Swarm Intelligence Techniques and Their Adaptive Nature with Applications - Complex System Modelling and Control Through Intelligent Soft Computations - page 256

3.3 Arti cial Bee Colony

Karaboga introduced Arti cial Bee Colony (ABC) (Karaboga 2005 ) algorithm by

modeling foraging behavior of bee. Arti cial bee colony have groups of three kind

of bees employed bee, onlooker bee and scout bee. Employed bee and onlooker bee
are responsible for determining amount of food present in the food source. One
employed bee is assigned for every source of food. Employed bee become scout if
food source assigned to it is exhausted.
Basic strategy of ABC consists three steps: (1) move the employed and onlooker
bees onto the food sources to determine food amounts, (2) determine scout bees
from employed bees, and (3) direct scout bees to search new food source. Position
of food source found by scout bees represents a possible solution to the problem
that has to be optimized. Onlooker bees are placed probabilistically to each food
source. As the quality of food source increases, the probability with which the food
source chosen by onlooker bees is also increases. Natural scout bees have no
guidance for exploring new food sources, but arti

cial bee can explore solution

space as a task to find new food sources strategically. This has led to fast discovery

of feasible solution that are represented by food sources. If within the predetermined
number of trials a solution representing a food source does not improved, then that
food source is abandoned by associated employed bee and converted to a scout.
This strategy can be incorporated into generalized SI framework by interpreting
group of solutions as arti cial bees. Divide population into three groups as men-

tioned above and perform task accordingly.

3.4 Gravitational Search Algorithm

Main inspiration of Gravitational search algorithm (GSA) was taken from Newton s

law of universal gravitation and laws of motion. Every point in solution space is
considered as a searching agent having mass. Searching agents of GSA are con-
sidered as objects and their performance is measured by their masses. Due to
gravitational force each object attracts other objects. Object with lower masses
will be attracted towards the object with heavier mass. Heavy masses represent
comparatively better solution and moves slowly. Another fact of physics is also
considered, according to which objects in space do not feel uniform gravitational
force due to varying gravitational constant. Actual value of gravitational constant G
depends on the age of the universe. So gravitational constant G(t) at time t can be
computed as follows:

'

b
;

t 0
t

G ð t Þ ¼G ð t 0 Þ b \ 1 ð 5 Þ