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A novel quasi-oppositional chaotic student psychology-based optimization algorithm for deciphering global complex optimization problems

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Abstract

This research work projects a novel quasi-oppositional chaotic student psychology-based optimization (SPBO) (QOCSPBO) algorithm for solving global optimization problems. To tackle the identified flaws of the standard SPBO, the proffered QOCSPBO algorithm combines two search strategies within the standard SPBO framework. The obtained outcomes exhibit that the proposed QOCSPBO algorithm outperforms SPBO and recently published algorithms in optimizing a set of well-known benchmark test functions. The projected QOCSPBO attains the optimal site and size of distributed generation and shunt capacitors in two radial distribution systems contemplating different types load models at three load levels. The obtained results prove that the recommended method can be highly suitable in solving real-time power system optimization problems with constrained and unknown search space.

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Data availability

The data that support the findings of this study are openly available in https://doi.org/10.1016/j.advengsoft.2016.01.008 (for benchmark test functions) [29], https://github.com/P-N-Suganthan (for CEC-C06 2019 benchmark test functions) [126], https://ieeexplore.ieee.org/document/19265, https://www.sba.org.br/revista/vol11/v11a261.htm (for IEEE 33-bus RDS) [132] and control & automation, (for 136-bus RDS) [133].

Abbreviations

ACO:

Ant colony optimization

AGTO:

Artificial gorilla troops optimizer

ALO:

Ant lion optimizer

AO:

Aquila optimizer

AOA:

Arithmetic optimization algorithm

AVOA:

African vulture optimization algorithm

BFOA:

Bacterial forging optimization algorithm

BPSO:

Binary PSO

BSA:

Backtracking search algorithm

CSA:

Crow search algorithm

CS:

Cuckoo search

Cf-PSO:

Constriction factor PSO

CLS:

Chaotic local search

DA:

Dragonfly algorithm

DG:

Distributed generation

DE:

Differential evolution

DMOA:

Dwarf mongoose optimization algorithm

EOSA:

Ebola optimization search algorithm

FFA:

Farmland fertility algorithm

FPA:

Flower pollination algorithm

FGA:

Fuzzy GA

GA:

Genetic algorithm

GABC:

Gbest-guided artificial bee colony

GWO:

Grey wolf optimizer

GSA:

Gravitational search algorithm

HSA:

Harmony search algorithm

IMDE:

Intersect mutation DE

MSA:

Moth search algorithm

MPSO:

Modified PSO

MFO:

Moth flame optimization

MOA:

Metaheuristic optimization algorithm

MVO:

Multi-verse optimizer

PDOA:

Prairie dog optimization algorithm

PL:

Peak load

PSO:

Particle swarm optimization

QOBL:

Quasi-oppositional (QO)-based learning

RDS:

Radial distribution system

RSA:

Reptile search algorithm

SC:

Shunt capacitor

SD:

Standard deviation

SCA:

Sine cosine algorithm

SPBO:

Student psychology-based optimization

SSA:

Salp swarm algorithm

SOS:

Symbiotic organisms search

SHADE:

Success history-based adaptive DE

TF:

Test function

TLBO:

Teaching–learning-based optimization

TOC:

Total operating cost

TVD:

Total voltage deviation

VSI:

Voltage stability index

WCA:

Water cycle algorithm

WOA:

Whale optimization algorithm

\(b_{{{\text{best}}\left( {{\text{new}}} \right)}}\) :

BS in the next iteration

\(b_{{{\text{LL}}}}\) :

Lower limit (LL) of marks

\(b_{{{\text{UL}}}}\) :

Upper limit (UL) marks

\({\text{Ch}}\) :

Chaotic sequence

\(\mu\) :

Control parameter of \({\text{Ch}}\)

\(D\) :

Dimension

\({\text{iter}}_{\max }\) :

Maximum number of iterations

\(j_{{\text{r}}}\) :

Jumping rate

\(K_{i}\) :

Annual cost per unit of \(P_{{{\text{loss}}}}\)

\(K_{p}\) :

Yearly installation cost of DGs

\(K_{C}\) :

Yearly installation cost of SCs

\(K\) :

Initial population

\(K_{{{\text{CLS}}}}\) :

Chaotic local search limit

\(K_{{{\text{opposite}}}}\) :

Opposite of \(K\)

\(K_{{\text{quasi - opposite}}}\) :

Quasi-opposite of \(K\)

\(N_{{{\text{dg}}}}\) :

Number of DGs

\(N_{{{\text{SC}}}}\) :

Number of SCs

\(N_{{{\text{pop}}}}\) :

Number of population

\({\text{OF}}\) :

Objective function

\(P\) :

Real power (kW)

\(P_{{{\text{loss}}}}\) :

Active power loss (kW)

\(P_{{{\text{DG}}}}\) :

Active power DG

\(P_{{{\text{DG}}}}^{{{\text{LL}}}}\) :

LL of \(P_{{{\text{DG}}}}\)

\(P_{{{\text{DG}}}}^{{{\text{UL}}}}\) :

UL of \(P_{{{\text{DG}}}}\)

\(Q\) :

Reactive power

\(Q_{{{\text{SC}}}}\) :

Reactive power SC

\(Q_{{{\text{SC}}}}^{{{\text{LL}}}}\) :

LL of SC

\(Q_{{{\text{SC}}}}^{{{\text{UL}}}}\) :

UL of SC

\(Q_{{{\text{loss}}}}\) :

Reactive power loss

\(R_{ij}\) :

Resistance between the ith and jth nodes

\(V^{{{\text{LL}}}}\) :

LL of voltage

\(V^{{{\text{UL}}}}\) :

UL of voltage

\(w_{1} - w_{5}\) :

Weight factors

\(X_{ij}\) :

Reactance between the ith and jth nodes

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Balu, K., Mukherjee, V. A novel quasi-oppositional chaotic student psychology-based optimization algorithm for deciphering global complex optimization problems. Knowl Inf Syst 65, 5387–5477 (2023). https://doi.org/10.1007/s10115-023-01931-5

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