Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Mohammadi, Ali; Sheikholeslam, Farid; Mirjalili, Seyedali

doi:10.1007/s11831-022-09800-0

Cited by 18 publications

(2 citation statements)

References 139 publications

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“…A reported 1.9 billion people worldwide are affected by skin diseases, with the shortage of dermatologists leading to many seeking dermatologic care from general practitioners, resulting in lower diagnostic accuracy [45]. Additionally, the utilization of heuristic/evolutionary search optimization AI-based algorithms, such as the Gravitational Search Algorithm [47][48][49] and Inclined Planes System Optimizations [50,51], to tackle hyperparameter optimization during network training is a promising research area. This paper introduces supervised loss, AdaBoost loss, and distillation loss, suggesting that enhancing the performance of dermatological diagnostic models could be achieved by carefully selecting optimal values for loss term weights and the temperature coefficient.…”

Section: Dermatologic Diagnostic Systems In Clinical Applicationsmentioning

confidence: 99%

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Yu,

Xiong,

et al. 2024

Complex Intell. Syst.

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Knowledge Distillation has shown promising results for classifying skin lesions in dermatology images. Traditional knowledge distillation typically involves the student model passively mimicking the teacher model's knowledge. We propose utilizing AdaBoost to enable the student to actively mine the teacher's learning representation for skin lesion classification. This paradigm allows the student to determine the “granularity” in mining the teacher's knowledge. As the student's learning process progresses, it can become challenging to pinpoint specific learning difficulties, especially with potential interference from the teacher. To address this issue, we introduce a variational difficulty mining strategy to reduce the impact of such interference. This strategy involves the distillation module capturing more nuanced classification difficulties by extracting information from the node's $$l$$ l th hops. By maximizing the mutual information between the teacher and student, we effectively filter out noise interference from these nuanced difficulties. Our proposed framework, Variational AdaBoost Knowledge Distillation (VAdaKD), allows the student to actively mine and leverage the teacher's knowledge for improved skin lesion classification. Our proposed method performs satisfactorily on three benchmark datasets: the Dermnet dataset, ISIC 2019 dataset, and HAM10000 dataset, respectively. Specifically, our method shows an improvement of 2–3% over the baseline on the Dermnet dataset and outperforms the best results of the other compared methods by 1%. Experimental results and visualization performance indicate that our proposed method effectively captures the learning difficulties and achieves better visualized t-distributed stochastic neighbor embedding classification results. Our code is available at https://github.com/25brilliant/VAdaKD.

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Section: Dermatologic Diagnostic Systems In Clinical Applicationsmentioning

confidence: 99%

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Yu,

Xiong,

et al. 2024

Complex Intell. Syst.

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“…In recent years, there have been two notable scientists who are active in the development of many metaheuristics: Mirjalili and Dehghani. Mirjalili was involved in a lot of metaheuristics, such as grey wolf algorithm (GWA) [8], marine predator algorithm (MPA) [9], cheetah optimizer (CO) [10], coronavirus optimization algorithm (COVIDOA) [11], geometric mean optimizer (GMO) [12], geyser inspired algorithm (GEA) [13], inclined planes system optimization (IPO) [14], and so on. Dehghani was involved in many swarm-based metaheuristics, such as northern goshawk optimization (NGO) [15], pelican optimization algorithm (POA) [16], language education optimization (LEO) [17], fully informed search algorithm (FISA) [18], coati optimization algorithm (COA) [19], zebra optimization algorithm (ZOA) [20], walrus optimization algorithm (WaOA) [21], average subtraction based optimization (ASBO) [22], three influential member-based optimization (TIMBO) [23], multileader optimization (MLO) [24], mixed leader based optimization (MLBO) [25], hybrid leader based optimization (HLBO) [26], and so on.…”

Section: Introductionmentioning

confidence: 99%

Swarm Bipolar Algorithm: A Metaheuristic Based on Polarization of Two Equal Size Sub Swarms

2024

IJIES

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This paper presents a new metaphor-free metaheuristic search called the swarm bipolar algorithm (SBA). SBA is developed mainly based on the non-free-lunch (NFL) doctrine, which mentions the non-existence of any general optimizer appropriate to answer all varieties of problems. The construction of SBA is based on splitting the swarm into two equal-sized swarms to diversify the searching process while performing intensification within the subswarms. There are two types of finest swarm members: the finest swarm member for the whole swarm and the finest swarm member in every sub-swarm. There are four directed searches performed in every iteration: (1) search toward the finest swarm member, (2) search toward the finest sub-swarm member, (3) search toward the middle between two finest sub-swarm members, and (4) search relative to the randomly picked swarm member from another sub-swarm. The performance of SBA is assessed through two assessments with a set of 23 functions representing the optimization problem. In the benchmark assessment, SBA is contended with five metaheuristics: northern goshawk optimization (NGO), language education optimization (LEO), coati optimization algorithm (COA), fully informed search algorithm (FISA), and total interaction algorithm (TIA). The result presents the superiority of SBA among its contenders by being better than NGO, LEO, COA, FISA, and TIA in 21,16,16,21,and 18 functions. The single search assessment is performed to evaluate each strategy involved in SBA. The result shows that the search toward the middle between the two finest sub-swarm members is the best among the four searches in SBA.

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Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

Al-Hasani,

Shahid

2024

Stoch Environ Res Risk Assess

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Nature-Inspired Metaheuristic Search Algorithms for Optimizing Benchmark Problems: Inclined Planes System Optimization to State-of-the-Art Methods

Cited by 18 publications

References 139 publications

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Variational AdaBoost knowledge distillation for skin lesion classification in dermatology images

Swarm Bipolar Algorithm: A Metaheuristic Based on Polarization of Two Equal Size Sub Swarms

Development of radiation and temperature-based empirical models for accurate daily reference evapotranspiration estimation in Iraq

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