An enhanced particle swarm optimization algorithm

In this paper, an enhanced stochastic optimization algorithm based on the basic Particle Swarm Optimization (PSO) algorithm is proposed. The basic PSO algorithm is built on the activities of the social feeding of some animals. Its parameters may influence the solution considerably. Moreover, it has a couple of weaknesses, for example, convergence speed and premature convergence. As a way out of the shortcomings of the basic PSO, several enhanced methods for updating the velocity such as Exponential Decay Inertia Weight (EDIW) are proposed in this work to construct an Enhanced PSO (EPSO) algorithm. The suggested algorithm is numerically simulated established on five benchmark functions with regards to the basic PSO approaches. The performance of the EPSO algorithm is analyzed and discussed based on the test results

Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method

USING PARTICLE SWARM OPTIMIZATION TO FIND OPTIMAL SIZING OF PV-BS AND DIESEL GENERATOR

This paper explores the sizing optimization of stand -alone hybrid energy system (HES) in southern Iraq (Thi Qar province) for supply stand-alone households by the electricity. HES consist of three components (solar cell (PV), diesel generator (DG) and battery storage (BS)). Particle swarm optimization (PSO) used in this study for find optimal sizing of the HES to minimizing multi-objective, first objective is to minimizing the total system cost (TSC) that lead to minimizing cost of energy (COE). Second objective is to minimizing total emission CO2 (TECO2). The constraint of the optimization is the reliability (100 %) mean continuous provide the load demand by the electricity. The results of the optimization show the ability the algorithm to minimizing the multi-objective with continuous supply the load by the electricity through life cycle of the project (25) years

Implementasi Algoritme Improved Particle Swarm Optimization Untuk Optimasi Komposisi Bahan Makanan Untuk Memenuhi Kebutuhan Gizi Penderita Diabetes Melitus

Diabetes Melitus merupakan salah satu penyakit dengan jumlah korban terbanyak di Indonesia. Tingginya penderita diabetes di Indonesia dikarenakan minimnya pengetahuan masyarakat mengenai kendali makanan yang sehat yang mengakibatkan pola makan mereka menjadi buruk. Akibatnya, banyak masyarakat Indonesia yang belum memenuhi keseimbangan asupan gizi yang menjadi bagian terpenting dalam mengatur pola makan yang baik dan sehat. Informasi mengenai pola makan yang tepat diperlukan bagi penderita diabetes untuk memperbaiki kondisi kesehatan mereka. Algoritme Particle Swarm Optimization (PSO) seringkali digunakan dalam melakukan sebuah kasus optimasi dengan hasil yang baik dan optimal, terlebih terdapat pengembangan menjadi Improved Particle Swarm Optimization (IPSO) yang semakin meningkatkan performa PSO. Oleh karena itu, penelitian ini merancang sebuah sistem optimasi komposisi bahan makanan untuk kebutuhan gizi penderita Diabetes Melitus dengan menggunakan algoritme Improved-PSO. Hasil yang didapatkan dari penelitian ini berupa parameterparameter Improved-PSO optimal yaitu jumlah populasi = 150, nilai koefisien akselerasi = 2;1, serta sistem konvergen pada iterasi ke 550. Selain itu, dari hasil analisis global menunjukkan bahwa perhitungan gizi dari sistem dapat memenuhi kebutuhan gizi pasien dengan selisih toleransi ± 10% dari perhitungan pakar

Document clustering with optimized unsupervised feature selection and centroid allocation

An effective document clustering system can significantly improve the tasks of document analysis, grouping, and retrieval. The performance of a document clustering system mainly depends on document preparation and allocation of cluster positions. As achieving optimal document clustering is a combinatorial NP-hard optimization problem, it becomes essential to utilize non-traditional methods to look for optimal or near-optimal solutions. During the allocation of cluster positions or the centroids allocation process, the extra text features that represent keywords in each document have an effect on the clustering results. A large number of features need to be reduced using dimensionality reduction techniques. Feature selection is an important step that can be used to reduce the redundant and inconsistent features. Due to a large number of the potential feature combinations, text feature selection is considered a complicated process. The persistent drawbacks of the current text feature selection methods such as local optima and absence of class labels of features were addressed in this thesis. The supervised and unsupervised feature selection methods were investigated. To address the problems of optimizing the supervised feature selection methods so as to improve document clustering, memetic hybridization between filter and wrapper feature selection, known as Memetic Algorithm Feature Selection, was presented first. In order to deal with the unlabelled features, unsupervised feature selection method was also proposed. The proposed unsupervised feature selection method integrates Simulated Annealing to the global search using Differential Evolution. This combination also aims to combine the advantages of both the wrapper and filter methods in a memetic scheme but on an unsupervised basis. Two versions of this hybridization were proposed. The first was named Differential Evolution Simulated Annealing, which uses the standard mutation of Differential Evolution, and the second was named Dichotomous Differential Evolution Simulated Annealing, which used the dichotomous mutation of the differential evolution. After feature selection two centroid allocation methods were proposed; the first is the combination of Chaotic Logistic Search and Discrete Differential Evolution global search, which was named Differential Evolution Memetic Clustering (DEMC) and the second was based on using the Gradient search using the k-means as a local search with a modified Differential Harmony global Search. The resulting method was named Memetic Differential Harmony Search (MDHS). In order to intensify the exploitation aspect of MDHS, a binomial crossover was used with it. Finally, the improved method is named Crossover Memetic Differential Harmony Search (CMDHS). The test results using the F-measure, Average Distance of Document to Cluster (ADDC) and the nonparametric statistical tests showed the superiority of the CMDHS over the baseline methods, namely the HS, DHS, k-means and the MDHS. The tests also show that CMDHS is better than the DEMC proposed earlier. Finally the proposed CMDHS was compared with two current state-of-the-art methods, namely a Krill Herd (KH) based centroid allocation method and an Artifice Bee Colony (ABC) based method, and found to outperform these two methods in most cases