Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications

Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications

Jun Chen (The University of Sheffield, UK) and Mahdi Mahfouf (The University of Sheffield, UK)
DOI: 10.4018/978-1-60566-310-4.ch002
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The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to show its appeal to engineering applications. The advantages and disadvantages of the new computing paradigm, compared to other bio-inspired optimization techniques, such as Genetic Algorithms and other evolution computing strategies, are highlighted. Responding to some aforementioned disadvantages, a population adaptive based immune algorithm (PAIA) and its modified version for multi-objective optimization are put forward and discussed. A multi-stage optimization procedure is also proposed in which the first stage can be regarded as a vaccination process. It is argued that PAIA and its variations are the embodiments of some new characteristics which are recognized nowadays as the key to success for any stochastic algorithms dealing with continuous optimization problems, thus breathing new blood into the existing AIS family. The proposed algorithm is compared with the previously established evolutionary based optimization algorithms on ZDT and DTLZ test suites. The promising results encourage us to further extract a general framework from the PAIA as the guild to design immune algorithms. Finally, a real-world engineering problem relating to the building of a transparent fuzzy model for alloy steel is presented to show the merits of the algorithm.
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Bio-Inspired Computing lies within the realm of Natural Computing, a field of research that is concerned with both the use of biology as an inspiration for solving computational problems and the use of the natural world experiences to solve real world problems. The increasing interest in this field lies in the fact that nowadays the world is facing more and more complex, large, distributed and ill-structured systems, while on the other hand, people notice that the apparently simple structures and organizations in nature are capable of dealing with most complex systems and tasks with ease. Artificial Immune Systems (AIS) is one among such computing paradigms, which has been receiving more attention recently.

AIS is relatively a new research area which can be traced back to Farmer et al.’s paper published in 1986 (Farmer, J. D. & Packard, N. H., 1986). In this pioneering paper the author proposed a dynamical model for the immune systems based on the Clonal Selection Principle (Bernet, F. M, 1959) and Network Hypothesis (Jerne, N. K., 1974; Perelson, A. S., 1989). However, there were only a few developments since then until 1996 when the first international conference based on artificial immune systems was held in Japan. Following this event, the increasing number of researchers involved in this field indicated the emergence of the new research field: Artificial Immune Systems. But hitherto, no new formal framework based on AIS has been proposed.

There are three main application domains which AIS research effort has focused on, viz. fault diagnosis, computer security, and data analysis. The reason behind this is that it is relatively easy to create a direct link between the real immune system and the aforementioned three application areas, e.g. in the applications of data analysis, clusters to be recognized are easily related to antigens, and the set of solutions to distinguish between these clusters is linked to antibodies. Recently, a few attempts to extend AIS to the optimisation field have been made (de Castro & Von Zuben, 2002; Kelsey, J. & Timmis, J., 2003). However, as mentioned by Emma Hart and Jonathan Timmis (2005), maybe by historic accident, many of the AIS practitioners arrive in the optimisation field by way of working in other biologically inspired fields such as Evolutionary Computing (EC), and thus in terms of optimisation the distinctive line between EC and AIS is vague. In other words, there is not a formal distinctive framework for AIS applied to optimisation. The situation is even worse when it comes to multi-objective optimisation (MOP) case since it is hard to find a way to define Antigen and the affinity due to the implicit Antigen population to be recognized (Chen J. & Mahfouf, M., 2006). Based on such an understanding, this chapter will present a systematic AIS framework to solve MOP with clear definitions and roles of the immune metaphors to be employed, and will highlight the difference between AIS and traditional EC to finally discover the extra advantages which are exclusively inherent in AIS.

Key Terms in this Chapter

Antigen (Ag): Ag is the problem to be optimized.

Transparency of the Fuzzy Model: A fuzzy model is regarded as having a better transparency if it contains less fuzzy rules, less fuzzy sets and less overlapped fuzzy sets

Pareto Front: The plot of the objective functions whose non-dominated vectors are in the Pareto optimal set is called the Pareto front.

Ab-Ab Suppression (Abs’ suppression/Network suppression): When two Abs are very close to each other, they can be recognized by each other. The result is that one of them is suppressed and deleted. Unlike Abs’ affinity, this term is defined as the Euclidian distance in the objective space

Antibody (Ab): Ab is the candidate solutions of the problem to be optimized.

Ab-Ab Affinity (Abs’ affinity): Is defined as the distance (refer to Eqs. (3)) in the decision variable space between one randomly chosen Ab in the first non-dominated front and the one in the remaining population.

Ag-Ab Affinity: For SOP, it is defined as the objective value (fitness value) for MOP, it is determined by using non-dominance concept, i.e. solutions in the first non-dominated front have the highest affinity, then the second front and so on

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Editorial Advisory Board
Table of Contents
Lipo Wang
Hongwei Mo
Chapter 1
Fabio Freschi, Carlos A. Coello Coello, Maurizio Repetto
This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be... Sample PDF
Multiobjective Optimization and Artificial Immune Systems: A Review
Chapter 2
Jun Chen, Mahdi Mahfouf
The primary objective of this chapter is to introduce Artificial Immune Systems (AIS) as a relatively new bio-inspired optimization technique and to... Sample PDF
Artificial Immune Systems as a Bio-Inspired Optimization Technique and Its Engineering Applications
Chapter 3
Licheng Jiao, Maoguo Gong, Wenping Ma
Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection... Sample PDF
An Artificial Immune Dynamical System for Optimization
Chapter 4
Malgorzata Lucinska, Slawomir T. Wierzchon
Multi-agent systems (MAS), consist of a number of autonomous agents, which interact with one-another. To make such interactions successful, they... Sample PDF
An Immune Inspired Algorithm for Learning Strategies in a Pursuit-Evasion Game
Chapter 5
Luis Fernando Niño Vasquez, Fredy Fernando Muñoz Mopan, Camilo Eduardo Prieto Salazar, José Guillermo Guarnizo Marín
Artificial Immune Systems (AIS) have been widely used in different fields such as robotics, computer science, and multi-agent systems with high... Sample PDF
Applications of Artificial Immune Systems in Agents
Chapter 6
Xingquan Zuo
Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling... Sample PDF
An Immune Algorithm Based Robust Scheduling Methods
Chapter 7
Fabio Freschi, Maurizio Repetto
The increasing cost of energy and the introduction of micro-generation facilities and the changes in energy production systems require new... Sample PDF
Artificial Immune System in the Management of Complex Small Scale Cogeneration Systems
Chapter 8
Krzysztof Ciesielski, Mieczyslaw A. Klopotek, Slawomir T. Wierzchon
In this chapter the authors discuss an application of an immune-based algorithm for extraction and visualization of clusters structure in large... Sample PDF
Applying the Immunological Network Concept to Clustering Document Collections
Chapter 9
Xiangrong Zhang, Fang Liu
The problem of feature selection is fundamental in various tasks like classification, data mining, image processing, conceptual learning, and so on.... Sample PDF
Feature Selection Based on Clonal Selection Algorithm: Evaluation and Application
Chapter 10
Yong-Sheng Ding, Xiang-Feng Zhang, Li-Hong Ren
Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network... Sample PDF
Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet
Chapter 11
Tao Gong
Static Web immune system is an important applicatiion of artificial immune system, and it is also a good platform to develop new immune computing... Sample PDF
A Static Web Immune System and Its Robustness Analysis
Chapter 12
Alexander O. Tarakanov
Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal... Sample PDF
Immunocomputing for Spatio-Temporal Forecast
Chapter 13
Fu Dongmei
In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object... Sample PDF
Research of Immune Controllers
Chapter 14
Xiaojun Bi
In fact, image segmentation can be regarded as a constrained optimization problem, and a series of optimization strategies can be used to complete... Sample PDF
Immune Programming Applications in Image Segmentation
Chapter 15
Xin Wang, Wenjian Luo, Zhifang Li, Xufa Wang
A hardware immune system for the error detection of MC8051 IP core is designed in this chapter. The binary string to be detected by the hardware... Sample PDF
A Hardware Immune System for MC8051 IP Core
Chapter 16
Mark Burgin, Eugene Eberbach
There are different models of evolutionary computations: genetic algorithms, genetic programming, etc. This chapter presents mathematical... Sample PDF
On Foundations of Evolutionary Computation: An Evolutionary Automata Approach
Chapter 17
Terrence P. Fries
Path planning is an essential component in the control software for an autonomous mobile robot. Evolutionary strategies are employed to determine... Sample PDF
Evolutionary Path Planning for Robot Navigation Under Varying Terrain Conditions
Chapter 18
Konstantinos Konstantinidis, Georgios Ch. Sirakoulis, Ioannis Andreadis
The aim of this chapter is to provide the reader with a Content Based Image Retrieval (CBIR) system which incorporates AI through ant colony... Sample PDF
Ant Colony Optimization for Use in Content Based Image Retrieval
Chapter 19
Miroslav Bursa, Lenka Lhotska
The chapter concentrates on the use of swarm intelligence in data mining. It focuses on the problem of medical data clustering. Clustering is a... Sample PDF
Ant Colonies and Data Mining
Chapter 20
Bo-Suk Yang
This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global... Sample PDF
Artificial Life Optimization Algorithm and Applications
Chapter 21
Martin Macaš, Lenka Lhotská
A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of... Sample PDF
Optimizing Society: The Social Impact Theory Based Optimizer
Chapter 22
James F. Peters, Shabnam Shahfar
The problem considered in this chapter is how to use the observed behavior of organisms as a basis for machine learning. The proposed approach for... Sample PDF
Ethology-Based Approximate Adaptive Learning: A Near Set Approach
Chapter 23
Dingju Zhu
Parallel computing is more and more important for science and engineering, but it is not used so widely as serial computing. People are used to... Sample PDF
Nature Inspired Parallel Computing
Chapter 24
Tang Mo, Wang Kejun, Zhang Jianmin, Zheng Liying
An understanding of the human brain’s local function has improved in recent years. But the cognition of human brain’s working process as a whole is... Sample PDF
Fuzzy Chaotic Neural Networks
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