Artificial Life Optimization Algorithm and Applications

This chapter describes a hybrid artificial life optimization algorithm (ALRT) based on emergent colonization to compute the solutions of global function optimization problem. In the ALRT, the emergent colony is a fundamental mechanism to search the optimum solution and can be accomplished through the metabolism, movement and reproduction among artificial organisms which appear at the optimum locations in the artificial world. In this case, the optimum locations mean the optimum solutions in the optimization problem. Hence, the ALRT focuses on the searching for the optimum solution in the location of emergent colonies and can achieve more accurate global optimum. The optimization results using different types of test functions are presented to demonstrate the described approach successfully achieves optimum performance. The algorithm is also applied to the test function optimization and optimum design of short journal bearing as a practical application. The optimized results are compared with those of genetic algorithm and successive quadratic programming to identify the optimizing ability.

Research of Immune Controllers

In engineering application, the characteristics of the control system are entirely determined by the system controller once the controlled object has been chosen. Improving the traditional controller or constructing the new controller is an unfading study field of control theory and application. The control system is greatly enriched and developed by this way. As a complicated self-adaptable system, the biological immune system can effectively and smoothly stand against antigens and viruses intruded into organism. It is possible to improve the self-learning, adaptive and robustness capability of the control system through embedded an artificial immune controller in control system. Based on the biological immune mechanism and artificial immune model, this chapter attempts to study the immune controller design and application in traditional control system..First, a kind of artificial immune controller is proposed based on the T-B cells immunity. The boundedness and the stability of SISO control systems, which constructed by the artificial immune controller, are proved by the little gain theorem. A general controller structure frame based on the T-B cells immunity is proposed, which includes the same kind of controller proposed previously. The validity of this artificial immune controller is verified by simulation. Second, a new type of artificial immune controllers is constructed according to a simple double-cell immune dynamics model. The non-error characteristic of SISO control systems, which constructed by the artificial immune controller, is proved by the nonlinear theory in this chapter. The I/O stability and no-error characteristic of the system are verified by simulations, which show that the kind of artificial immune system have good anti-lag capability. Third, the Varela immune network model has been improved based on which an artificial immune system is proposed. The odd linearization method of the non-linear system is used to prove the stability and non-error characteristic of the SISO system constructed by the artificial immune control system. Its I/O stability, non-error characteristic and strong anti-lag capability are also verified by simulation. Finally, based on the comparison of the three kinds of immune controllers, a general structure of the artificial immune controller is proposed. The further study on this field is indicated in this chapter lastly.

Immune Based Bio-Network Architecture and its Simulation Platform for Future Internet

Future Internet should be capable of extensibility, survivability, mobility, and adaptability to the changes of different users and network environments, so it is necessary to optimize the current Internet architecture and its applications. Inspired by the resemble features between the immune systems and future Internet, the authors introduce some key principles and mechanisms of the immune systems to design a bio-network architecture to address the challenges of future Internet. In the bio-network architecture, network resources are represented by various bioentities, while complex services and application can be emerged from the interactions among bio-entities. Also, they develop a bio-network simulation platform which has the capability of service emergence, evolution, and so forth. The simulation platform can be used to simulate some complex services and applications for Internet or distributed network. The simulators with different functions can be embedded in the simulation platform. As a demonstration, this chapter provides two immune network computation models to generate the emergent services through computer simulation experiments on the platform. The experimental results show that the bio-entities on the platform provide quickly services to the users’ requests with short response time. The interactions among bio-entities maintain the load balance of the bio-network and make the resources be utilized reasonably. With the advantages of adaptability, extensibility, and survivability, the bio-network architecture provides a novel way to design new intelligent Internet information services and applications.

An Artificial Immune Dynamical System for Optimization

Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection, positive selection, rather than the whole process of immune response to solve computational problems. In order to build a general computational framework by simulating immune response process, this chapter introduces a population-based artificial immune dynamical system, termed as PAIS, and applies it to numerical optimization problems. PAIS models the dynamic process of human immune response as a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. Some general descriptions of reaction rules, including the set of clonal selection rules and the set of immune memory rules are introduced in PAIS. Based on these reaction rules, a dynamic algorithm, termed as PAISA, is designed for numerical optimization. In order to validate the performance of PAISA, 9 benchmark functions with 20 to 10,000 dimensions and a practical optimization problem, optimal approximation of linear systems are solved by PAISA, successively. The experimental results indicate that PAISA has high performance in optimizing some benchmark functions and practical optimization problems.

Nature Inspired Parallel Computing

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 serial computing and feel parallel computing too difficult to understand, design and use. In fact, they are most familiar with nature, in which all things exist and go on in parallel. If one learns parallel computing before learning serial computing, even if he or she has not read this chapter, they can find that serial computing is more difficult to understand, design and use than parallel computing, for it is not running in the way as the nature we are familiar with. Nature is composed of a large number of objects and events. Events are the spirit of objects; objects the body of events. They are related with each other in nature. Objects can construct or exist in parallel and events can occur or go on in parallel. The parallelism mainly exists in four dimensions including space dimension, application dimension, time dimension, and user dimension. After reading this chapter, even if you have been used to serial computing, you can find that the parallel computing used in your applications is just from nature. This chapter illustrates NIPC (Nature Inspired Parallel Computing) and its applications to help you grasp the methods of applying NIPC to your applications. The authors hope to help you understand and use parallel computing more easily and design and develop parallel software more effectively.

Optimizing Society

A novel binary optimization technique is introduced called Social Impact Theory based Optimizer (SITO), which is based on social psychology model of social interactions. The algorithm is based on society of individuals. Each individual holds a set of its attitudes, which encodes a candidate solution of a binary optimization problem. Individuals change their attitudes according to their spatial neighbors and their fitness, which leads to convergence to local (or global) optimum. This chapter also tries to demonstrate different aspects of the SITO’s behavior and to give some suggestions for potential user. Further, a comparison to similar techniques – genetic algorithm and binary particle swarm optimizer – is discussed and some possibilities of formal analysis are briefly presented.

A Hardware Immune System for MC8051 IP Core

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 immune system is made from the concatenation of the PC values in two sequential machine cycles of the MC8051. When invalid PC transitions occurred in the MC8051, the alarm signal of the hardware immune system can be activated by the detector set. The hardware immune system designed in this chapter is implemented and tested on an FPGA development board, and the result is given in waveforms of the implemented circuits. The disadvantages and future works about the system are also discussed.

Immunocomputing for Spatio-Temporal Forecast

Based on mathematical models of immunocomputing, this chapter describes an approach to spatio-temporal forecast (STF) by intelligent signal processing. The approach includes both low-level feature extraction and highlevel (“intelligent”) pattern recognition. The key model is the formal immune network (FIN) including apoptosis (programmed cell death) and immunization both controlled by cytokines (messenger proteins). Such FIN can be formed from raw signal using discrete tree transform (DTT), singular value decomposition (SVD), and the proposed index of inseparability in comparison with the Renyi entropy. Real-world application is demonstrated on data of space monitoring of the Caspian, Black, and Barents Sea. A surprising result is strong negative correlation between anomalies of sea surface temperature (SST) and sunspot number (SSN). This effect can be utilized for long-term STF.

An Immune Algorithm Based Robust Scheduling Methods

Inspired from the robust control principle, a robust scheduling method is proposed to solve uncertain scheduling problems. The uncertain scheduling problem is modeled by a set of workflow simulation models, and then a scheduling scheme (solution) is evaluated by the results of workflow simulations that are executed by using the workflow models in the set. A variable neighborhood immune algorithm (VNIA) is used to obtain an optimal robust scheduling scheme that has good performances for each model in the model set. The detailed steps of optimizing robust scheduling scheme by the VNIA are given. The antibody coding and decoding schemes are also designed to deal with resource conflicts during workflow simulation processes. Experimental results show that the proposed method can generate robust scheduling schemes that are insensitive for uncertain disturbances of scheduling environments.

Multiobjective Optimization and Artificial Immune Systems

This chapter aims to review the state of the art in algorithms of multiobjective optimization with artificial immune systems (MOAIS). As it will be focused in the chapter, Artificial Immune Systems (AIS) have some intrinsic characteristics which make them well suited as multiobjective optimization algorithms. Following this basic idea, different implementations have been proposed in the literature. This chapter aims to provide a thorough review of the literature on multiobjective optimization algorithms based on the emulation of the immune system.