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A modification of I-SOS: performance analysis to large scale functions

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Abstract

SOS is a global optimization algorithm, based on nature, and is utilized to execute the various complex hard optimization problems. Be that as it may, some basic highlights of SOS, for example, pitfall among neighborhood optima and weaker convergence zone should be upgraded to discover better answers for progressively intricate, nonlinear, many optimum solution type problems. To diminish these deficiencies, as of late, numerous analysts increase the exhibition of the SOS by designing up a few changed form of the SOS. This paper suggests an improved form of the SOS to build up an increasingly steady balance between discovery and activity cores. This technique uses three unique procedures called adjusted benefit factor, altered parasitism stage, and random weighted number-based search. The technique is referred to as mISOS and tested in a popular series of twenty classic benchmarks. The dimension of these problems is considered to be hundred to monitor the impact of the suggested technique on the versatility of the test problems. Also, some real-life optimization problems are solved with the help of the proposed mISOS. The results investigated based on three different way and theses are statistical measures, convergence, and statistical analyses. The comparison of results of the mISOS with the standard SOS, SOS variants, and certain other cutting-edge algorithms shows its improved search performance.

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Abbreviations

SOS:

Symbiotic Organisms Search

mISOS:

Modification Based Improved Symbiotic Organisms Search

GA:

Genetic Algorithm

PSO:

Particle Swarm Optimization

DE:

Differential Evolution

BBO:

Biogeography-Based Optimization

HS:

Harmony Search

GSA:

Gravitational Search Algorithm

WCA:

Water Cycle Algorithm

BSA:

Backtracking Search Optimization Algorithm

EA:

Evolutionary Algorithm

SOS-VNS:

Hybrid Symbiotic Organisms Search Algorithm With Variable Neighbourhood Search

ATSP:

Asymmetric Traveling Salesman Problem

VNS:

Variable Neighbourhood Search

A-CSOS:

Competitive Ranking-Based Symbiotic Organisms Search Algorithm

ORPD:

Optimal Reactive Power Dispatch

ISOS:

Improved Symbiotic Organisms Search Algorithm

SASOS:

Simulated Annealing Based Symbiotic Organism Search

DOCR:

Directional Overcurrent Relay (DOCR) Problems

MQSOS:

Symbiotic Organism Search Algorithm With Multi-Group Quantum-Behavior Communication Scheme

OSOS:

Oppositional Symbiotic Organisms Search Optimization

ESOS:

Enhanced Symbiotic Organisms Search Algorithm

CSOS:

Complex-Valued Encoding Symbiotic Organisms Search Algorithm

MSOS:

Modified Symbiotic Organisms Search

QOBL:

Quasi-Opposition-Based Learning

CLS:

Chaotic Local Search

QOCSOS:

Quasi-Oppositional-Chaotic Symbiotic Organisms Search Algorithm

SQI:

Simple Quadratic Interpolation

HSOS:

Hybrid Symbiosis Organisms Search

BF1 and BF2:

Benefit Factors

SOS-ABF1,2,1&2:

Adaptive Symbiotic Organisms Search

I-SOS:

Improved Symbiotic Organisms Search Algorithm

ABSA:

Adaptive Backtracking Search Algorithm

CLPSO:

Comprehensive Learning Particle Swarm Optimizer

CPSO-H:

Cooperative Approach To Particle Swarm Optimization

FDR-PSO:

Fitness-Distance-Ratio Based Particle Swarm Optimization

FI-PS:

Fully Informed Particle Swarm

UPSO:

A Unified Particle Swarm Optimization Scheme

EPSDE:

Differential Evolution Algorithm with Ensemble Of Parameters And Mutation Strategies

TSDE:

Differential Evolution with A Two-Stage Optimization Mechanism

CPI-DE:

Cumulative Population Distribution Information In Differential Evolution

ACoS-PSO:

An Adaptive Framework To Tune The Coordinate Systems In Particle Swarm Optimization Algorithms

HBSA:

Hybrid Backtracking Search Optimization Algorithm

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Correspondence to Sukanta Nama.

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Nama, S. A modification of I-SOS: performance analysis to large scale functions. Appl Intell 51, 7881–7902 (2021). https://doi.org/10.1007/s10489-020-01974-z

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