**Part 3**

**Artificial Immune Systems and Swarm Intelligence** 

16 Will-be-set-by-IN-TECH

332 Bio-Inspired Computational Algorithms and Their Applications

Eiben, *A*´. E.; Hinterding, R. & Michalewicz, Z. (1999). Parameter control in evolutionary

Enjeti, P. N. ; Ziogas, P. D. & Lindsay J. F. (1990). Programmed PWM techniques to eliminate

Goldberg, D. E. (1989). *Genetic algorithms in search, optimization and machine learning*,

Holtz, J. (1992). Pulsewidth modulation–a survey. *IEEE Transactions on Industrial Electronics*,

Holtz, J. (1995). The representation of ac machine dynamics by complex signal flow graphs. *IEEE Transactions on Industrial Electronics*, Vol. 42, No. 3, June 1995, 263-271

Ljung, L. & Söderström, T. (1983). *Theory and Practice of Recursive Identification*, MIT Press,

Michalewicz, Z. (1996). *Genetic algorithms + data structures=evolution programs*, Springer-Verlag,

Mohan, N.; Undeland, T. M. & Robbins, W. P. (1995). *Power electronics: converters, applications,*

Rezazadeh, A. R.; Sayyah, A. & Aflaki, M. (2006). Modulation error observation and regulation

Sayyah, A.; Aflaki, M. & Rezazadeh, A. R. (2006). GA-based optimization of total harmonic

Sayyah, A.; Aflaki, M. & Rezazadeh, A. R. (2006). Optimal PWM for minimization of

Sayyah, A.; Aflaki, M. & Rezazadeh, A. R. (2006). Optimization of THD and suppressing

Sun, J. (1995). *Optimal Pulsewidth Modulation Techniques for High Power Voltage-source Inverters*,

Sun, J.; Beineke, S. & Grotstollen, H.: (1996). Optimal PWM based on real-time solution of

*and motion*, pp. 1361-1366, Italy, May 2006, Taormina (Sicily)

for use in off-line optimal PWM fed high power synchronous motors, *Proceedings of 1st IEEE conference on industrial electronics and applications*, pp. 1300-1307, May 2006,

current distortion and suppression of chosen harmonics in induction motors, *Proceedings of international symposium on power electronics, electrical drives, automation*

total harmonic current distortion in high-power induction motors using genetic algorithms, *Proceedings of SICE-ICASE international joint conference*, Korea, pp.

certain order harmonics in PWM inverters using genetic algorithms, *Proceedings of IEEE international symposium on intelligent control*, pp. 874-879, Germany, October

harmonic elimination equations. it IEEE Transactions on Power Electronics, Vol. 11,

˘ S316.

Leonhard, W. (2001). *Control of Electrical Erives*, 3rd ed., Springer-Verlag, New York.

124-141.

Cambridge, MA.

New York.

Singapore

2006, Munich.

No. 4, July 1996, 612-621.

No. 2, Mar./Apr. 1990, 302âA ¸

Addison-Wesley, Reading, MA.

*and design*, Wiley, New York.

5494-5499, October 2006, Busan.

Thesis, University of Paderborn, Germany.

Vol. 39, No. 5, December 1992, 410-419.

algorithms. *IEEE Transactions on Evolutionary Computation*, Vol. 3, No. 2, July 1999,

harmonics: a critical evaluation. *IEEE Transactions on Industrial Applications*, Vol. 26,

**0**

**17**

Hendrik Richter

*Germany*

*HTWK Leipzig University of Applied Sciences*

**Artificial Immune Systems, Dynamic Fitness**

**Landscapes, and the Change Detection Problem**

To let biological processes, behaviors and structures inspire the design of problem solving algorithms and devices has been a prominent and persistent theme in engineering and applied sciences in the last few decades. Within this context, bio–inspired computing has taken a pioneering role. Fields such as evolutionary computing (1; 8; 25), artificial immune systems (4; 6; 43), membrane computing (29) or swarm systems (9; 22) have outgrown their infancy and found theoretical ground as well as important applications. The fact that and the way how these fields advanced into its current form is due to three major developments: (i) the upcoming of cheap, fast and reliable computational power in form of digital computers, (ii) the understanding that computational power in connection with implementing an algorithmic approach creates potent problem solvers, and (iii) the insight that biological systems can be fruitfully understood as information–processing units and can hence frequently be employed for computational and/or algorithmic proposes. This trend is of course not to be confused with computational biology, but it is highly related and probably unthinkable without the fundamental progress towards algorithmization and mathematization in biology, see e.g. (5; 16; 21; 38) for some recent discussion. Among the mentioned fields of bio–inspired computing, evolutionary algorithms and artificial immune systems play a unique role as their history is particularly long and the maturity reached is notably high. In this paper we will use both schemes in connection to solve the intertwined problem of maximum tracking and

For successfully solving dynamic optimization problems by evolutionary computation, there is a need for additions to the standard algorithmic structure, namely by operators maintaining and enhancing population diversity. Dynamic optimization here means that the topology of the associated fitness landscape changes with time. A considerable number of these operators for diversity management (for instance memory schemes, random immigrants or hyper–mutation (24; 26; 30; 33; 35; 39; 45)) can only be provoked and hence made to work properly if the points in time are known where the changes in the fitness landscape occur. So, the problem of change detection is of high practical relevance in solving dynamic optimization

In principle, change detection is based on using information about the fitness values of points in the search space extracted from the fitness landscape. This extraction of information can be done in two ways. One is to use the fitness evaluations of the evolutionary algorithm's

**1. Introduction**

change detection in dynamic optimization.

problems (3; 19; 27).
