The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
The cuckoo search algorithm is a recently developed meta-heuristic optimization algorithm, which is suitable for solving optimization problems. Cuckoo search is a nature-inspired metaheuristic algorithm, based on the brood parasitism of some cuckoo species, along with Levy flights random walks
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
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Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
This presentation provides an introduction to the Particle Swarm Optimization topic, it shows the PSO basic idea, PSO parameters, advantages, limitations and the related applications.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm analysis of algorithm
In this modern era a great deal of metamorphism is observed around us which eventuate due to some
minute modifications and innovations in the area of Science and Technology. This paper deals with the
application of a meta heuristic optimization algorithm namely the Cuckoo Search Algorithm in the design
of an optimized planar antenna array which ensures high gain ,directivity, suppression of side lobes,
increased efficiency and improves other antenna parameters as well[1], [2] and [3].
In this paper a new evolutionary algorithm, for continuous nonlinear optimization problems, is surveyed.
This method is inspired by the life of a bird, called Cuckoo.
The Cuckoo Optimization Algorithm (COA) is evaluated by using the Rastrigin function. The problem is a
non-linear continuous function which is used for evaluating optimization algorithms. The efficiency of the
COA has been studied by obtaining optimal solution of various dimensions Rastrigin function in this paper.
The mentioned function also was solved by FA and ABC algorithms. Comparing the results shows the COA
has better performance than other algorithms.
Application of algorithm to test function has proven its capability to deal with difficult optimization
Running head BIOLOGY LAB PROJECT1BIOLOGY LAB PROJECT 4.docxjoellemurphey
Running head: BIOLOGY LAB PROJECT 1
BIOLOGY LAB PROJECT 4
Introduction
Drosophila melanogaster is a species of fly in the family drosophilidae. The common name for Drosophila melanogaster is fruit fly or vinegar fly (Capy, Gibert & Boussy, 2004). The drosophila is a species widely used for biological research in studies of genetics, physiology, microbial pathogenesis, and life history evolution. It has been used to study genetics for over 100 years. D. melanogaster was one of the first organisms used for genetic analysis and is still widely used today. The drosophila is largely used for research study because it is an insect that is easy to take care of and lays many eggs, which gives us the opportunity to have many flies to study. Also, fruit flies can create a complete generation in about ten days thus allows several generations to be produced and studied within a few weeks (Regan, 2014). The average life of a fruit fly in optimal temperatures is 40 to 50 days. The life of Drosophila melanogaster depends on the weather temperature. For example, D. melanogaster’s lifespan is around 30 days at 29˚ C, 84˚ F and the lifespan decreases with a decrease in temperature. Drosophila’s eggs can hatch after 12–15 hours. The female can mate with the male after 8 to 12 hours after hatching. Nowadays, most genetics scientists prefer to use the Drosophila melanogaster fliesbecause they can study different generations in a short period of time.
In the genetics lab, we determine the mode of inheritance of phenotype mutant and wild type. We cross wild type males with female mutants. Also, we cross mutant males with wild type females to determine the genetic changes in both generations. The wild type flies have red eyes phenotype and long (normal) wings. On the other hand, mutants have white eyes and short wings. These observations are made after observing the first and second generations for both cross and wild type breeds and then comparing the observable change between them. In this course, we make several crosses between flies from wild-type and mutant phenotypes to show the mode of inheritance of the genes in Drosophila Melanogaster.
Methods and Materials
In this lab we used fruit flies and we examined them by putting them under the microscope. We also use FlyNap by to make the flies sleep for a mount of time while we viewing them. In order to use the FlyNap, first we transfer the flies to an empty vial and we do that by place the vial that we want to transfer immediately over the opening of the empty vial, so by this we will not allow the flies to escape from our vial. After they have been transfer to the new vial we place a small FlyNap brush and wait for a while until they all sleep. When they all sleep we put them in a small plate. At this time, we will be able to put them under the microscope and we use a paintbrush to move and look at the flies. Under the microscope we can easily determine the phenotype and the sex for each fly. We careful ...
Task Implement the 12 classes 5 enums and one interface s.pdfacsmadurai
Task: Implement the 12 classes, 5 enums, and one interface shown in the diagram above and
then answer the questions at the end of the document.
Class Design:
Zoo A zoo contains a collection of animals.
Animal An animal is a living creature. Each animal has a name, species, & weight. An animal may
be able to fly, swim, or walk. An animal can breathe either through lungs or through gills. A mother
animal can deliver babies either though laying eggs or through live birth. Animals are either cold or
warm blooded. Their skin may be covered with fur. Animal in this project is an interface.
AbstractAnimal An abstract class to implement the Animal interface. An abstract class cannot be
instantiated. Many of the methods are defined as abstract, leaving it for one of the eight
subclasses to define.
Bird, Amphibian, Mammal, Reptile, Fish, FlightlessBird, SwimmingMammal, DuckBilledPlatypus
subclasses of AbstractAnimal, any of which can be instantiated into an object. Defines the
methods left as abstract in AbstractAnimal. All of these subclasses have a similar argument
signature.
AnimalFactory a class with a single static method, getInstance(), which, depending on the species
of the animal, will create the appropriate subclass of AbstractAnimal. You are not meant to create
Animals using the constructors for the subclasses of Abstract.
Depending on the species of the animal. There are two ways to do this:
1. Use a giant if block to examine the animals species and return an object of the correct subtype
of AbstractAnimal, or
2. Implement a hash map where the key is the animal species, and the value is an object of the
appropriate subtype of AbstractAnimal. Use the map get() method to return the subtype of Animal
given the species of the animal.
ZooKeeper The zookeeper admits animals into the zoo and keeps track of the animals in it. It is
the only class in this project with a main() method, and the only method in this class is main.
Tasks:
1. Build the 5 enumerations:
a. Birth,
b. BloodTemp,
c. Movement,
d. Respiration,
e. Skin Covering
2. Implement the Animal interface, the AbstractAnimal class and all the subclasses of
AbstractAnimal. Put these classes in the package edu.bergen.zoo
3. Implement the AnimalFactory class. Its getInstance() method should return one of:
a. Bird
b. Mammal
c. Reptile
d. Amphibian
e. Fish
f. FlightlessBird
g. SwimmingMammal,
h. DuckBilledPlatypus
based on the value of species provided in the argument list.
Hint#1 Use an if block, checking on the value of species, and return an object of the appropriate
subclass of AbstractAnimal, or
Hint#2 = create a HashMap, where the key is the name of the species, and the value is an object
of the appropriate subclass of AbstractAnimal
4. Implement the Zoo class. Put this class in the package edu.zoo
5. Build a ZooKeeper class. In its main() method, do the following tasks:
a. Construct a Zoo object.
b. Create the following ten Animals using AnimalFactory.getInstance(), and use the
Zoo.addAnim.
This is about Comparative Analysis of Artificial Bee Colony and Improve Cuckoo Search algorithm, a thesis work done by us. Finally it is published on February-10-2015 on IJARAI. Here you will find the basic of ABC algorithm, ICS algorithm and the comparison between them.
1. Cuckoo Search Optimization Algorithm
AlgorithmDesign CS435
Group Members :
Arjun
Feressa
Haymanot
James
June 2014
2. What is Cuckoo Search?
Cuckoo search (CS) is an optimization
algorithm developed by Xin-she Yang and
Suash De b in 2009.
It was inspired by the obligatebroodparasitism
of some cuckoo species by laying their eggs in
the nests of other host birds (of other species).
An obligate parasite is a parasitic organism
that cannot complete its life cycle without
exploiting a suitable host.
a cuckoo which hatches and is raised by non-
relatives, is known as a brood parasite.
3.
4. Consequence
Some host birds can engage direct conflict
with the intruding cuckoos.
For example, if a host bird discovers the
eggs are not their own, it will either throw
these alien eggs away or simply abandon its
nest and build a new nest elsewhere.
5. Adaptation, and Evolution
Some cuckoo species have evolved in such
a way that the female parasitic cuckoos are
often very specialized in the mimicry in colors
and pattern of the eggs of a few chosen host
species.
6. Inspiration
Cuckoo search idealized such breeding
behavior, and thus can be applied for various
optimization problems.
It seems that it can outperform other m e ta-
he uristic alg o rithm s in applications.
Note:-
Heuristic: experience-based techniques for problem solving,
A heuristic is still a kind of an algorithm, but one that will not
explore all possible states of the problem,
7. Representations
Each egg in a nest represents a solution, and a
cuckoo egg represents a new solution.
The aim is to use the new and potentially better
solutions (cuckoos) to replace a not-so-good
solution in the nests.
In the simplest form, each nest has one egg. The
algorithm can be extended to more complicated
cases in which each nest has multiple eggs
representing a set of solutions.
8. Three idealized rules of Cuckoo
Search
Each cuckoo lays one egg at a time, and
dumps its egg in a randomly chosen nest;
The best nests with high quality of eggs will
carry over to the next generation;
The number of available hosts nests is fixed,
and the egg laid by a cuckoo is discovered by
the host bird with a probability pa (0,1).
9. • As a further approximation, this last assumption can be
approximated by a fraction pa of the n nests being
replaced by new nests (with new random solutions at
new locations).
• For a maximization problem, the quality or fitness of a
solution can simply be proportional to the objective
function. Other forms of fitness can be defined in a
similar way to the fitness function in genetic algorithms.
10. Lé vy flig ht
When generating new solutions x(t+1)
for, say cuckoo i , a L´evy
flight is performed
xi
(t+1)
= xi
(t)
+ α ⊕ L´evy(λ ) ……. . (1)
Where α > 0 is the step size, which should be related to the
scales of the problem of interest. In most cases, we can use α = 1
New Solution Current
Location
The transition
probability
11. Use of Lé vy flig ht
Some of the new solutions should be
generated by L´evy walk around the best
solution obtained so far, this will speed up the
local search.
However, a substantial fraction of the new
solutions should be generated by far field
randomization and whose locations should be
far enough from the current best solution, this
will make sure the system will not be trapped
in a local optimum.
12. Replace j by the new
Solution
End
Start
Initialize a Random population
of n host nests, xi
Get a Cuckoo randomly ,i
Evaluate its Fitness Fi
Select a nest among n
randomly , j
Let j as the solution
Abandon a fraction pa of worse
nests and build new one at new
locations
Keep the Current Best
Find the best nest
Fi>=Fj
T<=MaxIterationsNo
Yes
No
Yes
13. Let Eggs Grow
Initialize Cuckoos
with Eggs
Lay Eggs in
different nests
Some of Eggs are
detected and killed
Determine Egg
Laying Radius for
each Cuckoo
Move all Cuckoos
to wards best
environment
Determine Cuckoo
Societies
Find nests with
best Survival rate
Start
Check Survival of
Eggs in nests
Kill Cuckoos in
worst Area
Stop
Condition
Population
< MaxValue
yes
No
No
yes End
14. PSEUDO CODE OF CUCKOO SEARCH ALGORITHM
Begin
Objective function f(x), x = (x1, ..., xd) ;
Initial a population of n host nests xi (i = 1, 2, ..., n);
while (t <MaxGeneration) or (stop criterion)
Get a cuckoo (say i) randomly by Lévyflights;
Evaluate its quality/fitness Fi;
Choose a nest among n (say j) randomly;
if (Fi > Fj)
Replace j by the new solution;
end
Abandon a fraction (pa) of worse nests and build new ones at
new locations via L´evy flights;
Keep the best solutions (or nests with quality solutions);
Rank the solutions and find the current best;
end while
Postprocess results and visualization;
End
15. Comparison with other Meta Heuristic
Algorithms
An important advantage of this algorithm
is its simplicity.
Compared to other metaheuristic
algorithms there is essentially only a
single parameter(Pa) in Cuckoo Search
(apart from the population size n).
It is very easy to implement.
16. Applications
The applications of Cuckoo Search in engineering
optimization.
Solve NP-Hard problems like Traveling Salesman Problem
and Nurse Scheduling Problem.
Spring design and Welded beam design problems.
Solve nurse scheduling problem.
An efficient computation for data fusion in wireless sensor
networks.
A new quantum-inspired cuckoo search was developed to
solve Knapsack problems.
Efficiently generate independent test paths for structural
software testing and test data generation.