Special section: “Soft-computing and advanced techniques in new algorithmic approaches to existing application areas” (original) (raw)
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Complex Systems Modeling with Cellular Automata and Genetic Algorithms: An Application to Lava Flows
2008
Cellular Automata are parallel computational models which are capable to give rise to heterogeneous emergent behaviors notwithstanding simple local rules of evolution. In this review paper, a methodology for modeling complex natural systems through Macroscopic Cellular Automata is presented and applied to lava flow simulation. In particular, the 2001 Mt. Etna volcano Nicolosi (Italy) case study has been considered for model calibration, while the validation has been performed by considering further cases of study, which differ both in duration and emission rate. Parameter optimization was carried out by a Parallel Master-Slave Genetic Algorithm. Results have confirmed both the goodness of the simulation model and of the calibration algorithm. Eventually an application related to Civil Defense purposes is briefly described and proposed as a development.
Applications of Soft Computing Methods in Environmental Engineering
Handbook of Environmental Materials Management, 2019
Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In environmental engineering, researchers and engineers have successfully employed different methods of soft computing for modeling of various real-life environmental problems. In this study, applications of core soft computing techniques, such as artificial neural networks (ANN), fuzzy logic (FL), adaptive neuro fuzzy inference systems (ANFIS), and support vector machines (SVM), are investigated and important mathematical aspects of these methods are highlighted. Considering the concepts and methods, this study briefly reviews applications of soft computing techniques in the field of environmental engineering, especially in water/wastewater treatment and air quality/pollution control/forecasting. A brief introduction to complexity of environmental problems and the general definition soft computing concept are presented in the first section of this chapter. The second section comprises four subsections and presents mathematical background of four different soft computing methods. Section "Implementation of Soft Computing Methods in Environmental Engineering", which is consisted of eight subsections, reviews successful applications of soft computing-based prediction models implemented in the field of environmental engineering and summarizes the important findings obtained in these studies. At the end of the overview of the published works on soft computing applications in different environmental areas, in the last section, some special illustrative soft computing examples and the respective MATLAB ®-based solutions are presented for environmental engineers.
2007
Forecasting through simulations the shape of lava invasions in a real topography represents a challenging problem, especially considering that the phenomenon usually evolves for a long time (e.g. from a few to hundreds of days) and on very large areas. In the latest years, Cellular Automata (CA) have been well recognized as a valid computational approach in lava flow modelling. In this paper we present some significant developments of SCIARA, a family of deterministic CA models of lava flows which are optimized for a specific scenario through the use of a parallel genetic algorithm. Following a calibration-validation approach, the model outcomes are compared with three real events of lava effusion.
An Introduction to Soft Computing Techniques in Water Resources System
The field of engineering is a creative one. The problems encountered in this field are generally unstructured, imprecise and influenced by intuitions and past experiences of a designer. The conventional methods of computing, relying on analytical or empirical relations, become time consuming and labor intensive when posed with real life problems. To study, model and analyze such problems, approximate computer based Soft Computing techniques inspired by the reasoning, intuition, consciousness and wisdom possessed by human beings are employed. In contrast to conventional computing techniques, which rely on exact solutions, soft computing aims at exploiting given tolerance of imprecision, the trivial and uncertain nature of the problem to yield an approximate solution to a problem in quick time. Soft Computing being a multi-disciplinary field uses a variety of statistical, probabilistic and optimization tools which complement each other to produce its three main branches viz. Neural Networks, Genetic Algorithms and Fuzzy Logic. The applications of two major soft computing techniques viz. , Artificial Neural Networks and Genetic Algorithms, has replaced, to some extent, the time consuming conventional techniques of computing with intelligent and time saving computing tools in the field of Civil Engineering. In simple understanding, by using soft computing techniques, some solution, less than the perfect solution, can be arrived at; which may be quite close to the desired solution or lie within the acceptable limits of error. The field of water resources has a lot of potential application of soft computing. Various such techniques have been used to model pollution transport in streams, optimum water allocation, and others.
Recent Trends in Soft Computing Techniques for Solving Real Time Engineering Problems
2014
In recent times, engineers have very well accepted soft computing techniques such as fuzzy sets theory, neural nets, neuro fuzzy system, adaptive neuro fuzzy inference system (ANFIS), coactive neuro fuzzy inference system (CANFIS), evolutionary computing, probabilistic computing, Computational intelligence (CI), etc. for carrying out varying numerical simulation analysis. In last two decades, these techniques have been successfully applied in various engineering problems independently or in hybrid forms. The main objective of this paper is to introduce engineers and students about the latest trends in soft computing. Also they will help young researchers to develop themselves in futures.In recent years Computational intelligence (CI) has gained a widespread concern of many scholars emerging as a new field of study. CI actually uses the bionics ideas for reference, it origins from emulating intelligent phenomenon in nature. CI attempts to simulate and reappearance the characters of intelligence, such as learning and adaptation, so that it can be a new research domain for reconstructing the nature and engineering. The essence of CI is a universal approximator, and it has the great function of non-linear mapping and optimization.In this paper we give an overview of intelligent systems. We discuss the notion itself, together with diverse features and constituents of it. We concentrate especially on computational intelligence and soft computing.
Soft Computing Techniques in Various Areas
2020
Soft computing is a study of the science of logic, thinking, analysis and research that combines real-world problems with biologically inspired methods. Soft computing is the main motivation behind the idea of conceptual intelligence in machines. As such, it is an extension of heuristics and the resolution of complex problems that are very difficult to model mathematically. Smooth computing tolerates printing; uncertainty and approximation that differ from manual calculation. Soft Computing enumerates techniques like ANN, Evolutionary computing, Fuzzy Logic and statistics, they are advantageous and separately applied techniques which are used together to solve problems which are complex, very easily. This article highlights the various soft computing ting techniques and emerging areas of soft computing ting where they have been successfully implemented.
Recent Trends and Applications of Soft Computing: A Survey
2013
This paper is survey on the development of soft computing applications in various domains. Specifically, it briefly reviews main approaches of soft computing (in the wide sense) , the more recent development of soft computing, and finalise by presenting a panoramic view of applications: from the most abstract to the most practical ones. Within this context, fuzzy logic (FL), genetic algorithms (GA) and artificial neural networks (ANN), as well as their fusion are reviewed in order to examine the capability of soft computing methods and techniques to effectively address various hard-to-solve design tasks and issues. This paper presents applications of using different Soft Computation methods in both industrial, biological processes, in engineering design, in investment and financial Trading. It analyses the literature according to the style of soft computing used, the investment discipline used, the successes demonstrated, and the applicability of the research to real world trading.
Soft computing and its applications
Fuzzy Sets and Systems, 2004
Soft Computing is a relatively new computing paradigm bestowed with tools and techniques for handling real world problems. The main components of this computing paradigm are neural networks, fuzzy logic and evolutionary computation. Each and every component of the soft computing paradigm operates either independently or in coalition with the other components for addressing problems related to modeling, analysis and processing of data. An overview of the essentials and applications of the soft computing paradigm is presented in this chapter with reference to the functionalities and operations of its constituent components. Neural networks are made up of interconnected processing nodes/neurons, which operate on numeric data. These networks posses the capabilities of adaptation and approximation. The varied amount of uncertainty and ambiguity in real world data are handled in a linguistic framework by means of fuzzy sets and fuzzy logic. Hence, this component is efficient in understanding vagueness and imprecision in real world knowledge bases. Genetic algorithms, simulated annealing algorithm and ant colony optimization algorithm are representative evolutionary computation techniques, which are efficient in deducing an optimum solution to a problem, thanks to the inherent exhaustive search methodologies adopted. Of late, rough sets have evolved to improve upon the performances of either of these components by way of approximation techniques. These soft computing techniques have been put to use in wide variety of problems ranging from scientific to industrial applications. Notable among these applications include image processing, pattern recognition, Kansei information processing, data mining, web intelligence etc.