First National Conference on Algorithms and Intelligent Systems under LeadCollege Activity Shivaji University. In association withComputer Society on India and International Journal of Computer and Communication Technology.
Paper Title Immune Artificial Cuckoo Search algorithm for Nonlinear System Identification. Paper ID – 45 Ritesh kumar Lav KediaSchool of electrical sciences Electrical Engineering IIT Bhubaneswar BIT Deoghar
CONTENTS… Keywords FLANN Problem Statement Cuckoo Search Algorithm Introduction to Immunity and its Need Advantages Pseudocode Conclusion
. Immune Artificial Cuckoo Search algorithm for Nonlinear System Identification
KEYWORDS FLANN NON-LINEAR SYSTEM CUCKOO SEARCH ALGORITHM IMMUNE SYSTEM
FLANN (FUNCTIONALLY LINKEDARTIFICIAL NEURAL NETWORK) FLANN was first proposed by Pao as a single layer ANN structure capable of forming arbitrarily wide complex decision regions by generating non-linear decision boundaries. FLANN has only input and output layers and the hidden layers are completely replaced by the nonlinear mappings. FLANN increases the learning rate but also reduces the computational complexity.
Non-Linear plant Trigonometric FLANN
PROBLEMTo frame algorithm that would be able to model the non-linear systems what we encounter in our day-2- day life.
NESTCUCKOO BREEDING eggs Cuckoo
LEVY FLIGHTS Random walk in which the step-lengths are distributed according to a heavy-tailed probability distribution. After a large number of steps, the distance from the origin of the random walk tends to a stable distribution.
IMMUNE ACSO: A BETTERAPPROACH TOWARDSOPTIMISATION Introducing the concept of immunity in AFSO is a novel way of development of new algorithms using natural swarm intelligence along with the self- sustaining capability of immune systems. There are several ways of making any optimisation algorithm immune. These include Negative selection algorithm, clonal selection algorithm and Immune Network algorithm. For our purpose, we have used clonal selection algorithm.
CLONAL SELECTION Widely accepted model for how the IMMUNE SYSTEM responds to an invasion from outside. Some of its basic steps are as follows: 1. For an attack from outside there is always a particular type of receptor cell for detecting it. 2. After an attack specific type of receptor cells are activated and are differentiated to form more number of identically specific cells. 3. So for an attack of similar type these receptor cells will come into function quite early and hence reduces the risk of cells damage.
CLONAL SELECTIONIN PICTURE
ADVANTAGES OF IMMUNEAFCSO Immune AFSO has a better convergence rate as compared to its non-immune counterpart. The probability of getting stuck in a local solution also gets minimised.
PSEUDOCODEBeginObjective function f(x)=(X1,X2,...........)Generate initial population of n host nests XiWhile (t<max.generation) or (stop criterion)Get a cuckoo randomly by Levy Flights.Evaluate its Fitness,Fi.Choose a nest randomly say j .If (Fi>Fj)Replace J by the new solutionEnd ifA fraction (pA) of the worst nests are abandoned and new ones arebuilt.Keep the best solution.Rank the solutions and find the current bestEnd whileUse genetic algorithm based crossover and mutation technique tofind the solution to the problems.Post process results and visualisation.End.
REFERENCES X.-S. Yang; S. Deb (December 2009). "Cuckoo search via Lévy flights". World Congress on Nature & Biologically Inspired Computing (NaBIC 2009). IEEE Publications. pp. 210–214. R. N. Mantegna, Fast, accurate algorithm for numerical simulation of Levy stable stochastic processes, Physical Review E, Vol.49, 4677- 4683 (1994). J.J.,1989a.AdaptiveIIRfiltering.IEEEASSPMagazine,421. 1989,chapter 8,pp.197-222. X.-S. Yang and S. Deb, "Engineering optimisation by cuckoo search", Int. J. Mathematical Modelling and Numerical Optimisation", Vol. 1, No. 4, 330-343 (2010). N. Bacanin, An object-oriented software implementation of a novel cuckoo search algorithm, Proc. of the 5th European Conference on European Computing Conference (ECC11), pp. 245-250 (2011).
CONTD… M. Tuba, M. Subotic, and N. Stanarevic, Modified cuckoo search algorithm for unconstrained optimization problems, Proc. of the 5th European Conference on European Computing Conference (ECC11), pp. 263-268 (2011). S. Walton, O. Hassan, K. Morgan, Using proper orthogonal decomposition to reduce the order ot optimization problems, in: Proc. 16th Int. Conf. on Finite Elments in Flow Problems (Eds. Wall W.A. and Gvravemeier V.), Munich, p.90 (2011). F. Wang, L. Lou, X. He, Y. Wang, Hybrid optimization algorithm of PSO and Cuckoo Search, in: Proc. of 2nd Int. Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC11), pp. 1172-1175 (2011). A. Kumar and S. Chakarverty, Design optimization for reliable embedded system using Cuckoo Search,in: Proc. of 3rd Int. Conference on Electronics Computer Technology (ICECT2011), pp. 564-268 (2011).
BRAIN TEASERS… So which famous algorithm is modified in Cuckoo Search algorithm?? So can anyone tell me that who was the first person to work on evolutionary computation?? Why do we prefer evolutionary algorithms over others to solve or optimize our problems?? So what all shortcomings of PSO are overcome by Cuckoo search algo.??