The document summarizes the Whale Optimization Algorithm (WOA), which is a meta-heuristic optimization algorithm inspired by the hunting behavior of humpback whales. It describes how WOA simulates the bubble-net feeding mechanism of humpback whales to optimize problem solutions. The algorithm includes steps of encircling prey to find the best solution, then exploiting and exploring further to update positions and potentially find an even better solution. WOA iterates through these steps until a termination criterion is met, at which point it outputs the best found solution.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
A presentation on PSO with videos and animations to illustrate the concept. The ppt throws light on the concept, the algo, the application and comparison of PSO with GA and DE.
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 :))
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
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 -
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 :))
This presentation provides an introduction to the Ant Colony Optimization topic, it shows the basic idea of ACO, advantages, limitations and the related applications.
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 -
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.
Two bio-inspired algorithms for solving optimal reactive power problemIJAAS Team
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Articial bee Colony algorithm (ABC) is a population based
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and worst parent is replaced by best ospring, other parent remains
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salesman problem. The experimental result shows that our proposed
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of eciency and accuracy.
Evolutionary and swarm algorithms have found many applications in design problems since todays
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An improved ant colony algorithm based onIJCI JOURNAL
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The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
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2. Outline
Whale Optimization Algorithm (WOA) (History and main idea)
Inspiration and foraging behavior
The basic concepts of the Whale optimization algorithm
Encircling prey
Exploitation phase: bubble-net attacking method
Exploration phase: search for prey
The WOA algorithm
References
3. Whale Optimization Algorithm (WOA)
(History and main idea)
The whale optimization algorithm
(WOA) is a novel meta- heuristics
algorithm proposed by Mirjalili at. el
WOA is a population based method
WOA simulate bubble-net attacking
method of the humpback whales when
they hunting their preys.
4. Inspiration and foraging behavior
Whales are considered as the biggest mammals in
the world.
They are intelligent due to the spindle cells in
their brain.
The whales are living in groups and they are able
to develop their own dialect.
There are 7 types of whales and the humpback
whale is one of these types.
5. Inspiration and foraging behavior (Cont.)
It has a special hunting mechanism which is
called bubble-net feeding method.
This foraging behavior is done by crating a
special bubbles in a spiral shape or (9 shape)
path.
6. The basic concepts of the Whale optimization
algorithm (Encircling prey)
Humpback whales know the location of prey and
encircle them.
They consider the current best candidate solution
is best obtained solution and near the optimal
solution.
After assigning the best candidate solution, the
other agents try to update their positions towards
the best search agent as shown in the following
equation
7. The basic concepts of the Whale optimization
algorithm (Encircling prey) (Cont.)
Where t is the current iteration, A and C are
coefficient vectors, X* is the position vector of the
best solution, and X indicates the position vector of a
solution, | | is the absolute value.
8. The basic concepts of the Whale optimization
algorithm (Encircling prey) (Cont.)
The vectors A and C are calculated as follows:
Where components of a are linearly decreased from
2 to 0 over the course of iterations and r is random
vector in [0; 1]
9. Exploitation phase: bubble-net attacking method
The humpback whales attack the prey with the
bubble-net mechanism.
This mechanism is mathematical formulated as
follow:
{Shrinking encircling mechanism:
In this mechanism, the value of A is a random
value in interval [-a, a] and the value of a is
decreased from 2 to 0 over the course of iterations
as shown in Eq. 9.
10. Exploitation phase: bubble-net attacking method
(Cont.)
Spiral updating position mechanism
In this mechanism, the distance between the
whale location and the prey location is calculated
then the helix-shaped movement of humpback is
created as shown in the following equation
Where D’ =| X*(t) - X(t) | is the distance between
the prey (best solution) and the ith whale, b is a
constant, l is a random number in [-1; 1].
11. Exploitation phase: bubble-net attacking method
(Cont.)
The humpback whales used the mentioned two
mechanisms when they swim around the prey.
We set the mathematical model of these two
mechanisms, we assume that there is a
probability of 50% to chose between these two
mechanisms to update the position of whales as
follow
Where p is a random number in [0; 1]
12. Exploration phase: search for prey
In the exploration phase, the humpback whales
(search agents) search for prey (best solution)
randomly and change their positions according the
position of other whales.
In order to force the search agent to move far a way
from reference whale, we use the A with values > 1
or < 1
The mathematical model of the exploration phase is
as follows
Where Xrand is a random position vector chosen
from the current population.
14. The WOA algorithm (Cont.)
Step 1. The standard whale optimization algorithm starts by setting the
initial values of the population size n, the parameter a, coefficients A and
C and the maximum number of iterations max_itr. line 1.
Step 2. Initialize the iteration counter t. lines 2.
Step 3. The initial population n is generated randomly and each search
agent Xi in the population is evaluated by calculating its fitness
function f(Xi). lines 3-6.
Step 4. Assign the best search agent X. line 7
Step 5. The following steps are repeated until the termination criterion
Satisfied. lines 9-30
15. The WOA algorithm (Cont.)
Step 5.1. The iteration counter is increasing t = t + 1. line 9
Step 5.2. All the parameters a,A, C, l and P are updated. line 11
Step 5.3. The exploration and exploitations are applied according to the
values of p and | A | line 12-27
Step 6. The best search agent X is updated. line 28
Step 7.The overall process is repeated until termination criteria satisfied.
line 29
Step8. Produce the best found search agent (solution) so far X. line 30
16. References
S. Mirjalili and A. Lewis, The Whale Optimization
Algorithm. Advances in Engineering Software, 95, (2016),
pp.51-67