A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms: Tabular and Graphical Analyses

A Comprehensive Literature Review on Nature-Inspired Soft Computing and Algorithms: Tabular and Graphical Analyses

Bilal Ervural (Istanbul Technical University, Turkey), Beyzanur Cayir Ervural (Istanbul Technical University, Turkey) and Cengiz Kahraman (Istanbul Technical University, Turkey)
DOI: 10.4018/978-1-5225-2128-0.ch002

Abstract

Soft Computing techniques are capable of identifying uncertainty in data, determining imprecision of knowledge, and analyzing ill-defined complex problems. The nature of real world problems is generally complex and their common characteristic is uncertainty owing to the multidimensional structure. Analytical models are insufficient in managing all complexity to satisfy the decision makers' expectations. Under this viewpoint, soft computing provides significant flexibility and solution advantages. In this chapter, firstly, the major soft computing methods are classified and summarized. Then a comprehensive review of eight nature inspired – soft computing algorithms which are genetic algorithm, particle swarm algorithm, ant colony algorithms, artificial bee colony, firefly optimization, bat algorithm, cuckoo algorithm, and grey wolf optimizer algorithm are presented and analyzed under some determined subject headings (classification topics) in a detailed way. The survey findings are supported with charts, bar graphs and tables to be more understandable.
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Soft Computing

In the early 1990s, Zadeh introduced the concept of Soft Computing (SC). The definition of soft computing is ‘a collection of methodologies that aim to exploit tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost’ (Lotfi A. Zadeh, 1994). Soft computing methodologies have been advantageous in many areas. In contrast to hard methods, soft computing methodologies mimic consciousness and cognition in several important respects: they can learn from experience; they can universalize into domains where direct experience is absent; and lastly, they can perform mapping from inputs to the outputs faster than inherently serial analytical representations by means of parallel computer architectures that simulate biological processes (Chaturvedi, 2008).

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