Penguin Search Optimization Research Articles

Penguin Search Optimization Based Feature Selection for Automated Opinion Mining

Twitter sentiment analysis is a vital concept in determining the public opinions about products, services, events or personality. Analyzing the medical tweets on a specific topic can provide immense benefits in medical industry. However, the medical tweets require efficient feature selection approach to produce significantly accurate results. Penguin search optimization algorithm (PeSOA) has the ability to resolve NP-hard problems. This paper aims at developing an automated opinion mining framework by modeling the feature selection problem as NP-hard optimization problem and using PeSOA based feature selection approach to solve it. Initially, the medical tweets based on cancer and drugs keywords are extracted and pre-processed to filter the relevant informative tweets. Then the features are extracted based on the Natural Language Processing (NLP) concepts and the optimal features are selected using PeSOA whose results are fed as input to three baseline classifiers to achieve optimal and accurate sentiment classification. The experimental results obtained through MATLAB simulations on cancer and drug tweets using k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) indicate that the proposed PeSOA feature selection based tweet opinion mining has improved the classification performance significantly. It shows that the PeSOA feature selection with the SVM classifier provides superior sentiment classification than the other classifiers.

Liver Cancer Recognition and Categorization Based on Optimum Hierarchical Feature Fusion with Pesoa and DVW Technique

Liver malignant growth extends the demise rate on the grounds that the symptoms can't be recognized even the disease is in its propelled stage. The early analysis and steady watching is the most ideal approach to control the advancement of the harm and to spare the lives. Ultrasound imaging is a champion among the most as often as possible used determination instruments to recognize and characterize inconsistencies of the liver which is likewise a non-obtrusive, safe procedure for patient examination, being anything but difficult to apply, efficient than the CT, MRI, PET based liver tumor recognition. Conventional liver disease recognition systems have high calculation time and multifaceted nature. So as to decrease the multifaceted nature in the computational method and to upgrade the symptomatic precision in this paper we propose another ideal progressive component combination dependent on Penguin Search Optimization Algorithm (PeSOA).

PENERAPAN ALGORITMA PENGUINS SEARCH OPTIMIZATION (PeSOA) DAN ALGORITMA MIGRATING BIRDS OPTIMIZATION (MBO) PADA PERMASALAHAN KNAPSACK 0-1

Every person would want maximum profit with as little as possible resources or capital. One example in everyday life is the problem of limited storage media but is required to get the maximum benefit. From this problem comes the term known as the knapsack problem. One of the problems with Knapsack is knapsack 0- 1, where knapsack 0-1 is a problem of storing goods where the item will be completely inserted or not at all. Completion of knapsack 0-1 problems can be helped using a metaheuristic algorithm. Metaheuristic algorithms include the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization (MBO) algorithm. This study aims to determine the resolution of knapsack 0-1 problems using the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization (MBO) algorithm and compare the optimal solutions obtained. This research method is divided into three main parts. First take data that includes the name of the item, the purchase price, the selling price and the weight of each item. The second is applying the Penguins Search Optimization (PeSOA) algorithm and the Migration Birds Optimization algorithm (MBO) on 0-1 knapsack problems. The third program is made to facilitate the calculation of data with the help of Matlab R2015b software. The results of this study found that both algorithms both reached the optimal solution, but the convergence and running time obtained were different. The Migrating Birds Optimization (MBO) algorithm is faster converging than the Penguins Search Optimization (PeSOA) algorithm to get the optimal solution. And also the Migrating Birds Optimization (MBO) algorithm has better running time than the Penguins Search Optimization (PeSOA) algorithm to achieve maximum iteration.
 Keywords: Whale optimization algorithm, multi knapsack 0-1 problem with multiple constraints.

GwPeSOA-based MSVNN: the multimodal biometric system for futuristic security applications

Biometric systems have gained considerable significance as they are highly employed in the security applications. Achieving human recognition is easier and cheaper and the single modality employed for the recognition faces a lot of challenges due to the environmental factors. Thus, the paper proposes a multimodal recognition system based on the Multi-Support Vector Neural Network (MSVNN). The algorithm proposed is the Glowworm Penguin Search Optimization Algorithm (GwPeSOA), which is a modification of the Glowworm Optimization Algorithm (GOA) with the Penguin Search Optimization Algorithm (PeSOA). The proposed method employs two modalities: the ear and the finger vein modalities; the features of the ear image are obtained using the proposed BiComp masking method of feature extraction, whereas the features from the finger vein are extracted using the Repeated Line Tracking (RLT) method. The features obtained are applied to the MSVNN classifier to recognize the person with good accuracy and the proposed BiComp Mask offers robust features for the extraction. The experimentation using the proposed method attained a better accuracy, specificity and sensitivity at a rate of 0.95, 0.95 and 0.9868, respectively.

Categorizing online news articles using penguin search optimization algorithm

Online news is an emerging channel where the internet users can get news. Analyzing huge volume of online news articles is a challenging one, because online news articles are generated and updated time to time. Big data techniques are used to tackle this problem. In order to classify the news articles into different categories, an approach based on Evolving Fuzzy Systems(EFS) was used. It categories news articles based on the changes in the content of the corresponding articles. However, it has the problem in the selection of threshold value. Moreover Gaussian membership function is used in EFS that describes the closeness to the prototype. Sometimes it is hard to justify. So in this paper, a Penguins Search Optimization Algorithm(PeSOA) is introduced to optimize the pruning threshold value and a bell shaped fuzzy membership function is introduced to define the closeness to the prototype. The optimized pruning threshold is used in term filtering which prune the generated terms based on their frequencies of occurrence throughout the collection. Then the fuzzy rules are generated by EFS where bell shaped fuzzy membership function is used to define the closeness to the prototype. Based on the fuzzy rules the online news articles are categorized.

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

Liver cancer detection and classification based on optimum hierarchical feature fusion with PeSOA and PNN classifier

Detection and segmentation of abnormalities present in the liver is one of the testing and vital steps to treatment planning which expands the survival of a patient. Liver cancer expands the death rate because the side effects cannot be distinguished even the cancer is in its advanced stage. The early diagnosis and consistent observing is the best way to control the development of the malignancy and to save the lives. Ultrasound imaging is a standout amongst the most frequently utilized diagnosis instruments to distinguish and classify irregularities of the liver which is also a non-invasive, safe technique for patient examination, being easy to apply, economical than the CT, MRI, PET based liver tumor detection. Traditional liver cancer detection strategies have high computation time and multifaceted nature. In order to diminish the complexity in the computational procedure and to enhance the diagnostic exactness in this paper we propose a new optimum hierarchical feature fusion based on Penguin Search Optimization Algorithm (PeSOA). The features resulting from PeSOA is used by a Probabilistic Neural Network (PNN) which classifies the liver cancer tissues. Test results demonstrate that the proposed technique acquires 98.92% classification accuracy and the correlation demonstrates that we obtained superior results than the existing strategies.

Introducing modified version of penguins search optimization algorithm (PeSOA) and its application in optimal operation of reservoir systems

Abstract In the current study, modified version of the penguins search optimization algorithm (PeSOA) was introduced, and its usage was assessed in the water resources field. In the modified version (MPeSOA), the Gaussian exploration was added to the algorithm. The MPeSOA performance was evaluated in optimal operation of a hypothetical four-reservoir system and Karun-4 reservoir as a real world problem. Also, genetic algorithm (GA) was used as a criterion for evaluating the performance of PeSOA and MPeSOA. The results revealed that in a four-reservoir system problem, the PeSOA performance was much weaker than the GA; but on the other hand, the MPeSOA had better performance than the GA. In the mentioned problem, PeSOA, GA, and MPeSOA reached 78.43, 97.46, and 98.30% of the global optimum, respectively. In the operation of Karun-4 reservoir, although PeSOA performance had less difference with the two other algorithms than four-reservoir problem, its performance was not acceptable. The average values of objective function in this case were equal to 26.49, 23.84, and 21.48 for PeSOA, GA, and MPeSOA, respectively. According to the results obtained in the operation of Karun-4 reservoir, the algorithms including MPeSOA, GA, and PeSOA were situated in ranks one to three in terms of efficiency, respectively.

Using Penguins Search Optimization Algorithm for Best Features Selection for Biomedical Data Classification

Feature selection is essential to improve the classification effectiveness. This paper presents a new adaptive algorithm called FS-PeSOA (feature selection penguins search optimization algorithm) which is a meta-heuristic feature selection method based on “Penguins Search Optimization Algorithm” (PeSOA), it will be combined with different classifiers to find the best subset features, which achieve the highest accuracy in classification. In order to explore the feature subset candidates, the bio-inspired approach PeSOA generates during the process a trial feature subset and estimates its fitness value by using three classifiers for each case: Naive Bayes (NB), Nearest Neighbors (KNN) and Support Vector Machines (SVMs). Our proposed approach has been experimented on six well known benchmark datasets (Wisconsin Breast Cancer, Pima Diabetes, Mammographic Mass, Dermatology, Colon Tumor and Prostate Cancer data sets). Experimental results prove that the classification accuracy of FS-PeSOA is the highest and very powerful for different datasets.

Bio Inspired Distributed WSN Localization Based on Chicken Swarm Optimization

Prime goal of WSN deployment in large specific area aims to sense the environment and execute the defined application with the help of essential location information of the devices. Through localization technique, location information is assigned to the unknown devices within the area of interest. Due to its definite solution capabilities with fast convergence rate, bio inspired application become popular to solve numerous applications in the field of wireless sensor network (WSN) applications. In this paper, a newly developed meta- heuristic algorithm based on the social behavior of chickens named as chicken swarm optimization (CSO) is proposed to solve the WSN node localization problem. Two performance metrics which are node precision and computation time are investigated using three different bio inspired algorithms that are particle swarm optimization (PSO), binary particle swarm optimization (BPSO) and penguin search optimization algorithm (PeSOA) respectively. Results are demonstrated using simulation graph where CSO performs more precise accuracy having a ratio of 55% over PSO and BPSO and 10% over PeSOA. For computation time, proposed algorithm performs a computation time that is shorter by 30% than PeSOA as well as 50 and 40% than PSO and BPSO, respectively.

Optimal Placement of DSTATCOM in Radial Distribution System using Fuzzy and Penguins Search Optimization Algorithm

This paper proposes a hybrid methodology to identify the optimal location and sizing for distribution static compensator (DSTATCOM) placement as a distribution flexible ac transmission system (DFACT) device in a distribution system, by using fuzzy and Penguins search optimization algorithm (PeSOA). The main purpose is to reduce the power loss and to improve the voltage profile of distribution networks. The PeSOA is meta-heauristic search method inspired from the hunting strategy of penguins. Here fuzzy is used for finding the optimal location and PeSOA is used to find the size of DSTATCOM in radial distribution networks. The feasibility and satisfying performance of the proposed method is examined on the IEEE 33-bus and 69-bus distribution system.