A scattering and repulsive swarm intelligence algorithm for solving global optimization problems

2019

Applied Soft Computing

Self Cite

112

In this research, we propose an intelligent decision support system for skin cancer detection. Since generating an effective lesion representation is a vital step to ensure the success of lesion classification, the discriminative power of different types of features is exploited. Specifically, we combine clinically important asymmetry, border irregularity, colour and dermoscopic structure features with texture features extracted using Grey Level Run Length Matrix, Local Binary Patterns, and Histogram of Oriented Gradients operators for lesion representation. Then, we propose two enhanced Particle Swarm Optimization (PSO) models for feature optimization. The first model employs adaptive acceleration coefficients, multiple remote leaders, in-depth sub-dimension feature search and reinitialization mechanisms to overcome stagnation. The second model uses random acceleration coefficients, instead of adaptive ones, based on non-linear circle, sine and helix functions, respectively, to increase diversification and intensification. Ensemble classifiers are also constructed with each base model trained using each optimized feature subset. A deep convolutional neural network is devised whose hyper-parameters are finetuned using the proposed PSO models. Extensive experimental studies using dermoscopic skin lesion data, medical data from the UCI machine learning repository, and ALL-IDB2 image data are conducted to evaluate the model efficiency systematically. The results from empirical evaluations and statistical tests indicate the superiority of the proposed models over other advanced PSO variants and classical search methods pertaining to discriminative feature selection and optimal hyper-parameter identification for deep learning networks in lesion classification as well as other disease diagnosis.

Section: Discussionmentioning

confidence: 99%

Intelligent skin cancer diagnosis using improved particle swarm optimization and deep learning models

Tan

2019

Applied Soft Computing

Self Cite

112

“…PSO is a population-based self-adaptive optimisation technique developed by Kennedy and Eberhart [ 22 ] based on swarm social behaviors, such as fish in a school and birds in a flock. The PSO algorithm conducts search in the landscape of the objective function by adjusting the trajectories of individual particles in a quasi-stochastic manner [ 23 , 24 ]. Each particle adjusts its velocity and position by following its own best experience in history and the global best solution of the swarm.…”

Section: Related Studiesmentioning

confidence: 99%

Feature Selection Using Enhanced Particle Swarm Optimisation for Classification Models

Xie

et al. 2021

Sensors

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In this research, we propose two Particle Swarm Optimisation (PSO) variants to undertake feature selection tasks. The aim is to overcome two major shortcomings of the original PSO model, i.e., premature convergence and weak exploitation around the near optimal solutions. The first proposed PSO variant incorporates four key operations, including a modified PSO operation with rectified personal and global best signals, spiral search based local exploitation, Gaussian distribution-based swarm leader enhancement, and mirroring and mutation operations for worst solution improvement. The second proposed PSO model enhances the first one through four new strategies, i.e., an adaptive exemplar breeding mechanism incorporating multiple optimal signals, nonlinear function oriented search coefficients, exponential and scattering schemes for swarm leader, and worst solution enhancement, respectively. In comparison with a set of 15 classical and advanced search methods, the proposed models illustrate statistical superiority for discriminative feature selection for a total of 13 data sets.

“…Moreover, owing to their simplicity and flexibility, PSO and FA and their variants have been employed to solve diverse real-life single-objective and multi-objective optimization problems, which include hyperspectral image classification [21], colour image segmentation [3], stock price index forecasting [22], data clustering [23] and mathematical benchmark optimization [24]. They have also been used for discriminative feature selection for facial and bodily expression recognition [25,26,27,28], brain tumour [4], heart disease [29,30], skin and blood cancer [31,32] detection and classifier ensemble reduction [33].…”

Section: The Final Term In Equationmentioning

confidence: 99%

“…the optimized CNN model has the same depth of three different types of convolutional blocks. Based on the identified optimal network depth, the initial number of filters, , for convolutional block 1 is calculated using Equation (24).…”

Section: Figure 4 the Structure Of The Initial Cnn Modelmentioning

confidence: 99%

Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks

Tan

Knowledge-Based Systems

2020

Self Cite

In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm, namely hybrid learning PSO (HLPSO), for skin lesion segmentation and classification. HLPSO combines diverse search mechanisms including modified Firefly Algorithm (FA) operations, a new spiral research action, probability distributions, crossover, and mutation procedures to diversify and improve the original PSO algorithm. It is used in conjunction with the K-Means clustering algorithm to enhance lesion segmentation. Its cost function takes both intra-class and inter-class variations into account to increase scalability. Two lesion classification systems are formulated based on HLPSO. In the first system, HLPSO is used to devise evolving convolutional neural networks (CNN) with optimized topologies and hyper-parameters for lesion classification. In the second system, shape and colour features, as well as texture features extracted using the Kirsch operator and Shift Local Binary Patterns are used to produce an initial discriminative lesion representation. HLPSO is then used to identify the most significant components of each feature vector for ensemble lesion classification. Evaluated using several skin lesion data sets, both systems depict superior capabilities in lesion segmentation, deep CNN architecture generation, and discriminative feature selection for ensemble lesion classification, and outperform a number of advanced PSO and FA variants, classical search methods, as well as other related models on skin lesion classification significantly. HLPSO also yields better performances over other classical and advanced search methods in solving a number of benchmark tasks related to mathematical landscapes and those in the complex CEC 2014 test suite.