Successfully reported this slideshow.
Your SlideShare is downloading. ×

Natural-Inspired_Amany_Final.pptx

Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Ad
Loading in …3
×

Check these out next

1 of 25 Ad

More Related Content

Recently uploaded (20)

Advertisement

Natural-Inspired_Amany_Final.pptx

  1. 1. Nature-Inspired Optimization Techniques and their Applications Dr. Amany Arafa
  2. 2. •Research is the process of finding the solution of the unsolved life problems. •Why to do research? •Understand the world •Solve new problems •Improve existing solutions Research
  3. 3. Problem • Most of the real-world problems, including design, optimization, scheduling and control etc. are inherently characterized by multiple conflicting objectives realization. • When addressing these problems, the parameters or variables are frequently imprecise due to uncontrollable factors, leading to more complicated problem formulation.
  4. 4. Problem Solving • While solving any problem one can find multiple solutions. • More attention must paid to : • cost minimization. • output maximization • energy saving, • environmental protection • sustainable development issues, etc. in various fields. • There’s lots of values to tune which are hard to predict
  5. 5. Optimize the solution Set of potential solutions Optimization criteria Evaluation space Goal: identify best solution(s) w.r.t. optimization criteria
  6. 6. Nature-Inspired Optimization • Nature can provide a great inspiration to the field of computation and artificial intelligent. Nature is the only source of AI • The beauty of nature is its ability to produce amazingly complex patterns with amazingly simple algorithms. • Observe how the nature work. Then Extract the principle • Ex. Animals can find the solutions of complex problems in less energy and cost with recovering error strategy. • Apply natures method in solving our problem
  7. 7. Ex. Firefly Algorithm
  8. 8. Fireflies: Introduction • One of the family of insects. • Live in tropical environment. • Produce-cold light-chemically • Yellow, green, pale-red light • Based on the flashing patterns and behavior of fireflies.
  9. 9. Behavior of Fireflies  Two fundamental functions of such flashes are: ◦ to attract mating partners (communication) ◦ to attract potential prey ◦ protective warning mechanism  They have unique flashing pattern.  As the distance increases, light becomes weaker and weaker because absorption by air.
  10. 10. Rules for Firefly Algorithm • All fireflies will be attracted to other fireflies. • Attractiveness is proportional to the brightness, and they both decrease as their distance increases. • The brightness of a firefly determined by the objective function.
  11. 11. Pseudo Code Objective function f(x), x = (x1, ..., xd ) Generate initial population of fireflies xi (i = 1, 2, ..., n) Light intensity Ii at xi is determined by f(xi) Define light absorption coefficient while(t <MaxGeneration) for i = 1 : n all n fireflies for j = 1 : i all n fireflies if(Ij > Ii), Move firefly i towards j in d-dimension; end if Attractiveness varies with distance r via exp[−r] Evaluate new solutions and update light intensity end for j end for i Rank the fireflies and find the current best end while Postprocess results and visualization.
  12. 12. Nature Inspired Optimization Algorithms
  13. 13. Progress of Algorithms
  14. 14. Heuristics vs. meta-heuristics • heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution • The biggest and most important difference between a heuristic and a meta-heuristic is that heuristics get stuck in local optima, while meta- heuristics have mechanism to avoid that. • As examples of this mechanisms, among others, we have: • Mutation (in Genetic Algorithm); • Constructive with Restricted Candidate List (GRASP); • Perturbation (ILS). • Hyper-heuristics are to tell what sequence of Meta-heuristics to use to solve the problem at hand. Also it can be used to classify what meta- heuristic fits better to which problem. • So for example, if we are solving and instance of TSP problem, it could result that the best solution is a result of running first GA, then Simulated Annealing then Ant- colony.
  15. 15. Fields Appear Based on Nature • When using computers and computation in solving biological or chemical problem new fields appear (bioinformatics and cheminformatics) • Bioinformatics is an interdisciplinary field that develops and applies computational methods to analyze large collections of biological data. • Cheminformatics is also known as multidisciplinary science as it combines Chemistry, Biology, Mathematics, Biochemistry, Statistics and informatics.
  16. 16. Application in Cheminformatics • Storing data generated through experiments or from molecular simulation Retrieval of chemical. • Structures from chemical database (Software libraries). • Prediction of physical, chemical and biological properties of chemical compounds. • Elucidation of the structure of a compound based on spectroscopic data. • Structure, Substructure, Similarity and diversity searching from chemical database. • Docking - Interaction between two macromolecules. • Drug Discovery • Molecular Science, Materials Science, Food Science (nutraceuticals), • Atmospheric chemistry, Polymer chemistry, Textile Industry, • Combinatorial organic synthesis (COS).
  17. 17. Application in Bioinformatics • The computational methods used include analytical methods, mathematical modelling and simulation. The Nature inspired computational methods Applied for: • genetic sequences, • cell populations or • protein samples, • to make new predictions or discover new biology. • Gene selection • Cancer classification
  18. 18. • Time tabling • Packing • Placement • Design • Grouping • renewable Energy design • The optimal siting of wind turbines • Position of solar cell Application in Engineering
  19. 19. Robots Inspired by Nature Robots as interesting complex systems Similarity to animals Consequences of having a real body Real tasks in the real world --- cannot predict all interactions Lessons learned from biological creatures Increase physical complexity Increase behavioral complexity
  20. 20. Inspiration from Insects Exploit physical modularity Complex robot made of simpler robots Sensors Actuation Computation Examples Hannibal Reconfigurable robots (Daniela Rus) Design by Evolution
  21. 21. • Evolutionary Algorithms (EA) are stochastic search methods that mimic the survival of the fittest process of natural ecosystems. The algorithms have strong adaptability and self organization, including Evolutionary Programming (EP) [10], • Evolutionary Strategy (ES) [11], Genetic Algorithm (GA), • Differential Evolution Algorithm (DE), • Harmony Search Algorithm (HS), • Membrane Computing (P system),
  22. 22. • Particle Swarm Optimization Algorithm (PSO), • Artificial Bee Colony Algorithm (ABC) [45], • Artificial Immune System (AIS), • Teaching-Learning Based Optimization algorithm (TLBO), • Ant Colony Optimization Algorithm (ACO)[46], • Cuckoo Search algorithm (CS) [47], • Firefly Algorithm (FA) [48], • Bacteria Foraging Optimization algorithm (BFO) [49], • Coral Reef Optimization algorithm (CROA) [50], • Shuffled Frog Leaping Algorithm (SFLA) [51], • Pigeon Inspired Optimization (PIO) [52], etc.
  23. 23. • Teaching-learning based optimization (TLBO). The common physics inspired algorithms include • Chaotic Optimization Algorithm (COA), • Intelligent Water Drops Algorithm (IWD) [68], • Magnetic Optimization Algorithm (MOA)[69], • Gravitational Search Algorithm (GSA) [70,71], • Simulated Annealing (SA)
  24. 24. Geography inspired algorithms are • one sort of metaheuristic algorithm and generate random solutions in the geographical search space. These optimization algorithms are classified as • Tabu Search Algorithm (TS), • Imperialistic Competition Algorithm (ICA), etc.

×