![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.](https://image.slidesharecdn.com/natural-inspiredamanyfinal-221127011946-c068d855/85/Natural-Inspired_Amany_Final-pptx-11-320.jpg)
![• 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),](https://image.slidesharecdn.com/natural-inspiredamanyfinal-221127011946-c068d855/85/Natural-Inspired_Amany_Final-pptx-22-320.jpg)
![• 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.](https://image.slidesharecdn.com/natural-inspiredamanyfinal-221127011946-c068d855/85/Natural-Inspired_Amany_Final-pptx-23-320.jpg)
![• 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)](https://image.slidesharecdn.com/natural-inspiredamanyfinal-221127011946-c068d855/85/Natural-Inspired_Amany_Final-pptx-24-320.jpg)
Natural-Inspired_Amany_Final.pptx
- 1. Nature-Inspired Optimization Techniques and their Applications Dr. Amany Arafa
- 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. 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. 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. Optimize the solution Set of potential solutions Optimization criteria Evaluation space Goal: identify best solution(s) w.r.t. optimization criteria
- 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
- 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. 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. 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. 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.
- 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. 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. 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. 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. • Time tabling • Packing • Placement • Design • Grouping • renewable Energy design • The optimal siting of wind turbines • Position of solar cell Application in Engineering
- 20. 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
- 21. Inspiration from Insects Exploit physical modularity Complex robot made of simpler robots Sensors Actuation Computation Examples Hannibal Reconfigurable robots (Daniela Rus) Design by Evolution
- 22. • 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),
- 23. • 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.
- 24. • 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)
- 25. 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.