1. Firefly Algorithm Hasan Gk Nature Inspired Computing

2. Outline Metaheuristic - Heuristic Aplications About fireflies Digital Image Compressionand Image Processing General knowledge Feature selection and fault How they behavedetection The Algorithm Demo Particle Swarm Optimization Four Peak Function FAs Explanation Parabolic Function Formulas Rastrigin Function Psuedo Code Styblinski Function 3. Heuristic Means to find or to discover by trial and error. Solutions can be found in a reasonable amount of time. There is no guarantee that optimal solutions are reached. 4. Metaheuristic Meta- means beyond or higher level. Generally perform better than simple heuristics. All metaheuristic algorithms use randomization and local search. Randomization provides a way to move away from local search. 5. Fireflies 6. About Fireflies GeneralOne of the family of insects.Live in tropical environment.Have wings.Produce cold light chemically.Yellow, green, pale-red lights.Their larvae called glowworm.~2000 species.Flightless females.[1] http://en.wikipedia.org/wiki/Firefly 7. About Fireflies - Video[2] http://www.youtube.com/watch?v=AcuTvFV6a8Q 8. About Fireflies - BehaviorTheir purpose of flashing: Attarct mating partners (communication). Attarct potential prey. Protective warning mechanism.They have unique flashing pattern.In some species, females can mimic mating pattern to hunt other species.They have limited light intensity.[3] Firefly Algorithms for Multimodal Optimization, Xin-She Yang 9. The Algorithm 10. Particle Swarm Optimization Consist of a collection (called a swarm) of individual entities (known asparticles) Each particle represents a candidate solution Every particle knows a) Its own position b) Its own direction and velocity c) The position of its own best solution d) The position of the best currently known solution of the whole swarm 11. Cooperation 12. The Algorithm Like Particle Swarm Optimization. Inspired by the behavior of fireflies. Developer of the algorithm is Dr. Xin-She Yang. Three main assumptions: 1. All fireflies are unisex. 2. Attractiveness Brigtness & Attractiveness 1 / Distance 3. Brightness is determined by objective function. 13. Formulas - Attractiveness 14. Formulas - Distance In our case, d is goint to be euclidean distance 15. Formulas - Movement Movement consists two elements Approach to better solutions Move randomly 16. Special Cases 17. Pseudo Code 18. Applications Digital Image Compression and Image Processing Feature selection and fault detection Antenna Design Structural Design Scheduling Semantic Web Composition Chemical Phase equilibrium Clustering Dynamic Problems Rigid Image Registration Problems 19. Fireflies in Use1. Four Peak Function 20. Fireflies in Use2. Parabolic Function 21. Fireflies in Use3. Rastrigin Function 22. Fireflies in Use4. Styblinski Function 23. Comparison with PSOFunction NPSO Firefly AlgorithmFour-peak15 1,53561,4840 20 2,01351,9326 25 2,49592,3652Parabolic15 1,54821,5039 20 2,08841,9296 25 2,64662,3534Rastrigin15 9,67619,5298 20 12,6412 12,5404 25 15,6878 15,5457Styblinski 15 1,64441,5478 20 2,15042,0725 25 2,61442,5323 24. Performance Comparison Genetic Algorithm Particle Swarm Firefly AlgorithmMichalewicz%95%98%99Rosenbrock %90%98%99De Jong%100%100 %100Schwefel %95%97 %100Ackley %90%92 %100Rastrigin%77%90 %100Easom%92%90 %100Griewank %90%92 %100Shubert (18 min) %89%92 %100Yang %83%90 %100 25. References [1] http://en.wikipedia.org/wiki/Firefly_algorithm (Accessed: 08.04.2013) [2] Xin-She Yang, Firefly Algorithms for Multimodal Optimization, 2010 [3] Saibal K. Pal, C.S Rai, Amrit Pal Singh, Comparative Study of Firefly Algorithmand Particle Swarm Optimization for Noisy Non-Linear Optimization Problems,2012 [4] Xin-She Yang, Comparative Study of Firefly Algorithm and Particle SwarmOptimization for Noisy Non-Linear Optimization Problems, 2010, ISBN: 1-905986-28-9 [5] Mohammad Kazem Sayadi, Reza Ramezanian and Nader Ghaffari-Nasab, Adiscrete firefly meta-heuristic with local search for makespan minimization inpermutation flow shop scheduling problems, 2010[6] Karel Durkota, Implementation of a Discrete Firefly Algorithm fort he QAPProblem, 2011