The shuffled frog leaping algorithm is an evolutionary algorithm inspired by the behavior of frogs searching for food. It works by first randomly generating a population of solutions and dividing them into groups. Each group conducts a local search, and the best solutions are shared among groups in shuffling processes. This continues until a convergence threshold is reached. The algorithm has applications in optimization problems like power grid design, construction scheduling, and water network planning by evaluating many potential solutions efficiently.

1. OPTIMIZATION-Shuffled Frog
Leaping Algorithm

2.  It’s a procedure to make a system or
design more effective, especially
involving the mathematical techniques.
 To minimize the cost of production or
to maximize the efficiency of
production.
What is optimization....?

3. It’s a technique to:
 Find Best Solution
 Minimal Cost
 Minimal Error
 Maximal Profit
 Maximal Utility
Optimization problems are solved by using
rigorous or approximate mathematical search
techniques.

4.  Mathematical optimization
1) Linear Programming
2) Dynamic Programming
 Evolutionary algorithms- That mimic
the metaphor of natural biological
evolution and the social behaviour of
species.
Methods of Optimization

5.  Genetic algorithms - ‘Survival of the
genetically fittest’
 Memetic algorithms- ‘Survival of the
genetically fittest and most experienced’
 Particle swarm- ‘Flock migration’
 Ant colony- ‘Shortest path to food
source’
 Shuffled frog leaping- ‘Group search of frogs
for food’
Types of Evolutionary algorithms

6. Types.......

7. What is shuffled
frog leaping
algorithm...?
Oh...! Simple..They are
just observing our food
searching nature.
The SFLA is a method which is based on observing,
imitating, and modelling the behaviour of a group of
frogs when searching for the location that has the
maximum amount of available food .

8.  Population consists of a set of frogs
 Partitioned into subsets referred to as memeplexes
 Each memeplexes performing a local search
 After a defined number of evolution steps, ideas
are passed among memeplexes in a shuffling
process
 The local search and the shuffling processes
continue until defined convergence criteria are
satisfied
Process of SFLA

9. 1. Population of P frogs is created randomly
2. A frog i is represented as xi (xi1, xi2,., Xi)
3. Sorted in a descending order according to
their fitness.
4. Population is divided into m memeplexes,
each containing n frogs.
P=m × n
5. Frogs with the best and the worst fitnesse
are identified as xb and xw
Analytical Process

10.  Change in frog position (Di) = rand( )× (Xb-Xw)
 Previous position Xw
 New position= Xw + Di;
 If no improvement becomes possible in this case,
then a new solution is randomly generated to
replace that frog.
 The calculations then continue for a specific
number of iterations.

11.  Ac-dc optimal power flow
 Scheduling of construction projects
 Computer-aided design activities
 Water distribution network design
Application of SFLA

12. Comparison among different
Evolutionary Algorithms

13.  SFLA has been used as appropriate tools to
obtain the best solutions with the least total
time and cost by evaluating unlimited possible
options.
 Implementation of evolutionary algorithms in
various field because of their reliability and
simple implementation
CONCLUSION

14.  Eusuff M. M., Lansey K.E. ,Shuffled Frog
Leaping Algorithm: A Memetic Metaheuristic
for Discrete Optimization, J. Eng.
Optimization, 2006.
 Eusuff, M.M. and Lansey, K.E., Optimization
using shuffled frog leaping algorithm. , 2003
 Elbeltag , T. and Grierson Comparison among
five evolutionary-based optimization
algorithms. J. Adv. Engg. Informatics, 2005.
REFRENCES

15. THANK YOU