PSO PSO is easy to realize, fast

PSO technique introduced originally by Kennedy and Eberhart in 1995 75. it involves simulating social behavior among individuals
(particles) “flying” through a multidimensional
search space, in which each particle represents a single intersection of all of
the search dimensions. The particles evaluate their positions relative to a
goal (fitness) at every iteration, and the particles in a local neighborhood
share memories of their “best” positions and use those memories to adjust their
own velocities and subsequent positions. PSO has the advantages of parallel
computation and robustness, and it can find the global optimal solution with a
higher probability and efficiency than traditional methods. PSO is easy to
realize, fast converging, and intelligent.

W. Yang and Q.  Li  76 proposed an approach based on a PSO algorithm to solve the capacitor
placement problem in radial distribution systems. Under consideration of the
potential harmonic effects, different load levels, and practical aspects of
fixed or switched capacitor banks, the target problem was reformulated by a
comprehensive objective function and a set of equality and inequality
constraints. The proposed solution method employed PSO to search for the
optimal location, type, and size of capacitors to be placed and the optimal
numbers of switched capacitor banks at different load levels. X. Yu et al. 77 proposed a PSO-based parallel search technique to estimate the
required level of shunt capacitive compensation to improve the voltage profile
of the system and reduce active power loss. K. Prakash, M.  Sydulu 78 present a novel approach that determines the optimal location and size
of capacitors on radial distribution systems to improve voltage profile and
reduce the active power loss. Capacitor placement and sizing are done by loss
sensitivity factors and particle swarm optimization respectively.

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M. Al-Hajri et al. 79 proposed a novel approach to optimally solve the problem of
determining the location and size of shunt capacitors in distribution systems.
the proposed method solves the problems of finding the optimal capacitor size
and location simultaneously. Throughout the optimization process, both the
capacitor injected reactive power and its location are being treated as
discrete variables. The objective function considered is to minimize the
total feeder losses. M. Khalil et al.
7 presented a PSO for optimal capacitor placement considering voltage
stability enhancement.

A. Eajal and M. El-Hawary 80 present a discrete version of PSO combined with a radial distribution
power flow algorithm to form a hybrid PSO algorithm. The former is employed as
a global optimizer to find the globally optimal solution, while the latter is
used to calculate the objective function and to verify bus voltage limits. P. Sonwane and B. Kushare  81
used a particle swarm optimization technique to evaluate objective function for capacitor placement and
sizing in IEEE bus system. Result analysis shows that optimal capacitor
configuration finds proper places and size of the capacitor. This placement
improves power factor, reduces active and reactive losses, maintain voltage
profile and KVA release. T.
Selim 82
applied Selective Particle Swarm Optimization in a real large distribution
system to find the optimal capacitor placement, optimal conductor selection,
and network reconfiguration, the main objectives are to increase the network
efficiency by increasing the annual benefits, reducing power and energy losses
and improving the voltage profile.

H. Lotfi, M. Samadi and A. Dadpour 3 proposed
a novel method, Improved Particle Swarm Optimization, to placement capacitor in
radial distribution systems. This method used a combination of shuffled
Frog-leaping algorithm and particle swarm algorithm. the objective function is a
combination of the power losses cost and the cost of installing capacitors, to
determine the location and optimal size of capacitors and constraint including
minimum and maximum voltage limitations.