Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in term...Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.展开更多
In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selec...In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.展开更多
Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a da...Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining.Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimizing the FS problem.The White Shark Optimizer(WSO),as a metaheuristic algorithm,primarily simulates the behavior of great white sharks’sense of hearing and smelling during swimming and hunting.However,it fails to consider their other randomly occurring behaviors,for example,Tail Slapping and Clustered Together behaviors.The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm.The Clustered Together behavior includes access to food and mating,which can change the direction of local search and enhance local utilization.It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer(IWSO).The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions,and the test results of IWSO are compared with other metaheuristic algorithms,which proves that IWSO combining the two behaviors has a stronger search capability.Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model,which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor(KNN)on 16 benchmark datasets and the results are compared with 7 metaheuristics.Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.展开更多
The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was prop...The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test展开更多
The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual elec...The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.展开更多
基金This work was supported by the National Natural Science Foundation of China(Grant No.2017YFC0403605 and No.11601419).
文摘Whale optimization algorithm(WOA)is a new population-based meta-heuristic algorithm.WOA uses shrinking encircling mechanism,spiral rise,and random learning strategies to update whale’s positions.WOA has merit in terms of simple calculation and high computational accuracy,but its convergence speed is slow and it is easy to fall into the local optimal solution.In order to overcome the shortcomings,this paper integrates adaptive neighborhood and hybrid mutation strategies into whale optimization algorithms,designs the average distance from itself to other whales as an adaptive neighborhood radius,and chooses to learn from the optimal solution in the neighborhood instead of random learning strategies.The hybrid mutation strategy is used to enhance the ability of algorithm to jump out of the local optimal solution.A new whale optimization algorithm(HMNWOA)is proposed.The proposed algorithm inherits the global search capability of the original algorithm,enhances the exploitation ability,improves the quality of the population,and thus improves the convergence speed of the algorithm.A feature selection algorithm based on binary HMNWOA is proposed.Twelve standard datasets from UCI repository test the validity of the proposed algorithm for feature selection.The experimental results show that HMNWOA is very competitive compared to the other six popular feature selection methods in improving the classification accuracy and reducing the number of features,and ensures that HMNWOA has strong search ability in the search feature space.
基金the Deputyship for Research and Innovation,“Ministry of Education”in Saudi Arabia for funding this research(IFKSUOR3-014-3).
文摘In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.
基金supported in part by the Basic Research Program of the Educational Department of Liaoning Province(Grant No.JYTMS20230802)the Natural Science Foundation of Liaoning Province(Grant No.2023-MS-317)the China Postdoctoral Science Foundation(Grant No.2021M701537).
文摘Feature Selection(FS)is an optimization problem that aims to downscale and improve the quality of a dataset by retaining relevant features while excluding redundant ones.It enhances the classification accuracy of a dataset and holds a crucial position in the field of data mining.Utilizing metaheuristic algorithms for selecting feature subsets contributes to optimizing the FS problem.The White Shark Optimizer(WSO),as a metaheuristic algorithm,primarily simulates the behavior of great white sharks’sense of hearing and smelling during swimming and hunting.However,it fails to consider their other randomly occurring behaviors,for example,Tail Slapping and Clustered Together behaviors.The Tail Slapping behavior can increase population diversity and improve the global search performance of the algorithm.The Clustered Together behavior includes access to food and mating,which can change the direction of local search and enhance local utilization.It incorporates Tail Slapping and Clustered Together behavior into the original algorithm to propose an Improved White Shark Optimizer(IWSO).The two behaviors and the presented IWSO are tested separately using the CEC2017 benchmark functions,and the test results of IWSO are compared with other metaheuristic algorithms,which proves that IWSO combining the two behaviors has a stronger search capability.Feature selection can be mathematically described as a weighted combination of feature subset size and classification error rate as an optimization model,which is iteratively optimized using discretized IWSO which combines with K-Nearest Neighbor(KNN)on 16 benchmark datasets and the results are compared with 7 metaheuristics.Experimental results show that the IWSO is more capable in selecting feature subsets and improving classification accuracy.
文摘The identification of targets varies in different surge tests. A multi-color space threshold segmentation and self-learning k-nearest neighbor algorithm （k-NN） for equipment under test status identification was proposed after using feature matching to identify equipment status had to train new patterns every time before testing. First, color space （L＊a＊b＊, hue saturation lightness （HSL）, hue saturation value （HSV）） to segment was selected according to the high luminance points ratio and white luminance points ratio of the image. Second, the unknown class sample Sr was classified by the k-NN algorithm with training set T~ according to the feature vector, which was formed from number ofpixels, eccentricity ratio, compact- ness ratio, and Euler＇s numbers. Last, while the classification confidence coefficient equaled k, made Sr as one sample ofpre-training set Tz＇. The training set Tz increased to Tz＋1 by Tz＇ if Tz＇ was saturated. In nine series of illuminant, indicator light, screen, and disturbances samples （a total of 21600 frames）, the algorithm had a 98.65% identification accuracy, also selected five groups of samples to enlarge the training set from To to T5 by itself. Keywords multi-color space, k-nearest neighbor algorithm （k-NN）, self-learning, surge test
文摘The potential to save energy in existing consumer electrical appliances is very high. One of the ways to achieve energy saving and improve energy use awareness is to recognize the energy consumption of individual electrical appliances. To recognize the energy consumption of consumer electrical appliances, the load disaggregation methodology is utilized. Non-intrusive appliance load monitoring (NIALM) is a load disaggrega-tion methodology that disaggregates the sum of power consumption in a single point into the power consumption of individual electrical appliances. In this study, load disaggregation is performed through voltage and current waveform, known as the V-I trajectory. The classification algorithm performs cropping and image pyramid reduction of the V-I trajectory plot template images before utilizing the principal component analysis (PCA) and the k-nearest neighbor (k-NN) algorithm. The novelty of this paper is to establish a systematic approach of load disaggregation through V-I trajectory-based load signature images by utilizing a multi-stage classification algorithm methodol-ogy. The contribution of this paper is in utilizing the “k- value,” the number of closest data points to the nearest neighbor, in the k-NN algorithm to be effective in classification of electrical appliances. The results of the multi-stage classification algorithm implementation have been discussed and the idea on future work has also been proposed.
