (FlLpF); (b) extracting features with a ResNet50 CNN technique based on transfer learning; (c) selecting the most informative features with the help of a non-dominated sorting genetic algorithm (NSGA-II) optimizer; and (d) using a support vector machine (SVM) to classify HER2 scores. Results from the HER2SC and HER2GAN datasets show that the suggested model is superior to other methods already in use, with 94.4% accuracy, 93.71% precision, 98.07% specificity, 93.83% sensitivity, and a 93.71% F1-score for the HER2SC dataset being achieved.INDEX TERMS HER2, CNN, transfer learning, NSGA-II optimizer, FlLpF.

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Section: Related Workmentioning

confidence: 99%

NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer

Rashid,

Majidpour,

Thinakaran

et al. 2024

IEEE Access

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“…As pointed out in [46], there are several reasons to conclude that it is difficult to measure the time complexity of multi-objective evolutionary algorithms; however it is important to examine it. According to [42], one generation of NSGA-II has complexity O(M × N 2 ), which is governed by the non-dominated sorting part of the algorithm.…”

Section: Computational Complexitymentioning

confidence: 99%

Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms

2023

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Antoine’s Equation is commonly used to explain the relationship between vapour pressure and temperature for substances of industrial interest. This paper sets out a combined strategy to obtain optimal designs for the Antoine Equation for D- and I-optimisation criteria and different variance structures for the response. Optimal designs strongly depend not only on the criterion but also on the response’s variance, and their efficiency can be strongly affected by a lack of foresight in this selection. Our approach determines compound and multi-objective designs for both criteria and variance structures using a genetic algorithm. This strategy provides a backup for the experimenter providing high efficiencies under both assumptions and for both criteria. One of the conclusions of this work is that the differences produced by using the compound design strategy versus the multi-objective one are very small.

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Learner Performance-Based Behavior Optimization Algorithm: A Functional Case Study

Salih

Rashid

2022

Advances in Intelligent Systems and Computing

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A novel algorithm called learner performance-based behavior algorithm (LPB) was proposed for single and multi-objective by Chnoor M. Rahman and Tarik A. Rashid in 2021. LPB proved its ability to deal with complex optimization problems compared to the dragonfly algorithm (DA), genetic algorithm (GA), and particle swarm optimization (PSO). This paper presents and explains the implementation of the LPB algorithm, and it applies it as a model in a case study to maximize a fitness function. As a result, the LPB algorithm is successfully improved the initial population and achieved the optimal solution.

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Multi-objective learner performance-based behavior algorithm with five multi-objective real-world engineering problems

Cited by 20 publications

References 37 publications

NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer

NSGA-II-DL: Metaheuristic Optimal Feature Selection With Deep Learning Framework for HER2 Classification in Breast Cancer

Optimal Designs for Antoine’s Equation: Compound Criteria and Multi-Objective Designs via Genetic Algorithms

Learner Performance-Based Behavior Optimization Algorithm: A Functional Case Study

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