3 research outputs found
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
Current Studies and Applications of Krill Herd and Gravitational Search Algorithms in Healthcare
Nature-Inspired Computing or NIC for short is a relatively young field that
tries to discover fresh methods of computing by researching how natural
phenomena function to find solutions to complicated issues in many contexts. As
a consequence of this, ground-breaking research has been conducted in a variety
of domains, including synthetic immune functions, neural networks, the
intelligence of swarm, as well as computing of evolutionary. In the domains of
biology, physics, engineering, economics, and management, NIC techniques are
used. In real-world classification, optimization, forecasting, and clustering,
as well as engineering and science issues, meta-heuristics algorithms are
successful, efficient, and resilient. There are two active NIC patterns: the
gravitational search algorithm and the Krill herd algorithm. The study on using
the Krill Herd Algorithm (KH) and the Gravitational Search Algorithm (GSA) in
medicine and healthcare is given a worldwide and historical review in this
publication. Comprehensive surveys have been conducted on some other
nature-inspired algorithms, including KH and GSA. The various versions of the
KH and GSA algorithms and their applications in healthcare are thoroughly
reviewed in the present article. Nonetheless, no survey research on KH and GSA
in the healthcare field has been undertaken. As a result, this work conducts a
thorough review of KH and GSA to assist researchers in using them in diverse
domains or hybridizing them with other popular algorithms. It also provides an
in-depth examination of the KH and GSA in terms of application, modification,
and hybridization. It is important to note that the goal of the study is to
offer a viewpoint on GSA with KH, particularly for academics interested in
investigating the capabilities and performance of the algorithm in the
healthcare and medical domains.Comment: 35 page
Metaheurísticas, optimización multiobjetivo y paralelismo para descubrir motifs en secuencias de ADN
La resolución de problemas complejos mediante técnicas evolutivas es uno de los aspectos más investigados en Informática.
El objetivo principal de esta tesis doctoral es desarrollar nuevos algoritmos capaces de resolver estos problemas con el menor tiempo computacional posible, mejorando la calidad de los resultados obtenidos por los métodos ya existentes. Para ello, combinamos tres conceptos importantes: metaheurísticas, optimización multiobjetivo y paralelismo. Con este fin, primero buscamos un problema de optimización importante que aún no fuese resuelto de forma eficiente y encontramos el Problema del Descubrimiento de Motifs (PDM). El PDM tiene como objetivo descubrir pequeños patrones repetidos (motifs) en conjuntos de secuencias de ADN que puedan poseer cierto significado biológico. Para abordarlo, definimos una formulación multiobjetivo adecuada a los requerimientos del mundo real, implementamos un total de diez algoritmos de distinta naturaleza (población, trayectoria, inteligencia colectiva...), analizando aspectos como la capacidad de escalar y converger. Finalmente, diseñamos diversas técnicas paralelas, haciendo uso de entornos de programación como OpenMP y MPI, que tratan de combinar las propiedades de varias metaheurísticas en una única aplicación. Los resultados obtenidos son estudiados en detalle a través de la aplicación de numerosos test estadísticos, y las predicciones son comparadas con las descubiertas por un total de trece herramientas biológicas bien conocidas en la literatura.
Las conclusiones obtenidas demuestran que la utilización de la optimización multiobjetivo en técnicas metaheurísticas favorece el descubrimiento de soluciones de calidad y que el paralelismo es útil para combinar las propiedades evolutivas de diferentes algoritmos.The resolution of complex problems by using evolutionary algorithms is one of the most researched issues in Computer Science.
The main goal of this thesis is directly related with the development of new algorithms that can solve this kind of problems with the least possible computational time, improving the results achieved by the existing methods. To this end, we combine three important concepts: metaheuristics, multiobjective optimization, and parallelism. For doing this, we first look for a significant optimization problem that had not been solved in an efficient way and we find the Motif Discovery Problem (MDP). MDP aims to discover over-represented short patterns (motifs) in a set of DNA sequences that may have some biological significance. To address it, we defined a multiobjective formulation adjusted to the real-world biological requirements, we implemented a total of ten algorithms of different nature (population, trajectory, collective intelligence...), analyzing aspects such as the ability to scale and converge. Finally, we designed parallel techniques, by using parallel and distributed programming environments as OpenMP and MPI, which try to combine the properties of several metaheuristics in a single application. The obtained results are discussed in detail through numerous statistical tests, and the achieved predictions are compared with those discovered by a total of thirteen well-known biological tools.
The drawn conclusions demonstrate that using multiobjective optimization in metaheuristic techniques favors the discovery of quality solutions, and that parallelism is useful for combining the properties of different evolutionary algorithms.Ministerio de Economía y Competitividad - FEDER (TIN2008-06491-C04-04; TIN2012-30685)
Gobierno de Extremadura (GR10025-TIC015