A Brief Analysis of Gravitational Search Algorithm (GSA) Publication from 2009 to May 2013

Gravitational Search Algorithm was introduced in year 2009. Since its introduction, the academic community shows a great interest on this algorith. This can be seen by the high number of publications with a short span of time. This paper analyses the publication trend of Gravitational Search Algorithm since its introduction until May 2013. The objective of this paper is to give exposure to reader the publication trend in the area of Gravitational Search Algorithm

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure

Gravity Theory-Based Affinity Propagation Clustering Algorithm and Its Applications

The original Affinity Propagation clustering algorithm (AP) only used the Euclidean distance of data sample as the only standard for similarity calculation. This method of calculation had great limitations for data with high dimension and sparsity when the original algorithm was running. Due to the single calculation method of similarity, the convergence and clustering accuracy of the algorithm were greatly affected. On the other hand, in the universe, we can consider the formation of galaxies is a clustering process. In addition, the interaction between different celestial bodies are achieved through universal gravitation. This paper introduced the Density Peak clustering algorithm (DP) and gravitational thought into the AP algorithm, and constructed the density property to calculate the similarity, put forward the Affinity Propagation clustering algorithm based on Gravity (GAP). The proposed algorithm was more accurate to calculate similarity of simple points through the local density of corresponding points, and then used the gravity formula to update the similarity matrix. The data clustering process could be seen as the sample points spontaneously attract each other based on ‘gravitation’. Experimental results showed that the convergence performance of GAP algorithm is obviously improved over the AP algorithm, and the clustering effect was better

A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.Comment: 48 pages, 14 Tables, 12 Figure

Estudio e implementación de optimización gravitatoria y desarrollo de distintas metaheurísticas generadas a partir de él

Gran cantidad de problemas en la Ciencia y la Tecnología, como el diseño de antenas, de satélites o de sondas espaciales, por citar algunos, se plantean como problemas matemáticos en los que es necesario encontrar el mínimo de una función dependiente de un buen número de parámetros: posición, velocidad, ángulo, etc., en un determinado dominio. En nuestra tesis planteamos el estudio pormenorizado de un algoritmo, "Optimización Gravitatoria", S.G.O., diseñado para encontrar ese mínimo. Comenzamos por realizar un estudio de los fundamentos físicos y matemáticos del algoritmo para, posteriormente analizar su estructura. A continuación "ayudamos" a S.G.O. uniéndolo con otros algoritmos para potenciarlo. Dos de ello: Segmentación y Agujero de Gusano son algoritmos inéditos que han sido diseñados y desarrollados exclusivamente por nosotros. Con ellos hemos obtenido muy buenos resultados en diversas pruebas. Concluimos nuestra investigación probando los distintos algoritmos diseñados con un caso práctico: Casinni 2 que describe la trayectoria real que la homónima sonda realizó en su viaje a Saturno.In this thesis we propose the comprehensive study of the heuristic: "Space Gravitational Optimization", S.G.O., designed for global optimization of continuous functions. We study its foundations and parameters to determine their values universally. We subsequently fulfill our goal of achieving the optimum in 40 benchmark functions, common tests of effectiveness and efficiency in global optimization, with different topologies and sizes between two and thirty, several multimodal. For achieving, we join S.G.O. with algorithms of different nature: local search (Nelder-Mead and Gradient), concentration (Segmentation) and intensification (Worm Hole and Very Simple Optimization). With them metaheuristic of high effectiveness and efficiency are generated. Two of them, Segmentation and Worm Hole, are unpublished, designed and developed by us. Last but not least our work involves several tests of the different metaheuristic generated with a real instance: Cassini 2, representing the actual track made by the eponymous unmanned spacecraft on its journey to Saturn