6 research outputs found
Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
Metaheuristic research has proposed promising results in science, business, and engineering problems. But, mostly high-level analysis is performed on metaheuristic performances. This leaves several critical questions unanswered due to black-box issue that does not reveal why certain metaheuristic algorithms performed better on some problems and not on others. To address the significant gap between theory and practice in metaheuristic research, this study proposed in-depth analysis approach using component-view of metaheuristic algorithms and diversity measurement for determining exploration and exploitation abilities. This research selected three commonly used swarm-based metaheuristic algorithms – Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Cuckoo Search (CS) – to perform component-wise analysis. As a result, the study able to address premature convergence problem in PSO, poor exploitation in ABC, and imbalanced exploration and exploitation issue in CS. The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. The proposed iPSO, iABC, and iCS were then employed on proposed novel Fuzzy-Meta Classifier (FMC) which offered highly reduced model complexity and high accuracy as compared to Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed three-layer FMC produced efficient rules that generated nearly 100% accuracies on ten different classification datasets, with significantly reduced number of trainable parameters and number of nodes in the network architecture, as compared to ANFIS
An assessment of the component-based view for metaheuristic research.
Masters Degree. University of KwaZulu-Natal, Durban.Several authors have recently pointed to a crisis within the metaheuristic research field,
particularly the proliferation of metaphor-inspired metaheuristics. Common problems identified
include using non-standard terminology, poor experimental practices, and, most importantly,
the introduction of purportedly new algorithms that are only superficially different from
existing ones. These issues make similarity and performance analysis, classification, and
metaheuristic generation difficult for both practitioners and researchers. A component-based
view of metaheuristics has recently been promoted to deal with these problems. A component
based view argues that metaheuristics are best understood in terms of their constituents or
components. This dissertation presents three papers that are thematically centred on this view.
The central problem for the component-based view is the identification of components of a
metaheuristic. The first paper proposes the use of taxonomies to guide the identification of
metaheuristic components. We developed a general and rigorous method, TAXONOG-IMC,
that takes as input an appropriate taxonomy and guides the user to identify components. The
method is described in detail, an example application of the method is given, and an analysis of
its usefulness is provided. The analysis shows that the method is effective and provides insights
that are not possible without the proper identification of the components. The second paper
argues for formal, mathematically sound representations of metaheuristics. It introduces and
defends a formal representation that leverages the component based view. The third paper
demonstrates that a representation technique based on a component based view is able to
provide the basis for a similarity measure. This paper presents a method of measuring similarity
between two metaheuristic-algorithms, based on their representations as signal flow diagrams.
Our findings indicate that the component based view of metaheuristics provides valuable
insights and allows for more robust analysis, classification and comparison
The dawning of computational psychoanalysis. A proposal for some first elementary formalization attempts
In this paper, we wish first to highlight, within the general cultural context, some possible elementary computational psychoanalysis formalizations concerning Matte Blanco’s bi-logic components through certain very elementary mathematical tools and notions drawn from theoretical physics and algebra. Afterwards, on the basis of recent work of Giampaolo Sasso (1999; 2005; 2011), relying on the crucial crossroad between neurosciences and psychoanalysis, it will be possible to identify some hints for further formalization attempts turned toward a computational psychoanalysis outlook. Lastly, possible interesting relationships with cognitive informatics are also outlined
Método de reducción de incertidumbre basado en algoritmos evolutivos y paralelismo orientado a la predicción y prevención de desastres naturales
La presente tesis doctoral aborda la problemática de la incertidumbre existente en todo sistema de predicción, focalizando en el desarrollo de métodos de reducción de incertidumbre aplicados a la predicción de fenómenos naturales. Debido a que estos fenómenos suelen causar gran impacto en las comunidades, la flora y la fauna, el ecosistema, entre otros, los sistemas de predicción deben proporcionar respuesta en el menor tiempo posible. Por estos motivos, los métodos propuestos han sido desarrollados utilizando capacidades de alto rendimiento. El primer método desarrollado en esta tesis (ESS-IM), comenzó con el objetivo de lograr una mejora a una metodologÃa previamente desarrollada denominada ESS (Sistema EstadÃstico Evolutivo).
EspecÃficamente se trabajó en el incremento del paralelismo de la metaheurÃstica interna, incorporando una arquitectura basada en modelo de islas bajo un esquema de migración. Este desarrollo logró incrementar la capacidad de búsqueda de la metaheurÃstica interna, impactando de forma directa en un incremento en la calidad de predicción del método. En la validación, ESS-IM fue aplicado en una serie de casos de quemas controladas e incendios forestales. Es importante destacar que, en forma conjunta, al desarrollo de la tesis, se llevaron a cabo diferentes investigaciones complementarias, tales como: estudios de sintonización de parámetros, desarrollo de un sistema de generación de mapas de incendios forestales a partir de imágenes satelitales, entre otros. Finalmente, en la última etapa de la tesis, se implementó una versión hÃbrida basada en metaheurÃsticas evolutivas bajo una estrategia colaborativa basada en islas. El método HESS-IM, se implementó de forma heterogénea (a nivel de hardware), logrando que los resultados obtenidos incrementen la calidad de predicción y eficiencia del método.Tesis doctoral de la Universidad Nacional de San Luis. Grado alcanzado: Doctor en Ciencias Informáticas. Director de tesis: Germán Bianchini. La tesis, presentada en el año 2020, obtuvo el Premio "Dr. Raúl Gallard" en el 2021.Red de Universidades con Carreras en Informátic