Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Soft Computing

Soft computing is used where a complex problem is not adequately specified for the use of conventional math and computer techniques. Soft computing has numerous real-world applications in domestic, commercial and industrial situations. This book elaborates on the most recent applications in various fields of engineering

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Defuzzification of groups of fuzzy numbers using data envelopment analysis

Defuzzification is a critical process in the implementation of fuzzy systems that converts fuzzy numbers to crisp representations. Few researchers have focused on cases where the crisp outputs must satisfy a set of relationships dictated in the original crisp data. This phenomenon indicates that these crisp outputs are mathematically dependent on one another. Furthermore, these fuzzy numbers may exist as a group of fuzzy numbers. Therefore, the primary aim of this thesis is to develop a method to defuzzify groups of fuzzy numbers based on Charnes, Cooper, and Rhodes (CCR)-Data Envelopment Analysis (DEA) model by modifying the Center of Gravity (COG) method as the objective function. The constraints represent the relationships and some additional restrictions on the allowable crisp outputs with their dependency property. This leads to the creation of crisp values with preserved relationships and/or properties as in the original crisp data. Comparing with Linear Programming (LP) based model, the proposed CCR-DEA model is more efficient, and also able to defuzzify non-linear fuzzy numbers with accurate solutions. Moreover, the crisp outputs obtained by the proposed method are the nearest points to the fuzzy numbers in case of crisp independent outputs, and best nearest points to the fuzzy numbers in case of dependent crisp outputs. As a conclusion, the proposed CCR-DEA defuzzification method can create either dependent crisp outputs with preserved relationship or independent crisp outputs without any relationship. Besides, the proposed method is a general method to defuzzify groups or individuals fuzzy numbers under the assumption of convexity with linear and non-linear membership functions or relationships

A review of application of multi-criteria decision making methods in construction

Construction is an area of study wherein making decisions adequately can mean the difference between success and failure. Moreover, most of the activities belonging to this sector involve taking into account a large number of conflicting aspects, which hinders their management as a whole. Multi-criteria decision making analysis arose to model complex problems like these. This paper reviews the application of 22 different methods belonging to this discipline in various areas of the construction industry clustered in 11 categories. The most significant methods are briefly discussed, pointing out their principal strengths and limitations. Furthermore, the data gathered while performing the paper are statistically analysed to identify different trends concerning the use of these techniques. The review shows their usefulness in characterizing very different decision making environments, highlighting the reliability acquired by the most pragmatic and widespread methods and the emergent tendency to use some of them in combination

Multi-objective Optimization Proposal with Fuzzy Coefficients in both Constraints and Objective Functions.

Propuesta de optimización multiobjetivo con coeficientes difusos en restricciones y en funciones objetivo.AbstractFuzzy sets, and more specifically, fuzzy numbers can be a very suitable way to include uncertainty within the formulation and solution of linear problems with multiple goals. Goals in a decision problem do not need to be either maximized, or minimized, as in classical mathematical programming, but they are substituted by aspiration levels, and they need to be met in order to satisfy the decision-maker. Experience shows that it is easier for the decision-maker to formulate both objectives and constraints with fuzzy coefficients, rather than specify a defined quantity for the matrices A, b or g. This paper shows the versatility of a methodology that solves multi-objective linear problems, formulated with fuzzy coefficients. This conception becomes an alternative in contrast with the hard methodologies predominant in Operations Research, since the fuzzy approach allows the decision-maker to make uncertain assumptions both for the formulation and solution of optimization problems.Keywords: Fuzzy logic, multi-criteria analysis, triangular fuzzy numbers.ResumenLos conjuntos difusos y específicamente los números difusos constituyen una manera efectiva de incluir la incertidumbre en la formulación y solución de problemas lineales de optimización multiobjetivo. Las metas en un problema de decisión no necesitan ser maximizadas ni minimizadas, como ocurre en las herramientas clásicas de programación matemática, sino que se pueden sustituir por niveles de aspiración, las cuales constituyen las expectativas para un decisor. La experiencia demuestra que es más fácil para el decisor formular los objetivos y las restricciones en un problema con coeficientes difusos, en vez de simplemente especificar un número concreto en las matrices A, b ó g. Este artículo presenta la versatilidad de una formulación metodológica que permite resolver problemas multiobjetivo de tipo lineal, los cuales son formulados con coeficientes difusos. Esta concepción constituye una alternativa a las metodologías duras que dominan la investigación de operaciones, dado que la aproximación difusa permite que los decisores realicen presunciones inciertas en la formulación y solución en los problemas de optimización.Palabras Clave: Lógica difusa, análisis multiobjetivo, números triangulares difusos

NEUTROSOPHIC MULTI-OBJECTIVE LINEAR PROGRAMMING

For modeling imprecise and indeterminate data for multi-objective decision making, two different methods: neutrosophic multi-objective linear/non-linear programming neutrosophic goal programming, which have been very recently proposed in the literatuire. In many economic problems, the well-known probabilities or fuzzy solutions procedures are not suitable because they cannot deal the situation when indeterminacy inherently involves in the problem. In this case we propose a new concept in optimization problem under uncertainty and indeterminacy. It is an extension of fuzzy and intuitionistic fuzzy optimization in which the degrees of indeterminacy and falsity (rejection) of objectives and constraints are simultaneously considered together with the degrees of truth membership (satisfaction/acceptance). The drawbacks of the existing neutrosophic optimization models have been presented and new framework of multi-objective optimization in neutrosophic environment has been proposed. The essence of the proposed approach is that it is capable of dealing with indeterminacy and falsity simultaneously