## Abbreviations  
ABC  
ABC-FF  
ABC with firefly algorithm  
ACO  
Ant Colony Optimization  
AGA  
Adaptive genetic algorithm  
ALC-PSO  
PSO with an aging leader and challengers  
ALO  
Ant lion optimizer  
BA  
Bat algorithm  
CKHA  
Chaotic krill herd algorithm  
CLPSO  
Comprehensive learning particle swarm optimization  
COA  
Coyote optimization algorithm  
CSA  
Cuckoo search algorithm  
CSOA  
Crow search optimization algorithm  
DE  
Differential evolution  
DE-AS  
Combination of differential evolution and ant system  
method  
DSA  
Differential search algorithm  
EMOA  
Exchange market optimization algorithm  
GA  
Genetic algorithm  
GBBWCA  
Gaussian barebones water cycle algorithm  
GBTLBO  
Gaussian barebones-based TLBO algorithm  
GSA  
Gravitational search algorithm  
GWO  
Gray wolf optimizer  
HFA-NMS  
Hybrid Nelder-Mead simplex-based firefly algorithm  
HLGA  
Hybrid loop genetic algorithm  
HMAPSO  
Hybrid multiagent particle swarm optimization  
HPSO  
Hybrid PSO  
HYBOLCA  
Hybrid PSO and imperialist competitive algorithms  
HPSO-TS  
Hybridization of PSO and Tabu search method  
HAS  
Harmony search algorithm  
Incorporation of chaotic artificial bee colony algorithm and  
differential evolution  
ICA  
Imperialist competitive algorithms  
ICBO  
Improved Colliding Bodies optimization  
ICOA  
Improved coyote optimization algorithm  
ICSA  
Improved CSA  
JA  
Jaya algorithm  
KHA  
Krill herd algorithm  
MFO  
Moth-flame optimization technique  
MGBTLBO  
modified GBTLBO  
MOGWA  
Multiobjective grey wolf algorithm  
MPSO  
Modified Particle swarm optimization  
MTLA-DDE  
Hybrid modified teaching learning technique and double  
differential evolution algorithm  
ORPD  
Optimal reactive power dispatch  
PPs  
Power plants  
PSO  
Particle swarm optimization  
HPSO  
Hybrid PSO  
PSO-AAC  
PSO with adaptive acceleration coefficients  
PSO-AIW  
PSO with adaptive inertia weight  
PSO-CF  
PSO with constriction factor  
PSO-GT  
Combination of PSO and graph theory
PSO-IPG PSO with pseudogradient theory and constriction factor PSO-PG PSO with pseudogradient theory PSO-PGSWT with pseudogradient theory PSO-SWT PSO with the trade-off of stochastic weight PST Probabilistic state transition QODE Quasi-oppositional differential evolution QOTLBO Quasi-oppositional teaching learning-based optimization RCGA Real coded genetic algorithm SARCGA Self-adaptive real coded genetic algorithm SFOA Sunflower optimization algorithm SGA Specialized genetic algorithm SPSO-AAC Self-adaptive PSO with adaptive acceleration coefficients SR Success rate SSA Salp swarm algorithm Std. dev. Standard deviation TLBO Teaching learning-based optimization TPL Total active power losses TS Tabu search method TVD Total voltage deviation VSMF Variable scaling mutation factor WCA Water cycle algorithm WOA While optimization algorithm  
**Nomenclature**  
\(Fitness_{d,p}^{new}\): \(Fitness_{d,p}\) Fitness function of new solution and old solution \(d\) in the _pth_ pack Current iteration Real element and imaginary element of admittance of branch _ij_ Number of all buses in considered power system Number of coyotes in each pack Number of shunt capacitor banks NG Number of generators The largest iteration NL Number of all loads Number of packs Number of coyotes in all packs Number of runs Number of new solutions produced per iteration Number of new solutions produced per run Number of quality solution evaluations Number of transformers Active power and reactive power generated by generator at bus \(i\) Number of reactive power generated by load at bus \(i\) Number of reactive power generated by shunt capacitor banks at shunt capacitor banks at bus \(i\) Number of lowest and highest reactive power  
\begin{tabular}{l l l} \hline \hline \multicolumn{1}{c}{**Case**} & \multicolumn{1}{c}{**Continuous Variables**} & \multicolumn{1}{c}{**Discrete Variables**} \\ \hline \multirow{2}{*}{ \begin{tabular}{c} **Input/Control** \\ **Variables** \\ \end{tabular}  } & \begin{tabular}{c} **Minimize** \\ **Power** \\ **Loss** \\ \end{tabular}  &