A CRITICAL STUDY ON TEN NONTRADITIONAL OPTIMIZATION METHODS IN SOLVING ENGINEERING PROBLEMS

http://www.iaeme.com/IJMET/index.asp 233 editor@iaeme.com
International Journal of Mechanical Engineering and Technology (IJMET)
Volume 9, Issue 11, November 2018, pp. 233–249, Article ID: IJMET_09_11_025
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=11
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
A CRITICAL STUDY ON TEN NON-
TRADITIONAL OPTIMIZATION METHODS IN
SOLVING ENGINEERING PROBLEMS
Rejula Mercy.J
Scholar, Karunya Institute of Technology and Sciences
Assistant Professor in Mathematics, PSGR Krishnamal College for women, Coimbatore
S. Elizabeth Amudhini Stephen
Associate Professor, Department of Mathematics Karunya Institute of Technology and
Sciences, Coimbatore
ABSTRACT
The objective functions used in Engineering Optimization are complex in nature with
many variables and constraints. Conventional optimization tools sometimes fail to give
global optima point. Very popular methods like Genetic Algorithm, Pattern Search,
Simulated Annealing, and Gradient Search are useful methods to find global optima
related to engineering problems. This paper attempts to review new nontraditional
optimization algorithms which are used to solve such complicated engineering problems
to obtain global optimum solutions
Keywords: Ant Lion Optimizer, Grey Wolf Optimizer, Dragonfly Optimization
Algorithm, Firefly Algorithm, Flower Pollination Algorithm, Whale Optimization
Algorithm, Cat Swarm Optimization, Bat Algorithm, Particle Swarm Optimization,
Gravitational Search Algorithm
Cite this Article Rejula Mercy.J and S. Elizabeth Amudhini Stephen, a Critical Study on
Ten Non-Traditional Optimization Methods in Solving Engineering Problems,
International Journal of Mechanical Engineering and Technology, 9(11), 2018, pp. 233–
249.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=11
1. ANT LION OPTIMIZER
Ant lion optimization (ALO) is a novel nature inspired algorithm. The ALO algorithm mimics
the hunting mechanism of ant lions in nature. Five main steps of hunting prey such as the random
walk of ants, building traps, entrapment of ants in traps, catching preys, and re-building traps are
implemented. Ant lions are called as doodlebugs. They are under the Myrmeleontidae family and
live in two phases of larvae and adult. Their hunt mechanism is interesting when they are larvae.
The small cone-shape trapped that we see in nature are made by ant lions to trap ants. Ant lions
sit under the pit and wait for prey to be trapped. After consuming the prey’s flesh, ant lions throw
the leftovers outside the pit and amend the pit for the next hunt. It has been observed that ant lions

Rejula Mercy.J and S. Elizabeth Amudhini Stephen
tend to dig a bigger pit when they are hungry and this is exactly the main inspiration for ALO
algorithm.
The ALO has three main advantages:
It can find nearest possible optimal solution
Its convergence rate is fast due to the employed random walk and roulette wheel selection
mechanisms.
it can handle non linear and multi-modal problems.
1. Ant lion optimization (ALO) algorithm is tested in the standard 10-unit generating
system with 10% spinning reserve requirements. The convergence speed and the total
operating cost results are compared with Dynamic Programming (DP) and it is found
that ALO is faster, saves cost and time than DP(I. N. Sam’on, Z. M. Yasin, Z.
Zakaria,2017).
2. In Renewable Distributed Generation ALO algorithm is used to reduce the power
losses and to enhance the voltage profiles and stability index and the algorithm is
compared with others to feature its benefits in minimizing total power losses and
maximizing the net saving. Validity of the algorithm is proved through statistical
Wilcoxon test (E.S. Ali , S.M. Abd Elazim, A.Y. Abdelaziz, 2016). ALO as the
trainers for Multi-Layer Perceptron(MLP) is used to find the weights and biases of the
MLP to achieve a minimum error and a high classification rate compared with other
algorithms. It can efficiently solve the local minimum problem (Waleed Yamany,
Alaa Tharwat, Mohamed Fawzy, Tarek Gaber, and Aboul Ella Hassanien, 2016).
ALO algorithm is used in solve non linear electrical economic power dispatch
planning it is found that less fuel cost and power generation results is superior
compared to other algorithms (Navpreet Singh Tung, Sandeep Chakravorty, 2016).
The paper multi-objective economic emission load dispatch has been solved using
ALO algorithm. ALO algorithm can evade the deficiency of early convergence to get
superior solutions in comparison to other algorithms (Arjun Rathore, Nitish Chopra,
2017).
ALO algorithms is used in process planning problem and the results are comparison with
other algorithms.The performs of ALO algorithms is superior in comparison with algorithms.
(Petrović, M., Petronijević, J., Mitić, M., Vuković, N., Plemić, A., Miljković, Z., Babić, B, 2015)
In this paper many algorithms are compared with Ant Lion Optimization algorithm for
solving Economic dispatch problems and the result shows that cost optimization f ALO is greater
than PSO. (Faseela C.K. and Dr. Vennila, 2017). Optimal undervoltage load shedding using ALO
finds that by shedding an appropriate amount of load it help to improve the system performance
in power loss minimization and voltage profile improvement. (Zuhaila Mat Yasin, Hasmaini
Mohamad, Izni Nadhirah Sam’on, Norfishah Ab Wahab and Nur Ashida Salim, 2017). ALO has
been used to solve the Optimal load dispatch (OLD) problems and the results is compared with
other techniques shows that ALO has good convergence property and its efficiency to solve OLD
problems (Menakshi Mahendru Nischal , Shivani Mehta, 2015).
A procedure for employing an ALO method is developed for optimum design of truss
structures. The comparing between different results obtained by the other algorithms with the
ALO shows advantages and disadvantages of the new method in solving structures. (S. Talatahari,
2016)
2. GREY WOLF OPTIMIZER

A Critical Study on Ten Non-Traditional Optimization Methods in Solving Engineering Problems
Grey wolf optimizer (GWO) is a population based meta-heuristics algorithm simulates the
leadership hierarchy and hunting mechanism of gray wolves in nature proposed by Mirjalili et al.
in 2014. Grey wolves are considered as apex predators, which they are at the top of the food
chain. Grey wolves prefer to live in groups (packs); each group contains 5-12 members on
average. All the members in the group have a very strict social dominant hierarchy.
Alpha: The leaders either a male or a female, called alphas. The alpha is mostly responsible
for making decisions about hunting, sleeping place, time to wake, and so on. The alpha’s
decisions are dictated to the pack.
Beta: The second level in the hierarchy of grey wolves is beta. The betas are subordinate
wolves that help the alpha in decision-making or other pack activities.
Omega: The lowest ranking grey wolf is omega. The omega plays the role of scapegoat.
Omega wolves always have to submit to all the other dominant wolves.
Delta: If a wolf is not an alpha, beta, or omega, he/she is called subordinate (or delta in some
references). Delta wolves have to submit to alphas and betas, but they dominate the omega.
Scouts, sentinels, elders, hunters, and caretakers belong to this category. In addition to the social
hierarchy of wolves, group hunting is another interesting social behavior of grey wolves. The
main phases of gray wolf hunting are as follows:
• Tracking, chasing, and approaching the prey
• Pursuing, encircling, and harassing the prey until it stops moving
• Attack towards the prey
A new image segmentation algorithm is proposed based on Grey Wolf Optimization (GWO).
Simulation results on two satellite images of New Delhi reveal that the GWO gives better results
in less time (Shubham Kapoor, Irshad Zeya, Chirag Singhal, Satyasai Jagannath Nanda, 2017).
Grey Wolf Optimizer (GWO) algorithm for quality improvement of the low dynamic range
images provide better enhanced output image and preserve brightness of the enhanced image very
close to the input image and the resulted images were suitable for consumer electronic products
(J.Vinothini, R. Ashok Bakkiyaraj, 2016). Mathematical framework to formulate a Cumulative
Index (CI) on the basis of an individual concentration of four major pollutants and a GWO
algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to
classify air quality into two types, that is, good or harmful and inputs are the calculated values of
CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and
Bhopal. It is observed that the classifier performs well to classify the quality of air (Akash Saxena
and Shalini Shekhawat, 2017). A hybrid Electroencephalogram (EEG) classification approach
based on grey wolf optimizer (GWO) enhanced support vector machines (SVMs) called GWO-
SVM approach for automatic seizure detection outperformed genetic algorithm GA-SVM and the
typical SVMs classification algorithm for RBF kernel function. GWO-SVM is efficient for
seizure detection in EEG signals. (Asmaa Hamad, Essam H. Houssein, Aboul Ella Hassanien,
and Aly A. Fahmy, 2018)
The GWO with invasion-based migration operation algorithm have more pack and have
migrated between them. The results showed that the algorithm is capable of efficiently to solving
optimization problems compared with other algorithms. (Duangjai Jitkongchuen, Pongsak
Phaidang, Piyalak Pongtawevirat, 2016). The design of modular granular neural network
(MGNN) architecture is performed and applied to human recognition based on ear, face, and iris,
but using a GWO. MGNN design was easily adaptable depending of the number of persons and
the number of images independently of the biometric measure used and provides better results
when applied to human recognition compared to GA and FA (Daniela Sánchez, Patricia Melin,
and Oscar Castillo, 2017)

An improved GWO (IGWO) algorithm for global optimization problems was studied and it
provided highly competitive results compared to standard GWO algorithm and HGWO
algorithm. (LONG Wen, 2016). A new optimal PID controller design is designed using the GWO
algorithm. DC Motor drive speed is controlled by GWO controller and results showed that the
designed controller can be implemented as an efficient search for the optimal PID controller. A
comparison between PID-GWO and PID-PSO controllers, it shows that the proposed technique
can enhance the dynamic performance of the system in a better way. The PID-GWO controller is
the best which illustrated satisfactory performances and possesses good robustness toward PSOs.
(Ali Madadi and Mahmood Mohseni Motlagh, 2014). This paper proposed a novel GWO based
on Powell local optimization method, namely, PGWO compared to other algorithms it is superior
so it use it in solving clustering problems (Sen Zhang and Yongquan Zhou, 2015)
A new SI algorithm namely GWO is employed for short term crude oil and gasoline price
forecasting. GWO produces a better forecast for gasoline price and crude oil as compared to
Artificial Bee Colony (ABC) and Differential Evolution (DE). (Yuhanis Yusof, Member,
IAENG and Zuriani Mustaffa, 2015)
3. DRAGONFLY OPTIMIZATION ALGORITHM
The main inspiration of the Dragonfly Algorithm (DA) algorithm proposed in 2015 originates
from static and dynamic swarming behaviors. These two swarming behaviours are very similar
to the two main phases of optimization using meta-heuristics: exploration and exploitation.
Dragonflies create sub swarms and fly over different areas in a static swarm, which is the main
objective of the exploration phase. In the static swarm, however, dragonflies fly in bigger swarms
and along one direction, which is favourable in the exploitation phase. For simulating the
swarming behaviour of dragonflies, three primitive principles of swarming in insects proposed
by Reynold as well as two other new concepts have been utilized: separation, alignment,
cohesion, attraction to food source, distraction from enemies. These five concepts allow us to
simulate the behaviour of dragonflies in both dynamic and static swarms. The DA algorithm is
developed based on the framework of the Particle Swarm Optimization (PSO) algorithm, so there
are two main vectors: step vector and position vector. These vectors store the movement
directions/speed and position of dragonflies, respectively.
The paper is of dragonfly algorithm also of binary and multi-objective versions of DA called
binary DA (BDA) and multi-objective DA (MODA). The algorithms are able to outperform the
well-known and powerful algorithms and MODA is effective in solving real problems with
unknown search spaces(Seyedali Mirjalili, 2016) This paper gets the use of DA, as one of
metaheuristic algorithm, in wireless localization and with proper adjustment, the error can be
lessened then DA can perform effectively for wireless localization. (Philip Tobianto Dael , Soo
Yonng Shin, 2017). Improved dragonfly algorithm is compared with the basic DA and other
similar algorithms have a good performance, accuracy and minimized run time. (Fatemeh Bandi,
Mahdi Yaghoobi, 2018)
This paper presents the optimal location for placement of FACTS device problem and is
solved using a new heuristic algorithm called the Dragonfly Algorithm. The Dragonfly Algorithm
is used for finding out the optimal locations and parameter settings of Thyristor Controlled Series
Compensator (TCSC) devices, to achieve minimum transmission line losses in the system. The
Dragonfly Algorithm results are compared with the results of the Genetic Algorithm (GA) and
the Particle Swarm Optimization (PSO) techniques it gives better voltage profile improvement
and better reduction in transmission line losses and it is easy to use and best optimization
technique (Mr.A.Hema Sekhar, Dr.A.Lakshmi Devi, 2016). Dragonfly Algorithm achieves the
environmental protection goal along with fuel economy considerations. Compared with other
meta-heuristic optimization techniques it perform better. (Ajay Kumar Pathania, Shivani Mehta,

Chintu Rza, 2016). In this paper Dragonfly Algorithm method has been used to solve the problem
optimum location and size of distributed generation(DG) in distribution systems. Two types of
the DGs are studied and investigated. The proposed method is established and validated finished
in distribution networks with different sizes and complexities (Shimaa.A.Hussien, M.A.Ebrahim,
Hassan M.Mahmoud, Ebtisam M. Saied, M.M. Salama, 2017). In this paper comparing with PSO
algorithm and dragonfly algorithm, results show that the dragonfly algorithm can obtain better
robust performance on LFC with complex operation situations, namely, random renewable
energy generation and continuous load disturbances. (M. Venkatesh, G. Sudheer, 2017). In this
paper DA algorithm based approach DA-SVM model for parameters optimization of SVM
improves the classification rate and is capable to find the optimal values of the SVM parameters
and avoids the local optima problem compared to other algorithm. (Alaa Tharwat, Thomas Gabel,
Aboul Ella Hassanien, 2018). The ECED problem is solved using Dragonfly algorithm using six
different penalty factors and their effect is analyzed on IEEE-30 bus system and fuel cost is less
when using “Min-Max” Price penalty factor, “Max-Max” price penalty factor is good to yield a
minimum emission, “Max-Min” price penalty factor is good to get lowest transmission loss
compared to other penalty factors (R.H.Bhesdadiya, Narottam Jangir, Mahesh H. Pandya,
Pradeep Jangir, Indrajit N Trivedi, Arvind Kumar, 2016)
This paper develop a new reconfiguration scheme that minimizes the net deviation between
the node voltages and the nominal voltage value, using DO. The method is tested on 33- and 69-
node radial networks and the results are two test systems have exhibited that the proposed DORM
is able to provide enhanced VP without any extra infrastructural facilities. (T. K. Abhiraj, P.
Aravindhababu, 2017)
4.FIREFLY ALGORITHM
Firefly Algorithm (FA) is an emerging metaheuristic swarm optimization technique based on the
natural behavior of fireflies. FA is invented by Xin-She Yang for solving multimodal
optimization problem. The development of FA is based on two important issues: formulation of
the attractiveness and the variation of light intensity. There are about two thousand firefly species
where the flashes often unique for a particular species. The flashing light is produced by a process
of bioluminescence where the exact functions of such signaling systems are still on debating. The
two fundamental functions of such flashes are to attract mating partners (communication) and to
attract potential prey.
Three rules are introduced in FA development. They are
1. all fireflies are unisex so that one firefly will be attracted to other fireflies regardless
of their sex
2. attractiveness is proportional to their brightness, thus for any two flashing fireflies,
the less brighter one will move towards the brighter one
3. the brightness of a firefly is affected by the landscape of the objective function
For maximization problem, the brightness can simply be proportional to the value of the
objective or fitness function. This paper presents the fundamentals of firefly algorithm, the latest
developments with diverse applications. (Xin-She Yang, Xingshi He, 2013).This paper presents
Firefly algorithm for application to the multi objective minimization problem of economic
emissions load dispatch. The algorithm achieved good results compared to other algorithms
(Theofanis Apostolopoulos and Aristidis Vlachos, 2011). This paper presents a new reversible
database watermarking method using Firefly Algorithm to both minimize distortion and improve
robustness of DEW called FFADEW. FFA reduces complexity and has less distortion and
improved watermark capacity compared to other methods. (Mustafa Bilgehan Imamoglu,
Mustafa Ulutas, and Guzin Ulutas, 2017)

Firefly algorithm is analyzed on basis of performance and success rate using five standard
benchmark functions. (Sankalap Arora, Satvir Singh, 2013). This paper presents improve the
efficiency of FA, other ways of calculating the distance between the points and other functions
to compute the attractiveness of fireflies were tested and analyzed. A set of 30 benchmark
experiments reveal that the obtained results are competitive when comparing with the original
FA version. (Rogério B. Francisco, M. Fernanda P. Costa, Ana Maria A. C. Rocha, 2014). This
paper presents a GPU-based FA (FA-MLR) for selection of variables in protein determination
problem in whole grain wheat samples. In addition a variable selection problem in simulated
study was presented. The results also demonstrated that the FA-MLR performed in a GPU can
be five times faster than its sequential implementation. (Lauro C. M. de Paula, Anderson S.
Soares, Telma W. de Lima, Alexandre C. B. Delbem, Clarimar J. Coelho, Arlindo R. G. Filho,
2014)
This paper, optimal multilevel image thresholding problem is addressed using Otsu guided
firefly algorithms. The results show that BD guided FA provides better objective function, PSNR,
and SSIM, whereas LF based FA provides faster convergence with relatively lower CPU time.
(N. SriMadhava Raja, V. Rajinikanth and K. Latha, 2014). This paper presents a modified firefly
algorithm (FA) for cardinality constrained mean-variance (CCMV) portfolio optimization with
entropy constraint. Modified firefly algorithm proved to be better than other algorithms, while
introduction of entropy diversity constraint further improved results (Nebojsa Bacanin and Milan
Tuba, 2014)
This paper presents the new Firefly Algorithm and to provide the comparison study of the FA
with PSO and other relevant algorithms. FA is potentially more powerful in solving NP-hard
problems and in terms of both efficiency and success rate it is superior to both PSO and GA.
(Xin-She Yang, 2009). This paper presents detailed study of Firefly Algorithm (FA), its
experimental evaluation and possible improvements and for constrained continuous optimization
tasks. (Szymon Lukasik and Slawomir Zak, 2009)
5. FLOWER POLLINATION ALGORITHM
Flower pollination algorithm (FPA) is a nature inspired population based algorithm proposed by
Xin-She Yang (2012). The main objective of the flower pollination is to produce the optimal
reproduction of plants by surviving the most fittest flowers in the flowering plants. In fact this is
an optimization process of plants in species. There are over a quarter of a million types of
flowering plants in Nature, 80% of them are flowering species. The main purpose of a flower is
ultimately reproduction via pollination. Flower pollination process is associated with the transfer
of pollen by using pollinators such as insects, birds, bats,...etc.
There are two major process for transferring the pollen
• Biotic and cross pollination process.
• Abiotic and self pollination Process
Biotic pollination represents 90% of flowering plants, while 10% of pollination takes from
abiotic process. In the biotic pollination, pollen is transferred from one flower to other flower in
different plant by a pollinator such as insects, birds, bats,…etc. Biotic, cross-pollination may
occur at long distance and they can considered as a global pollination process with pollinators
performing Le'vy flights.
Abiotic or self pollination process is a fertilization of one flower from pollen of the same
flower of different flower of the same plant. In this type of pollination, wind and diffusion in
water help pollination of such flowering plants. Abiotic and self pollination process are
considered as local pollination. This paper presents a new algorithm based on the flower
pollination process of flowering plants. Ten test functions for validation were compared and

results show that flower algorithm is more efficient than both GA and PSO. (Xin-She Yang,
2012). FPA is a feasible algorithm for optimization in structural engineering by providing better
designs with less computing time and improving the robustness of finding the best optimum
values.(Sinan Melih Nigdeli, Gebrail Bekdaş and Xin-She Yang, 2016)
The paper presents novel nature-inspired metaheuristics of Flower Pollination Algorithm. The
experimental evaluation of its sensitivity to parameter values and performance of the benchmarks
are compared with PSO algorithm (Szymon Lukasi and Piotr A. Kowalski, 2014). This paper
presents FPA is it implemented for different ELD problems. The numerical results show that the
proposed algorithm gives better results compared with other algorithms (R. Prathiba, M.
Balasingh Moses , S. Sakthivel, 2014). In this paper, three optimization strategies (local
neighborhood search strategy, dimension by dimension evaluation and improvement strategy,
and dynamic switching probability strategy) have been applied to FPA to improve its deficiencies.
By 12 typical standard benchmark functions simulation result show that algorithm generally has
strong global searching ability and local optimization ability, and effectively avoid the defects of
other algorithms fall into local optimization. (RuiWang and Yongquan Zhou, 2014)
This paper present the performance of HEM using CPP on the basis of BFOA and FPA and
show that hybrid technique performed better both for single and multiple homes. (Muhammad
Awais, Nadeem Javaid, Abdul Mateen, Nasir Khan, Ali Mohiuddin, Malik Hassan Abdul
Rehman, 2018). This paper introduced the flower pollination algorithm for the optimization of
linear antenna arrays. FPA was applied to obtain optimized antenna positions in order to achieve
desired array pattern with minimum SLL along with null placement in specified directions.
(Prerna Saxena and Ashwin Kothari, 2016). This paper presents a new algorithm, FPA-
simplification in order to improve computing time of simplification process. Results from the
experiment shows FPA-simplification has improved standard simplification performance in
reducing computing time from 61% to 75%. (Dhabitah Lazim, Azlan Mohd Zain and Abdullah
Hisham Omar, 2016). This paper is a review which explores the FPA efficiency by
implementation or by hybridization with other algorithms, the studies prove that FPA is a
powerful tool in solving several optimization problems. Based on its applications in the field of
optimization this algorithm has a better convergence speed compared to other algorithms. (Nabil
Diab and Emad El-Sharkawy, 2016). This paper presents using nature inspired flower pollination
algorithm (FPA) a method of circular array synthesis. The numerical results show that the
proposed algorithm is effective compared with genetic algorithm and uniform circular array
antenna (UCAA) with uniform spacing. (V. S. S. S. Chakravarthy Vedula, S. R. Chowdary
Paladuga, and M. Rao Prithvi, 2015)
6. WHALE OPTIMIZATION ALGORITHM
The Whale optimization algorithm (WOA) is a novel metaheuristics algorithm proposed by
Seyedali Mirjalili and Andrew Lewis in 2016. WOA is a population based method. WOA
simulate bubble-net attacking method of the humpback whales when they hunting their preys.
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. 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. 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. WOA was found to be
enough competitive with other state-of-the-art meta-heuristic methods. WOA is tested with 29
mathematical optimization problems and 6 structural design problems. Optimization results show
that the WOA algorithm is very competitive compared to other algorithms as well as conventional

methods. (Seyedali Mirjalili , Andrew Lewis, 2016) In this paper the inertia weight is introduced
into WOA and improved whale optimization algorithm(IWOA) is proposed. IWOA performs
significantly better than ABC and FOA. (Hongping Hu Yanping Bai Ting Xu, 2016). This paper
introduces nonlinearly distance control parameter strategies to design improved WOA algorithm.
Six benchmark test functions were employed to verify the performance of the proposed improved
WOA algorithm. (Minghui Zhong and Wen Long, 2017). In this paper, the problem Combined
Heat and Power Economic Dispatch (CHPED) is explained using Whale Optimization algorithm
(WOA). The results are then compared with the other algorithms. (T. Kalaipriyan, J. Amudhavel
And P. Sujatha, 2017) This paper present the work of WOA is implemented for standard IEEE-
30 bus test system to solve the Optimal Power Flow problem. The results are then compared with
FPA and PSO show that WOA gives better optimization values.(R. H. Bhesdadiya, Siddharth A.
Parmar, Indrajit N. Trivedi, Pradeep Jangir, Motilal Bhoye and Narottam Jangir, 2016). The
WOA method is programmed in MATLAB domain and the effectiveness of this algorithm for
cost minimization and loss reduction by placing capacitors optimally is tested on 34-bus and 85-
bus radial distribution test systems and the results show that it is effective in bringing down the
operating costs and in maintaining better voltage profile. (D.B. Prakash , C. Lakshminarayana,
2017)
In this paper whale optimization algorithm is used to determine the optimal DG size. Better
results have been achieved with WOA when compared with other algorithms and it is efficient
and robust. (P. Dinakara Prasad Reddy , V. C. Veera Reddy and T. Gowri Manohar, 2017). In
this paper, the computation of the optimum weights is approached as a mapping problem which
can be modelled using Radial Basis Function. The performance of the system is observed as the
sharp beams towards the PRB. (Anitha S Sastry, Dr. Akhila S, 2017). This paper presents WOA
to determine the optimal solution for the ED problem. The algorithm has been tested on system
of IEEE 30- Bus with six generating thermal units. (Haider J.Touma, 2016). WOA algorithm has
quality feature that it uses logarithmic spiral function so it covers a broader area in exploration
phase then addition with powerful randomization adaptive technique potent the adaptive whale
optimization Algorithm (AWOA) to attain global optimal solution and faster convergence with
less parameter dependency. (Dilip P. Ladumor, Pradeep Jangir, Jangir Narottam, Indrajit N
Trivedi, Arvind Kumar, 2011)
7. CAT SWARM OPTIMIZATION
Cat Swarm Optimization (CSO) is one of the new heuristic optimization algorithm which based
on swarm intelligence. According to the classification of biology, there are about thirty-two
different species of creatures in feline, e.g. lion, tiger, leopard, cat etc. Though they have different
living environments, there are still many behaviors simultaneously exist in most of felines. In
spite of the hunting skill is not innate for felines, it can be trained to acquire. For the wild felines,
the hunting skill ensures the survival of their races, but for the indoor cats, it exhibits the natural
instinct of strongly curious about any moving things. Though all cats have the strong curiosity,
they are, in most times, inactive. If you spend some time to observe the existence of cats, you
may easily find that the cats spend most of the time when they are awake on resting. The alertness
of cats are very high, they always stay alert even if they are resting. Thus, you can simply find
that the cats usually looks lazy, lying somewhere, but opening their eyes hugely looking around.
On that moment, they are observing the environment. They seem to be lazy, but actually they are
smart and deliberate. In Cat Swarm Optimization, we first model the major two behaviors of cats
into two sub-models, namely, seeking mode and tracking mode. By the way of mingling with
these two modes with a user-defined proportion, CSO can present better performance. Cat Swarm
Optimization (CSO) is superior to PSO and PSO with weighting factor. In this paper a method of
load rescheduling which is Available Transfer Capability (ATC) based incentive to the loads like
in smart grid is studied. CSO is the best optimization technique to load reschedule to enhance the

ATC in deregulated market compared to other algorithms. (T. Nireekshana and G. Kesava Rao,
2015). Cat Swarm Optimization (CSO) clustering gives better cluster’s accuracy level compared
to k means and PSO clustering. CSO clustering has better accuracy on clustering data with small
number of cluster.(Budi Santosa and Mirsa Kencana Ningrum, 2009). In this paper, cat swarm
optimization is used to deal with the clustering problem and to develop two clustering approaches,
CSOC and KCSOC. The result compared shows that KCSOC can get better clustering results
than KHM and CSOC. (Yongguo Liu and Yidong Shen, 2010). In this paper, a new variable
universe fuzzy controller based on the cat swarm optimization (CSO-VUFC) is proposed and the
temperature of a reactor is the controlled object, with the large time delay characteristics and high
precision adjustment, short transient time, and hard real-time are the result of the simulation.
(Haipeng Pan and Dongbin Jin, 2016).
Evolutionary image filter, the CSO based FLANN adaptive filter, for Gaussian noise removal
from CT image is presented. The result shows that proposed method is superior to other methods.
(M. Kumar, S. K. Mishra, and S. S. Sahu, 2016). CSO Algorithms is tested for a sample 3 bus
and an IEEE 14 bus test systems. This paper is presented to identify the optimal location and size
of shunt FACTS controllers in an interconnected power system under N-1 contingency for
voltage stability analysis. (G. Naveen Kumar, M. Surya Kalavathi and R. Harini Krishna, 2012).
CSO algorithm is used as the training algorithm to train the artificial neural networks with
Optimal Brain Damage as its pruning method. The result shows that CSO was able to produce
ANNs that perform well using different datasets. (John Paul T. Yusiong, 2013).
Original CSO algorithm and some improved branches of CSO family algorithms where
reviewed and utilizing CSO to solve problems in engineering examples were also reviewed. (Pei-
Wei Tsai and Vaci Istanda, 2013). This paper presents CSO algorithm for handling scheduling
courses problems in universities. The result shows that high efficiency of the algorithm in the
process of creating schedules appointments without exceeding the restrictions. (Ali Hussein
Khalaf, Hazem M.El-Bakry, Sahar fawzy Sabbeh, 2016). A scheduling technique using CSO is
presented. The result shows that it is more efficient than PSO by showing faster convergence
towards solutions. (Saurabh Bilgaiyan, Santwana Sagnika, Madhabananda Das, 2014).
8. BAT ALGORITHM
Nature inspired algorithms are the most powerful algorithms for optimization problems. Based
on the echolocation behavior of Bats a new nature-inspired metaheuristic algorithm, called BAT
algorithm has been proposed by Yang, 2010. Bats are only mammals having wings and high
echolocation capability. In practical they radiate a sound signal called sonar/echolocation to
detect the objects surrounding them and find their way in the night. This short pulse if sound wait
it hits into an object and after a fraction of time, the echo returns back to their ears. Thus, bats
can compute how far they are from an object. This mechanism makes bats being able to
distinguish the difference between an obstacle and prey, allowing them to hunt even in complete
darkness. In order to model this algorithm, Yang has idealized some rules, as follows
• All bats are echolocation to sense distance, and they also “know” the difference
between food/prey and background barriers in some magical way:
• A bat flies randomly with velocity with a fixed frequency, varying wavelength and
loudness to search for prey. They can automatically adjust the wavelength of their
emitted pulse and adjust the rate of pulse emission depending on the proximity of their
target:
• Although the loudness can vary in many ways, Yang assumes that the loudness varies
from a large positive to a minimum constant value.
The fast non-dominating sort and crowding distance assignment are adapted in BA algorithm.
This paper presented a bat algorithm with mutation for UCAV path planning in complicated

combat field environments. Compared with other optimization methods, the experiments show
that this method is a feasible and effective way in UCAV path planning. (GaigeWang, Lihong
Guo, Hong Duan, Luo Liu and HeqiWang, 2012). In this paper, Bat Algorithm for continuous
constrained optimization problems is presented and it is a very promising algorithm compared
with other algorithms. (Xin-She Yang, 2010). Bat algorithm with an improved DV-Hop
localization algorithm (BAD-Hop) is presented to solve the error which is brought about by the
average distance per hop in DV-Hop. (Xiaoying Yang, Wanli Zhang, 2016). Bat algorithm (BA)
overview and its applications are presented. (S. Induja, Dr. V.P. Eswaramurthy, 2016). BA has
been carefully implemented and carried out optimization for eight well-known optimization tasks
and comparison has been made with other existing algorithms. (Xin-She Yang, 2012). This paper
provides a review of the bat algorithm and its new variants. (Xin-She Yang, 2013). This paper s
presents review of bat algorithm and its recent developments (Iztok Fister Jr. and Iztok Fister,
Xin-She Yang, Simon Fong and Yan Zhuang, 2015). Bat algorithm is used to solve scheduling
problem in cloud computing. (Liji Jacob, 2014). An optimal parameter settings for bat algorithm
with orthogonal experimental design is presented. results show that our parameter settings
achieves best performance on real-parameter optimization compared with other algorithms. (Fei
Xue and Yongquan Cai, Yang Cao, Zhihua Cui and Feixiang Li, 2015). This paper reviews
PPDM techniques based on a PPDM framework. The results are compared for the advantages
and disadvantages of different PPDM techniques (Aliya Ahmad, Bhanu Pratap Singh Senger,
2016).
9. PARTICLE SWARM OPTIMIZATION
The Particle Swarm Optimization algorithm (abbreviated as PSO) is a novel population-based
stochastic search algorithm and an alternative solution to the complex non-linear optimization
problem. The PSO algorithm was first introduced by Dr. Kennedy and Dr. Eberhart in 1995 and
its basic idea was originally inspired by simulation of the social behavior of animals such as bird
flocking, fish schooling and so on. It is based on the natural process of group communication to
share individual knowledge when a group of birds or insects search food or migrate and so forth
in a searching space, although all birds or insects do not know where the best position is. But
from the nature of the social behavior, if any member can find out a desirable path to go, the rest
of the members will follow quickly.
The PSO algorithm basically learned from animal’s activity or behavior to solve optimization
problems. In PSO, each member of the population is called a particle and the population is called
a swarm. Starting with a randomly initialized population and moving in randomly chosen
directions, each particle goes through the searching space and remembers the best previous
positions of itself and its neighbors. Particles of a swarm communicate good positions to each
other as well as dynamically adjust their own position and velocity derived from the best position
of all particles. The next step begins when all particles have been moved. Finally, all particles
tend to fly towards better and better positions over the searching process until the swarm move
to close to an optimum of the fitness function
The PSO method is becoming very popular because of its simplicity of implementation as
well as ability to swiftly converge to a good solution. It does not require any gradient information
of the function to be optimized and uses only primitive mathematical operators. As compared
with other optimization methods, it is faster, cheaper and more efficient. In addition, there are
few parameters to adjust in PSO. That’s why PSO is an ideal optimization problem solver in
optimization problems. PSO is well suited to solve the non-linear, non-convex, continuous,
discrete, integer variable type problems. This paper presents to derive a heuristic for the
initialization of the inertia weight and acceleration coefficient values of the PSO to guarantee
convergent trajectories. The results shows that the PSO is sensitive to the inertia weight and
acceleration coefficient values, and that the derived heuristic ensures convergent trajectories. (F.

van den Bergh, A.P. Engelbrecht, 2006). This paper presents a bird’s eye view of PSO
applications. This has been obtained by identifying and analysing around 700 PSO application
papers stored in IEEE Xplore database at the time of writing. (Riccardo Poli, 2008). An efficient
approach using PSO in combination with the steepest gradient descent algorithm to determine the
optimal network structure and hyperparameter is presented.(Fei Ye, 2017). Three connection
rules for generating feed-forward ANN and guiding the connections between neurons is
presented. The ANN designed is compared with Back-Propagation and Levenberg-Marquardt
Learning Algorithms. (Beatriz A. Garro and Roberto A. Vázquez, 2015). Particle Swarm
Optimization with double learning patterns (PSO-DLP) is presented which apply the master
swarm and the slave swarm with different learning patterns to achieve a trade-off between the
convergence speed and the swarm diversity. (Yuanxia Shen, Linna Wei, Chuanhua Zeng, and
Jian Chen, 2016). In this paper PSO is used in chaotic maps and Gaussian mutation strategy. The
result shows that PSO has powerful ability to search the global optimum and also effectively
prevent the premature convergence in time. (Dongping Tian, 2015). This paper surveys the
existing PSO based localization algorithms of wireless sensor networks and chooses the best
parameters based on simulations. (Huanqing Cui, Minglei Shu, Min Song and Yinglong Wang,
2017). Three PSO-based approaches conventionally-used unconstrained, hard-constrained and
proposed virtual search are presented. The proposed virtual search approach boosts the swarm
search efficiency and improves the optimization convergence rate and robustness for PSO.
(Arezoo Modiri, Xuejun Gu, Aaron M. Hagan and Amit Sawant, 2017). This paper presents a
review on PSO in single and multi objective optimization. Result shows that simple PSO
performs well in 2 dimensions as compared to 10 dimensions. (Hemlata S. Urade and Prof. Rahila
Patel, 2011). PSO variant (PSO-IVL) which integrates the generic PSO, a novel inner variable
learning (IVL) strategy, and a novel trap detection and jumping out strategy is presented. PSO-
IVL is superior to all the selected state-of-the-art peer PSO variants is shown by the results.
(GuohuaWu, Witold Pedrycz, Manhao Ma, Dishan Qiu, Haifeng Li and Jin Liu, 2013).
10. GRAVITATIONAL SEARCH ALGORITHM
Gravitational search algorithm (GSA) is a population search algorithm proposed by Rashedi et
al. in 2009. The GSA is based on the low of gravity and mass interactions. The solutions in the
GSA population are called agents, these agents interact with each other through the gravity force.
The performance of each agent in the population is measured by its mass. Each agent is
considered as object and all objects move towards other objects with heavier mass due to the
gravity force. This step represents a global movements
(exploration step) of the object, while the agent with a heavy mass moves slowly, which
represents the exploitation step of the algorithm. The best solution is the solution with the heavier
mass.This paper presents the evolutions and applications of GSA and analyzed the works related
to GSA, to review GSA advances and its performances, to review the applications. (Norlina Mohd
Sabri, Mazidah Puteh, and Mohamad Rusop Mahmood, 2013). This paper presents a new
optimization algorithm based on the law of gravity, namely Gravitational Search Algorithm
(GSA). The results obtained by GSA superior results compared to other algorithms. (Esmat
Rashedi, Hossein Nezamabadi-pour , Saeid Saryazdi, 2009). In this paper, an automatic clustering
and feature selection technique using gravitational search algorithm has been presented.
GSA_CFS outperforms the other competitive clustering techniques has been proved. (Vijay
Kumar, Dinesh Kumar, 2018). This paper presents bi-objective optimization problem is
converted into a single objective function using a price penalty factor in order to solve it with
GSA. The results demonstrate the effectiveness and robustness of the algorithm in solving the
CEED problem under various test systemsand it can provide better solutions than other
algorithms (U. Güvenç, Y. Sönmez, S. Duman, N. Yörükeren, 2012). The goal of this paper was
to increase the exploration and exploitation abilities of GSA a novel operator called Disruption,

originating from astrophysics. The improved GSA (IGSA) is successful, faster and more precise
than GSA itself. (S. Sarafrazi, H. Nezamabadi-pour, S. Saryazdi, 2011). In this paper, the GSA
method has been proposed to solve economic dispatch problem with valve-point effect for 3 and
13 units test systems. The results shows that performance of the algorithm is efficient and robust
compared to other algorithms (S. Duman, U. Güvenç, N. Yörükeren, 2010). This paper presents
a novel approach for oil consumption modeling. The demand estimation models are developed
to forecast oil consumption based on socio-economic indicators using GSA. Comparison was
made with the GA and PSO it show that GSA is a satisfactory tools for successful oil demand
forecasting. (M.A. Behrang , E. Assareh , M. Ghalambaz , M.R. Assari , A.R. Noghrehabadi,
2011). This paper presents a comparative study on economic dispatch problem (EDP) and GSA
technique. The GSA method gives better results with reduced computational time, robustness,
fast convergence and proficiency compared to other algorithms. (R.K.Swain, N.C.Sahu,
P.K.Hota, 2012). In this paper, GSA method is used to solve the optimal power flow (OPF)
problem which is formulated as a nonlinear optimization problem with equality and inequality
constraints in a power system. The results obtained from the GSA technique is compared with
other methods shows that GSA provides effective and robust high-quality solution for the OPF
problem. (Serhat Duman , Ug˘ur Guvenc , Yusuf Sonmez , Nuran Yorukeren, 2012). In this paper,
GSA algorithm has been employed which is more effective and capable of solving nonlinear
optimization problems faster and with better accuracy in detecting the global best solution. The
results shows that GSA is robust and reliable optimization algorithm for solving small and large-
scale unit commitment (UC) problems. (Provas Kumar Roy, 2013)
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