This document discusses the bee algorithm, which is an optimization technique inspired by the foraging behavior of honey bees. It begins with an introduction and overview of concepts like nature of bees, hill climbing, swarm intelligence, and bee colony optimization. It then describes the key steps of the proposed bee algorithm, including initializing a population of solutions, evaluating their fitness, selecting sites for neighborhood search, recruiting bees to search those sites, and iterating until an optimal solution is found. An example application to a traveling salesperson problem is provided. The document concludes that bee algorithm can help provide an optimal solution for problems with many possible solutions, such as in artificial intelligence applications.
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
Bat algorithm is metaheuristic that can be applied for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Arti cial bee Colony algorithm (ABC) is a population based
heuristic search technique used for optimization problems. ABC
is a very e ective optimization technique for continuous opti-
mization problem. Crossover operators have a better exploration
property so crossover operators are added to the ABC. This pa-
per presents ABC with di erent types of real coded crossover op-
erator and its application to Travelling Salesman Problem (TSP).
Each crossover operator is applied to two randomly selected par-
ents from current swarm. Two o -springs generated from crossover
and worst parent is replaced by best o spring, other parent remains
same. ABC with real coded crossover operator applied to travelling
salesman problem. The experimental result shows that our proposed
algorithm performs better than the ABC without crossover in terms
of eciency and accuracy.
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
Bat algorithm is metaheuristic that can be applied for global optimization. It was inspired by the echolocation behaviour of microbats, with varying pulse rates of emission and loudness
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover and selection.
Arti cial bee Colony algorithm (ABC) is a population based
heuristic search technique used for optimization problems. ABC
is a very e ective optimization technique for continuous opti-
mization problem. Crossover operators have a better exploration
property so crossover operators are added to the ABC. This pa-
per presents ABC with di erent types of real coded crossover op-
erator and its application to Travelling Salesman Problem (TSP).
Each crossover operator is applied to two randomly selected par-
ents from current swarm. Two o -springs generated from crossover
and worst parent is replaced by best o spring, other parent remains
same. ABC with real coded crossover operator applied to travelling
salesman problem. The experimental result shows that our proposed
algorithm performs better than the ABC without crossover in terms
of eciency and accuracy.
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.
In this tutorial, we will learn the the following topics -
+ Voting Classifiers
+ Bagging and Pasting
+ Random Patches and Random Subspaces
+ Random Forests
+ Boosting
+ Stacking
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
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS
Optimal k-means clustering using artificial bee colony algorithm with variab...IJECEIAES
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2. Introduction
Nature of the bees
Hill climbing
Swarm based optimization
Bee algorithm
Proposed bee algorithm
Example-bee algorithm
Conclusion
3. Proposing a shortest path
Provide speed and accuracy
Gives optimal solution
Swarm based algorithm
Avoid bottleneck
4. To provide a decision making process to
overcome the bottleneck
The main goal of bees is to provide a optimal
solution for the given set of process
It initiates a waggle dance to communicate
What is a waggle dance?
◦ It is a dance that performed by scout
bees to inform other foraging bees
about nectar site.
5. A group of bees provide a nector from
different places……..
8. A method to occur on with optimal solution.
Provides hill climbing process
it includes ---
Ant colony optimization – (to determine shortest path)
Genetic algorithm –(creates a new set of population)
Particle swarm optimization – (social behaviour of
groups on org.)
Bee algorithm – (occur at optimal solution)
9. It is an optimization technique
It can be used to solve that has many number
of solutions.
Applied to travelling sales person problem to
determine the shortest route with minimal
cost.
Widely used in AI , to know the starting and
goal state.
10. The algorithm that determine the basic
concept of honey bees to occur with the
optimal solution.
11. 1. Initialise population with random solutions.
2. Evaluate fitness of the population.
3. While (stopping criterion not met)
//Forming new population.
4. Select sites for neighbourhood search.
5. Recruit bees for selected sites (more bees for
best e sites) and evaluate fitnesses.
6. Select the fittest bee from each patch.
7. Assign remaining bees to search randomly
and evaluate their fitnesses.
8. End While.
12. The bee algorithm can be explained as with a
simple flow chart…..
13. Masaryk University, Brno, Czech Republic , Wed 08 Apr 2009 13
Evaluate the Fitness of the Population
Determine the Size of Neighbourhood
(Patch Size ngh)
Recruit Bees for Selected Sites
(more Bees for the Best e Sites)
Select the Fittest Bee from Each Site
Assign the (n–m) Remaining Bees to Random Search
New Population of Scout Bees
Select m Sites for Neighbourhood Search
NeighbourhoodSearch
Flowchart of the Basic BA
Initialise a Population of n Scout Bees
14. Step 1
N= no of bees in random manner
(ie) N = 500
15. Step 2
F = fitness
if optimal solution is 500 out of
1000
Then a single bee results with the
50% of its optimal solution
Second bee may have 50% or more
Process continues until it reaches
the optimal solution
16. The optimal solution for the 500 bees is
represented by.,
01 02 03 ……
…
……
…
……
…
500
50% 60% 80% ----
---
----
----
----
----
75%
17. Once stored recognizes the best among the
overall bees
Filtered in ascending order
◦ (ie) from higher priority to lower priority.
21. Population evaluation
an array of 10 values in constructed and
ordered in ascending way from the highest
value of y to the lowest value of y depending
on the previous mathematical function
The best m site is chosen randomly ( the
best evaluation to m scout bee) from n
m=5, e=2, m-e=3
25. Select the best bee from each location (higher
fitness) to form the new bees population.
At the end we reach the best solution as
shown in the following figure
26. Bees = servers
Flower patches = web applications
Advert board = waggle dance
Server = forager/scout
Advert board = the board which fulfills the
request of the corresponding servers.
27. Thus an optimal solution is obtained through
bee’s algorithm in cloud computing.
Provided with speed and accuracy
Better optimal value.
