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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- 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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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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DOI: https://doi.org/10.1007/s10489-020-01974-z