1. Biologically Inspired Algorithms
Submitted by:-
Varun
ME(regular-2013) student
Roll no
National Institute of Technical Teacher’s Training and Research, Chandigarh, India
Submitted to :-
Dr. Lini Mathew
Professor(electrical department)
National Institute of Technical Teacher’s Training and Research, Chandigarh, India

2. Conventional methods of computing….
Popular Conventional methods that have been widely used includes ..
Mathematical optimization algorithms..(such as Newton’s method and gradient descent method that use derivatives to locate a local minimum)
Direct search method (eg.. Simplex method and Nelder-mead method that use a search pattern to locate optima).
Enumerative approaches (such as Dynamic programming)
Limitation
Several assumptions about the problem in order to suit the particular method.
Flexibility to solve particular problem as it is.
May obstruct the possibility of modeling the problem closer to reality.
The efficiency of algorithm varies depending on the complexity of the problem.
The conventional nonlinear optimization solvers are not applicable for problems with non differentiable & /or discontinuous function relationship

3. Introduction
Bio-inspired computing, short for biologically inspired computing, is a field of study that loosely knits together subfields related to the topics of connectionism, social-behavior and emergence.
It relies heavily on the fields of biology, computer science and mathematics. Briefly put, it is the use of computers to model the living phenomena, and simultaneously the study of life to improve the usage of computers.
 Biologically inspired computing is a major subset of Natural Computation.

4. Life …..? Certain kind of animated behavior.
As an organization distinct from inorganic matter(with an associated list of properties.)
As a special commensurable quantity ,vitalism.

5. Taxonomy Biologically Inspired algorithms Evaluation Swarm based
Ecology
1)Genetic algorithm (GA), 2)Genetic programming (GP),
3)Differential Evolution, 4)evolutionary strategy (ES) 5)Paddy Field Algorithm. (most recent )
Natural river system. (i IWS)
Human immune system.(i AIS)
Convergent social phenomenon in animals/microbes
1) Biogeography.
i) BBO
2) Weed colony.
i) AWC
3) Symbiosis.
i) PS2O

6. Convergent phenomenon in animals/microbes
Producer scrounger.
Group Search Optimization(GSO).
Bird flocking .
Practical Swarm Optimization(PSO).
Stigmergy.
Ant colony optimization(ACO).
4)Fish schooling.
Fish Swarm Optimization(FSO).
5) Bacterial foraging.
Bacterial Foraging Algorithms(BFA).
6) Fire fly.
Fire fly algorithms(FA).
7) Social Behavior of bees.
Artificial bee Colony (ABC).
8) Frog leaping.
Shuffled Frog Leaping Algorithm(SFLA)

7. Shuffled frog leaping algorithm
Purpose to solve the combinatorial optimization problems.
Algorithm contain elements of local search and global information exchange.
Consists of virtual frogs act as hosts or carriers of memes where a meme is a unit of cultural evaluation.
Global information exchange
Global information exchange
Global information exchange
Local search
Global information exchange

8. ….. Local search performed by particle swarm optimization technique .
To ensure global exploration the virtual frogs are periodically shuffled and recognized into new memplexes (similar to shuffled complex evaluation algorithms).
For random generation of improved information ,random virtual frogs are generated and substituted in the population).
Examples of commonly used meta-heuristic :
1)ACO.
2)GA.
3)ANN.
4) PSO.
5) SCEA(shuffled complex evolution algorithms.).
6)SAA(simulated annealing algorithms).

9. Memetic algorithm :-contagious(direct or indirect contact of peoples) information pattern that replicate by parasitically infecting human/animal minds and altering their behavior, which cause them to propagate the pattern .
such as mutual understanding.
Heuristic:- enabling a person to discover or learn something for themselves.(computing) proceeding to a solution by trail and error method or by rules that are only loosely defined.
Comparison between meme and gene.
Meme-positively selected mainly for increased communicability among the host.
Gene- selected for sexual reproduction.
Information transfer gene-from generation to generation.
meme- transmitted in space of minutes.
Replication(for unique) gene-limited to small number.
meme-un-limited.

10. After defined no. evaluation
SCE (controlled random search algorithms + competitive evaluation.)+PSO = SFLA
Important features of shuffled complex evaluation
Global search as a process of natural selection.
sharing information and properties independently gained by each community
Population.
communities 1 4
After defined no. evaluation 3 2
Each community Permitted to evolve independently
New communities formed by shuffling
Complexes(i.e communities) forced to mix

11. Important features of particle swarm optimization
motivated from the simulation of social behavior .
Each particle adjust its flying state according to its own flying experience and its companions flying experience.
Random initialization of population with position and velocity.
each particle is treated as a point in d-dimension.
ith particle is initialized with 𝑥 𝑖 .
The best pervious position of particle is recorded and and represented by 𝑃 𝐼 .
The index of the best particle among all the particle in population is represented by 𝑃 𝑔 .
Evaluation of preformation on the bases of fittest factor.

12. Shuffled frogs leaping algorithms
Shuffled frog leaping algorithm
1) virtual population of frogs.
2) algorithms details.

13. Genetic algorithms Representation Control parameters. Binary,
real numbers,
permutation of elements,
list of rules,
program elements,
data structure,
tree ,
Matrix.
Control parameters.
Population size,
max. generation number,
cross over probability,
mutation probability,
length of chromosome,
chromosome encoding
Operators
Crossover,
mutation,
selection,
inversion,
gene,
silencing
Genetic algorithms

14. Area of application.
Optimization problems in data mining and rule extraction, dynamic and multiple criteria web-sit optimizations, decision thresholds for distributed detection in wireless sensor networks, computer aided design path planning for robots, fixed charge transportation problem, flight control system design, pattern recognition, reactive power dispatch, sensor based robot path planning,training of radial basis function, multi-objective vehicle routing problem, minimum energy broadcast problem in wireless ad-hoc network, software engineering problems, pollutant emission reduction problem in manufacturing industry, power system optimization problems, port folio optimization, optimal learning path in e learning, web based classification system ,closest sting problem in bioinformatics ,structural optimization ,defect identification system, molecular modeling , web service selection ,cutting stock problem ,drug design ,personalized e-learning system , SAT solver

15. Genetic programing Control parameters
Population size ,maximum number of generations, probability of crossover ,probability of mutation
Representation.
Tree structure (terminals and function set.)
Genetic programing
Operators.
Crossover,
reproduction,
mutation,
permutation ,editing ,encapsulation,
decimation.
Area of Application
Portfolio optimization ,design of image exploring agent ,epileptic pattern recognition ,automated synthesis of analogue electrical circuits symbolic regression , robotics ,data mining (automatic feature extraction, classification etc.),cancer diagnosis ,power transformer fault classification

16. Evolution strategies Control parameters:- Representation:-
Population size, maximum number of generations ,probability of crossover ,probability of mutation.
Representation:-
Real-valued vector
Evolution strategies
Area of application:-
Parameter estimation ,image processing ,computer vision system ,task scheduling and car automation ,structural optimization ,evolution strategy for gas-turbine fault –diagnoses , multi-parametric evolution strategies algorithm for vehicle.
Operators:-
Mutation ,
Selection,
Discrete recombination

17. Differential evolution
Representation:-
Real- valued vectors.
Control parameters:-
S-population size ,n-dimension of problem ,F-scale factor ,Pr-probability of crossover
Differential evolution
Area of application:-
Unsupervised image classification ,clustering ,digital filter design ,optimization of non- linear chemical processes and multi-objective optimization.
Operators :-
Crossover ,mutation ,
selection

18. Practical Swarm optimization
Control parameters:-
Number of particles, dimension of particles ,range of particles , 𝑉 𝑚𝑎𝑥 ,learning factors:C1&C2 ,inertial weight , maximum number of iterations.
Representation:-
D-dimensional vector for position, speed ,best state.
Practical Swarm optimization
Area of application:-
Multimode biomedical image registration's & the iterated prisoner’s dilemma, classification of instances in multiclass databases, feature selection ,power system optimization problems(economic ,dispatching) ,edge detection in noise images , finding optimal machining parameter assembly line balancing problem in production and operations management ,vehicle routing problems ,anomaly detection , color image segmentation ,selecting particle regeneration for data clustering , extracting rules from fuzzy neural networks, machine fault detection , Signature verification , prediction of tool life in ANN
Operators:-
Initializer ,updater and evaluator.

19. Ant Colony Optimization
Representation:-
Undirected graph
Control parameters:-
Number of ants ,iteration , pheromone evaporation rate ,amount of reinforcement
Ant Colony Optimization
Area of application:-
Quadratic assignment problem(QAP) ,dynamic problem of data network routing, a short part problem where properties of system such as node availability vary over time ,continuous optimization and parallel processing implementations , vehicle routing ,graph coloring and set covering ,agent based dynamic scheduling , digital image processing ,classification problem in data mining , protein folding problem
Operators:-
Pheromone update and measure ,trail evaporation.

20. Paddy field algorithm (new)
Representation:-
linear
Control parameters:-
Size of population , the boundary of parameter space ,initial value of maximum number of seeds.
Paddy field algorithm (new)
Area of application:-
Continuous function optimization ,tuning parameters in PID controllers in Higher order systems and RBF Neural Network parameters optimization
Operators:-
Dispersal, Pollination

21. Artificial Immune system
Representation:-
Attribute string(a real valued vector ) ,integer string ,binary string , symbolic string.
Control parameters:-
Antibody population size ,number of antibodies to be selected for hyper-mutation ,number of antibodies to be replaced, multiplier factor ß
Artificial Immune system
Operators:-
Immune operators
(cloning ,hyper mutation and selection based on elitism)
Area of application:-
Computer security ,anomaly/outlier detection ,clustering/classification ,numeric function optimization , learning ,IIR filter design ,control ,robotics , data mining ,virus detection ,pattern recognition , tuning of controllers , multi-model optimization ,job shop scheduling

22. Artificial bee colony Representation:- D-dimensional vector
Control parameters:-
Number of food sources which is equal to number of employed or onlooker bees(SN), the value of the limit , the maximum cycle number(MCN)
Artificial bee colony
Area of application:-
Scheduling problems, Image segmentation ,capacitated vehicle routing problem ,wireless sensor networks ,assembly line balancing problem ,solving reliability redundancy allocation problem , training neural networks, Detector encoder and 3-bit parity benchmark problems , pattern classification , reliability redundancy allocation problems ,clustering, resource –constrained projects scheduling P-center problem
Operators:-
Reproduction ,replacement of bee ,selection

23. Fish-Swarm algorithms.
Control parameters:-
Visual distance,maximum step length, crowd factor.
Representation:- Xi
Fish-Swarm algorithms.
Area of application:-
Function optimization, parameter estimation ,least square support vector machine and geotechnical engineering problems.
Operator:-
Swarming ,following ,searching.

24. Group search optimization
Representation:-
Unit vector.
Control parameters:-
Population size ,percentage of ranges , number of ranges , head angle ,position ,maximum pursuit angle, maximum turning angle , maximum pursuit distance.
Group search optimization
Area of application:-
Truss structure design ,benchmark function applied for power flow problems ,multi objective optimization ,optimal placement of FACT devices ,machine conditioning and monitoring ,optimal location and capacity of distributed generations.
Operators:-
Scrounging ,ranging ,
Producing.

25. Shuffled frog leaping algorithm
Representation:-
Control parameters:-
Number of frogs P, number of memeplexes, and number of evolutionary iterations for each memeplex before shuffling.
Shuffled frog leaping algorithm
Area of application:-
Color image segmentation ,automatic recognition of speech emotion water ,grid task scheduling ,multi-user detection in DS-CDMA distribution ,Fuzzy controller design ,optimal reactive power flow , Mobile robot path planning ,classification rule mining, ground water calibration problems , Multicast routing optimization.
Operators:-
Replacement, shuffling

26. Bacterial foraging algorithms.
Representation:-
ø_𝑖 (𝑗,𝑘,𝑙)
Represents i-th bacterium at jth chemotactic ,k-th reproductive and l-th elimination dispersal step.
Control parameters:-
Dimension of the search space ,number of bacteria ,number of chemotactic steps ,number of elimination and dispersal events ,number of reproduction steps ,probability of elimination and dispersal , location of each bacterium , number of iterations ,step size c(i)
Bacterial foraging algorithms.
Area of application:-
Inverse airfoil design ,application for harmonic estimation problem in power systems , optimal power system stabilizer design , tuning the PID controller of an AVR ,Machine learning ,an application of job shop scheduling benchmark problems, the parameters of membership functions and weights of rule of a fuzzy rule set are estimated ,transmission loss reduction ,application in the null steering of linear antenna arrays by controlling the element amplitudes .
Operators:-
Reproduction ,chemotaxis ,dispersion ,elimination.

27. Intelligent water drop
Representation:-
Vector in D-dimensional space.
Control parameters:-
Weed population size ,Modulation index , standard deviations.
Intelligent water drop
Area of application:-
Time modulated linear antenna array synthesis ,cooperative multiple task assignment of UA ,fractional order PID controller ,training of Feed forward networks ,blind multi-user detection for MC-CDMA interference suppression over multipath fading channel , recommender system.
Operators:-
Reproduction ,dispersal ,selection.

28. PS2O Control parameters:-
number of particles ,dimension of particles ,range of particles , 𝑉 𝑚𝑎𝑥 ,learning factors: inertial weight ,maximum number of iterations.
Representation:-
D-dimensional vector for position , speed ,best state.
PS2O
Operator:-
Initializer updater ,extinction ,evaluator.
Area of application:-
Cooperative cognition wireless communication ,constructing collaboration service systems(CSSs)

29. Biogeography-based optimization
Representation:-
H= ℎ 1 , ℎ 2 …….. ℎ 𝑛 as individuals of habitat.
Control parameters:-
Number of habitats(population size) , maximum migration rates ,mutation rates.
Biogeography-based optimization
Area of application:-
General benchmark functions ,constrained optimization , the sensor selection problem for aircraft engine health estimation ,power system optimization ,ground water detection and satellite image classification ,web based biogeography-based optimization graphical user interface , global numerical optimization ,optimal meter placement for security constrained state estimation.
Operator:-
Migration(emigration and immigration) ,mutation.