The document presents a summary of the Gravitational Search Algorithm (GSA), an optimization technique inspired by Newton's law of gravity. It discusses how GSA works, its advantages and disadvantages, and how hybridizing GSA with other algorithms can help overcome some of its limitations, such as slow convergence. Examples of GSA hybridization are provided for applications like clustering, classification, feature selection, neural networks, and power systems optimization. The document concludes by stating that GSA has greatly impacted many fields and remains a powerful swarm-based optimization technique that could be further improved through additional hybridization.

1. Presented by
Sajad Ahmad Rather
Full Time Research Scholar
Department of Computer Science
School of Engineering and Technology
Pondicherry University
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2.  Objective
 Gravitational Search Algorithm (GSA)
 Advantages and Disadvantages of GSA
 GSA Hybridization
 Discussion
 Conclusion and Future Scope
 References
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3.  A Gravitational search algorithm is a physics-based
heuristic algorithm inspired by Newton’s gravity law. GSA is
good at finding the global optimum but has the drawbacks
of slow convergence speed and getting stuck in local
minima in last iterations.
 To overcome these problems, the GSA is hybridized with
other swarm based optimization algorithms and it results in
the increase in searching capability, problem-solving and
application domains of the gravitational search algorithm.
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4.  The GSA has been used to solve various optimization
problems in different application areas such as
clustering, classification, feature subset selection, load
power dispatch, routing, etc. and it shows better
performance than other swarm intelligence algorithms.
 This paper gives information about the GSA and its
hybridization with other meta-heuristic algorithms.
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5.  The Gravitational Search Algorithm (GSA) is a powerful
heuristic optimization method, which is based on the
concept of mass interaction i.e. “a particle in the universe
attracts every other particle with a force that is directly
proportional to the product of their masses and inversely
proportional to the square of the distance between
them”.
 If and are two point masses, R is the distance
between them, then the gravitational force, F is
calculated by using equation as under:
1 2
2
M M
F G
R

1M 2M
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7. Advantages of GSA
 Simple implementation.
 Suitable swarm-based algorithm for solving non-
linear optimization problems.
 It takes less computational time.
 Generation of feasible solutions.
 Adaptive learning capability.
 Gives high precision results.
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8. Disadvantages of GSA
 The GSA operators are complex in nature.
 The searching capability of GSA gets slower in the
last iterations of the algorithm.
 It is not flexible due to the inactivity after
convergence.
 Randomized nature of Gravitational operator (G) in
the algorithm.
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9.  Hybridization is the technique of modifying the
mathematical structure of the parent algorithm by
using another optimization algorithm(s) so that the
limitations of the parent algorithm can be
removed.
 The main advantage of hybridization is to increase
the search space and the problem-solving domain
of the algorithm.
 It also improves the exploration and exploitation
capabilities of the algorithm.
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10. Scope of GSA Hybridization
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11. Clustering
 Clustering is the technique of grouping similar data samples based
on distance and similarity criterion of the data points.
 The K-harmonic clustering is the most commonly used clustering
algorithm because it has simple implementation and takes fewer
iterations. But there is a major drawback in K-harmonic algorithm i.e.
its dependency on initial states of the data centers.
 Here, GSA helps the clustering algorithm (i.e. KH means) to get away
from “trapping local optima” problem and also increases its
convergence speed.
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12. Classification
 Classification is the process of categorizing the data into groups
based on mathematical information. It is basically a technique of
finding the patterns in data and is a pattern recognition method.
 The GSA is combined with K-nearest neighbor for the classification
of data. The GSA provides the randomized initialization of the search
space and increases the optimization of the features. The hybrid
technique is tested on 12 benchmark datasets.
 Prototype generation is the process of reducing the dimension of the
class samples used for decision making. The GSA is hybridized with
K- nearest neighbor for classification of prototypes.
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13. Feature Selection
 Feature selection is one of the fundamental steps in the
classification process in data mining. It simply means to
get the important features from the data set that can
reduce the dimensionality and search space of the
problem.
 It is carried out using GSA, a swarm based technique and
Optimum-Path Forest (OPF), a powerful pattern
recognition method. The GSA acts as the optimization
tool that helps in finding the pattern(s) in the search
domain and maximizes the output given by OPF.
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14. Neural Networks
 The GSA is used with a genetic algorithm for training the
neural network. It is used for performing the global
search in order to find the global optimum and then
genetic algorithm is used for performing the local search
around the solution. The hybrid algorithm shows better
performance than the back propagation algorithm.
 GSA is hybridized with PSO for training the multilayer
FNN. The hybridization results in good convergence
speed and avoidance from the “trapping in local optima”
problem for GSA.
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15. Power Systems
 GSA and PSO are used to solve the load dispatch
problem in power systems by considering some
constraints such as Generator rate, transmission
loss, etc. The proposed algorithm shows better
performance than other power system optimization
algorithms.
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16. Routing
 The GSA is a memory less algorithm i.e. it’s searching
operator considers only the current position of the
agents. This problem can be solved by using PSO which is
a memory based algorithm.
 To increase the quality of the optimal solutions, the
social operators of the PSO are combined with GSA
operators.
 The improved GSA-PSO hybrid algorithm is used for path
planning which is a global optimization problem.
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17. Optimization
 Optimization is the technique of selecting the most
feasible solution for the given problem. The GSA is also
hybridized with other algorithms for solving the
optimization problems in different fields.
 The artificial immune system (AIS) algorithm is hybridized
with GSA in order to overcome the drawback of local
minima trapping in GSA.
 Gravitational operators are used for increasing the fitness
value of the particles in the PSO algorithm. The hybrid
PSO-GSA hybrid algorithm results in the decrease in
computational cost and increase in the feasibility and
efficiency of the PSO.
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18. GSA Hybridization Applications
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19. Year wise analysis of GSA Hybridization
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20.  This paper provided a comprehensive survey of GSA and
its hybridization with other optimization techniques.
Now, it can be concluded that GSA is a powerful swarm
based optimization technique that has profoundly
impacted the various application areas of different fields
of study whether it be clustering, classification, feature
extraction, Routing, and Neural Networks in computer
science or Emission load power dispatch and power
distribution in power systems.
 GSA is an ideal population-based algorithm suitable for
solving different optimization problems that were
previously tackled using classical optimization techniques
such as GA and PSO; with higher accuracy and efficiency.
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21.  This fact can never be underestimated that GSA has a lot
to offer preferably to the computer science community as
it can be hybridized with other heuristic algorithms such
as Biogeography-based optimization, Bacterial Foraging
Algorithm, and Grey Wolf Algorithm because these
optimization techniques can be used for overcoming the
drawbacks such as global optimization and premature
convergence of GSA.
 In recent years, it can be observed that Big Data, IoT,
Cloud Computing and Information Security are hot areas
of computer science and quite popular in the research
community. The optimization capability of GSA can be
utilized in these areas.
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30. SUGGESTIONS
&
QUERIES
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31. THANK YOU
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