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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

References

  1. Rao SS (2019) Engineering optimization: theory and practice. John Wiley & Sons

  2. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  3. Holland JH (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press

  4. Shi Y, Eberhart R (1998) A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (cat. No. 98TH8360) (pp 69-73). IEEE

  5. Storn R, Price K (1997) Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  Google Scholar 

  6. Simon D (2008) Biogeography-based optimization. IEEE Trans Evol Comput 12(6):702–713

    Article  Google Scholar 

  7. Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933

    Article  Google Scholar 

  8. Rashedi E, Nezamabadi-Pour H, Saryazdi S (2009) GSA: a gravitational search algorithm. Inf Sci 179(13):2232–2248

    Article  Google Scholar 

  9. Eskandar H, Sadollah A, Bahreininejad A, Hamdi M (2012) Water cycle algorithm–a novel metaheuristic optimization method for solving constrained engineering optimization problems. Comput Struct 110:151–166

    Article  Google Scholar 

  10. Civicioglu P (2013) Backtracking search optimization algorithm for numerical optimization problems. Appl Math Comput 219(15):8121–8144

    MathSciNet  MATH  Google Scholar 

  11. Cheng MY, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112

    Article  Google Scholar 

  12. Umam MIH, Santosa B (2018) A hybrid symbiotic organisms search algorithm with variable neighbourhood search for solving symmetric and asymmetric traveling salesman problem. In: IOP conference series: materials science and engineering, vol 337 no 1. IOP publishing, p 012005

  13. Yalçın E, Çam E, Taplamacıoğlu MC (2019) A new chaos and global competitive ranking-based symbiotic organisms search algorithm for solving reactive power dispatch problem with discrete and continuous control variable. Electric Eng:1-18

  14. Çelik E (2020) A powerful variant of symbiotic organisms search algorithm for global optimization. Eng Appl Artif Intell 87:103294

    Article  Google Scholar 

  15. Sönmez Y, Unal M (2020) Estimation of smooth and non-smooth fuel cost function parameters using improved symbiotic organisms search algorithm. J Electric Eng Technol 15(1):13–25

    Article  Google Scholar 

  16. Chu SC, Du ZG, Pan JS (2020) Symbiotic organism search algorithm with multi-group quantum-behavior communication scheme applied in wireless sensor networks. Appl Sci 10(3):930

    Article  Google Scholar 

  17. Chakraborty F, Nandi D, Roy PK (2019) Oppositional symbiotic organisms search optimization for multilevel thresholding of color image. Appl Soft Comput 82:105577

    Article  Google Scholar 

  18. Ezugwu AE (2019) Enhanced symbiotic organisms search algorithm for unrelated parallel machines manufacturing scheduling with setup times. Knowl-Based Syst 172:15–32

    Article  Google Scholar 

  19. Miao F, Zhou Y, Luo Q (2019) Complex-valued encoding symbiotic organisms search algorithm for global optimization. Knowl Inf Syst 58(1):209–248

    Article  Google Scholar 

  20. Kumar S, Tejani GG, Mirjalili S (2019) Modified symbiotic organisms search for structural optimization. Eng Comput 35(4):1269–1296

    Article  Google Scholar 

  21. Liu D, Li H, Wang H, Qi C, Rose T (2020) Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling. Expert Syst Appl 148:113230

    Article  Google Scholar 

  22. Truong KH, Nallagownden P, Baharudin Z, Vo DN (2019) A quasi-oppositional-chaotic symbiotic organisms search algorithm for global optimization problems. Appl Soft Comput 77:567–583

    Article  Google Scholar 

  23. Nama S, Saha A, Ghosh S (2016) Improved symbiotic organisms search algorithm for solving unconstrained function optimization. Decision Sci Lett 5(3):361–380

    Article  Google Scholar 

  24. Nama S, Saha AK, Ghosh S (2017) A hybrid symbiosis organisms search algorithm and its application to real world problems. Memetic Comput 9(3):261–280

    Article  Google Scholar 

  25. Saha A, Nama S, Ghosh S (2019) Application of HSOS algorithm on pseudo-dynamic bearing capacity of shallow strip footing along with numerical analysis. Int J Geotech Eng:1–14

  26. Nama S, Saha AK, Sharma S (2020) A novel improved symbiotic organisms search algorithm. Comput Intell. https://doi.org/10.1111/coin.12290

  27. Tejani GG, Savsani VJ, Patel VK (2016) Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization. J Comput Design Eng 3(3):226–249

    Article  Google Scholar 

  28. Prayogo D, Cheng MY, Wong FT, Tjandra D, Tran DH (2018) Optimization model for construction project resource leveling using a novel modified symbiotic organisms search. Asian J Civil Eng 19(5):625–638

    Article  Google Scholar 

  29. Satapathy S, Naik A (2013) Improved teaching learning based optimization for global function optimization. Decision Sci Lett 2(1):23–34

    Article  Google Scholar 

  30. Nama S, Saha A (2019) A novel hybrid backtracking search optimization algorithm for continuous function optimization. Decision Sci Lett 8(2):163–174

    Article  Google Scholar 

  31. Duan H, Luo Q (2014) Adaptive backtracking search algorithm for induction magnetometer optimization. IEEE Trans Magn 50(12):1–6

    Article  Google Scholar 

  32. Liang JJ, Qin AK, Suganthan PN, Baskar S (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295

    Article  Google Scholar 

  33. Van den Bergh F, Engelbrecht AP (2004) A cooperative approach to particle swarm optimization. IEEE Trans Evol Comput 8(3):225–239

    Article  Google Scholar 

  34. Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In proceedings of the 2003 IEEE swarm intelligence symposium. SIS'03 (cat. No. 03EX706). IEEE, pp 174-181

  35. Mendes R, Kennedy J, Neves J (2004) The fully informed particle swarm: simpler, maybe better. IEEE Trans Evol Comput 8(3):204–210

    Article  Google Scholar 

  36. Parsopoulos KE (2004) UPSO: a unified particle swarm optimization scheme. Lecture Series Comput Comput Sci 1:868–873

    MathSciNet  Google Scholar 

  37. Mallipeddi R, Suganthan PN, Pan QK, Tasgetiren MF (2011) Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl Soft Comput 11(2):1679–1696

    Article  Google Scholar 

  38. Liu ZZ, Wang Y, Yang S, Cai Z (2016) Differential evolution with a two-stage optimization mechanism for numerical optimization. In 2016 IEEE congress on evolutionary computation (CEC). IEEE, pp 3170-3177

  39. Wang Y, Liu ZZ, Li J, Li HX, Yen GG (2016) Utilizing cumulative population distribution information in differential evolution. Appl Soft Comput 48:329–346

    Article  Google Scholar 

  40. Liu ZZ, Wang Y, Yang S, Tang K (2018) An adaptive framework to tune the coordinate systems in nature-inspired optimization algorithms. IEEE Trans Cybernet 49(4):1403–1416

    Article  Google Scholar 

  41. Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolution Comput 1(1):3–18

    Article  Google Scholar 

  42. Demšar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(Jan):1–30

    MathSciNet  MATH  Google Scholar 

  43. Das S, Suganthan PN (2010) Problem definitions and evaluation criteria for CEC 2011 competition on testing evolutionary algorithms on real world optimization problems. Jadavpur University, Nanyang Technological University, Kolkata, pp 341–359

    Google Scholar 

  44. Niu B, Liu Y, Zhou W, Li H, Duan P, Li J (2019a) Multiple Lyapunov functions for adaptive neural tracking control of switched nonlinear nonlower-triangular systems. IEEE Transactions on Cybernetics

  45. Niu B, Wang D, Liu M, Song X, Wang H, Duan P (2019b) Adaptive neural output-feedback controller Design of Switched Nonlower Triangular Nonlinear Systems with Time Delays. IEEE Trans Neural Netw Learn Syst

  46. Niu B, Wang D, Alotaibi ND, Alsaadi FE (2018) Adaptive neural state-feedback tracking control of stochastic nonlinear switched systems: an average dwell-time method. IEEE Trans Neural Netw Learn Syst 30(4):1076–1087

    Article  MathSciNet  Google Scholar 

  47. Kahraman HT, Aras S, Gedikli E (2020) Fitness-distance balance (FDB): a new selection method for meta-heuristic search algorithms. Knowl-Based Syst 190:105169

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mathematics, Ram Thakur College, Agartala, Tripura, 799003, India

    Sukanta Nama

  2. Department of Applied Mathematics, MBB University, Agartala, Tripura, 799003, India

    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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