Alma Y. Alanis - Bio-inspired Algorithms for Engineering

Alma Y. Alanis

Nancy Arana-Daniel

Carlos Lpez-Franco Guadalajara, Jalisco, Mexico

Bio-inspired algorithms have become an important research area due to intent to emulate nature in order to help solve real-life complex problems, particularly for optimization. Due to the high level of enthusiasm generated by successful applications, the use of bio-inspired algorithms to solve complex optimization problems in a heuristic way has become a well-established methodology. This book proposes novel algorithms, including combined well-known bio-inspired algorithms, to solve real-life complex problems in classification, approximation, vision, pattern recognition, identification, and control. Rigorous analyses as well as unique applications of these algorithms are also presented.

Most research in this field has two main focuses: one dedicated to solve academic problems (complex but only with academic meaning) and the second, to develop more bio-inspired algorithms. The work is intended to alleviate well-known problems such as slow convergence, stagnation in local minimal, and high computational complexity, among others.

In this book authors present a set of real-life bio-inspired algorithms applications, including intelligent pattern recognition, object reconstruction, robot control and vision, intelligent identification, and control of nonlinear systems. Nevertheless, the main drawback of this book is that the proposed methodologies are designed to deal with a wide range of engineering problems and not limited to applications selected to show their effectiveness. The proposed applications of each methodology, however, is at the level of state of the art of their respective field. The wide range of considered applications shows the capacity of bio-inspired algorithms to solve real-life problems.

The main goal of this book is to facilitate the application of many proposed bio-inspired algorithms developed in last few decades to real-life problems and not limit the same to an academic context. To achieve this, the book covers both theoretical and practical methodologies to allow readers greater appreciation regarding the implementation of bio-inspired algorithms.

The vast majority of other books related to bio-inspired algorithms and a great number of scientific papers only show the respective algorithms, pseudo-codes, flux diagrams, etc. but do not include real-life problems; they are limited to showing the effectiveness of bio-inspired algorithms to solve academic problems, typically benchmark functions. In this book the algorithms are presented in a rather friendly manner giving more emphasis to real-life applications and their implementation without neglecting their mathematical foundations, including both simulation and experimental results. The book also contains rigorous analysis of the proposed and/or used bio-inspired algorithms in addition to fundamental aspects of the proposed topics, development, variants, and modifications.

This book is organized as follows:

In Chapter bio-inspired algorithms are introduced, and the algorithms used in the book are presented. The latter include Particle Swarm Optimization (PSO), Artificial Bee Colony Algorithm (ABC), Micro Artificial Bee Colony Algorithm (ABC), Differential Evolution (DE), and Bacterial Foraging Optimization Algorithm (BFO).