Название: Computational Intelligence: Theoretical Advances and Advanced ApplicationsАвтор: Dinesh C.S. Bisht, Mangey Ram
Издательство: De Gruyter
Год: 2020
Страниц: 260
Язык: английский
Формат: epub
Размер: 15.0 MB
Computational Intelligence (CI) lies at the interface between engineering and Computer Science; control engineering, where problems are solved using computer-assisted methods. Thus, it can be regarded as an indispensable basis for all Artificial Intelligence (AI) activities. This book collects surveys of most recent theoretical approaches focusing on fuzzy systems, neurocomputing, and nature inspired algorithms. It also presents surveys of up-to-date research and application with special focus on fuzzy systems as well as on applications in life sciences and neuronal computing.
In this modern era, we need instruments that can be applied to the problems arising in interdisciplinary fields. In this sense, Computational Intelligence (CI) is a developing computing approach for the forthcoming several years. CI gives the litheness to model the problem according to given requirements. It helps to find swift solutions to the problems arising in numerous disciplines. These methods mimic human behavior. The origins of CI are found in fuzzy logic, data analysis and intelligent systems. CI constitutes of following three main parts:
A. Fuzzy systems
B. Neurocomputing
C. Nature-inspired algorithms
Fuzzy logic has the capacity to cope up with imprecise data, linguistic terminology or vague form of the information. Neural networks can learn with examples and can produce extraordinary results. Nature-inspired algorithms are inspired by nature’s way of adaptation. This book aims to cover recent advancements in the field of CI. It will cover the theoretical as well as the application part of the computational intelligence. The projected audience for this book will be scientists, researchers and postgraduate students who can use these techniques directly as black-box or can modify according to their research problems.
The above-said algorithm is widely used in various problem in different field. Except these also some additional bio-inspired algorithms are investigated by the researcher like artificial fish-swarm algorithm (AFSA), termite algorithm, group search optimizer (GSO), flower pollination algorithm (FPA), fruit fly optimization algorithm (FFOA), krill herd (KH), cuckoo optimization algorithm (COA), dolphin echolocation, grey wolf optimizer (GWO), artificial algae algorithm (AAA), ant lion optimizer (ALO), shark smell optimization (SSO), dolphin swarm optimization algorithm (DSOA), grasshopper optimization algorithm (GOA), selfish herd optimizer (SHO), Symbiotic Organisms Search, spotted hyena optimizer, lion’s Algorithm, lion optimization algorithm (LOA), cat swarm optimization (CSO), virus colony search (VCS), coral reefs optimization (CRO) algorithm, whale optimization algorithm (WOA), mouth brooding fish algorithm and competitive optimization algorithm (COOA).
Also, there are some well-known nature-inspired algorithms not inspired by biological species. Some of them are simulated annealing (SA), differential evolution (DE), gravitational search algorithm (GSA), mine blast algorithm (MBA), lightning search algorithm (LSA), exchange market algorithm (EMA), atom search optimization (ASO), volleyball premier league (VPL) algorithm, tree growth algorithm (TGA), multiverse optimizer (MVO), electrosearch (ES) algorithm, thermal exchange optimization, water wave optimization (WWO), weighted superposition attraction (WSA), Lightning Attachment Procedure Optimization (LAPO) and teaching-learning-based optimization algorithm (TLBO).
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