Non-Dominated Sorting Whale Optimization Algorithm (NSWOA): A Multi-Objective Optimization algorithm for Solving Engineering Design Problems

1
Pradeep Jangir
Pradeep Jangir
2
Dr. Pradeep Jangir
Dr. Pradeep Jangir
3
Narottam Jangir
Narottam Jangir

Send Message

To: Author

GJRE Volume 17 Issue F4

Article Fingerprint

ReserarchID

4NK89

Non-Dominated Sorting Whale Optimization Algorithm (NSWOA): A Multi-Objective Optimization algorithm for Solving Engineering Design Problems Banner
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

This novel article presents the multi-objective version of the recently proposed the Whale Optimization Algorithm (WOA) known as Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). This proposed NSWOA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutionsin achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and bubblenet hunting strategy to guide humpback whales towards the dominated regions of multi-objective search spaces.For validate the efficiency and effectiveness of proposed NSWOA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSWOA algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS).The results of proposed NSWOAalgorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage andfaster convergence.

48 Cites in Articles

References

  1. C Kelley (1999). Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition.
  2. T Vogl,J Mangis,A Rigler,W Zink,D Alkon (1988). Accelerating the convergence of the back-propagation method.
  3. Seyedali Mirjalili (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.
  4. Xin-She Yang (2010). The bat algorithm (BA),"A Bioinspired algorithm.
  5. J Kennedy,R Eberhart (1995). Particle swarm optimization.
  6. Marco Dorigo,Mauro Birattari,Thomas Stutzle (2006). Ant Colony Optimization.
  7. H John (1992). Holland, adaptation in natural and artificial systems.
  8. Amir Gandomi,Xin-She Yang,Amir Alavi (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems.
  9. A Sadollah,A Bahreininejad,H Eskandar,M Hamdi (2013). Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems.
  10. Amir Gandomi,Amir Alavi (2012). Krill herd: A new bio-inspired optimization algorithm.
  11. Amir Gandomi (2014). Interior search algorithm (ISA): A novel approach for global optimization.
  12. Hans-Georg Beyer,Bernhard Sendhoff (2007). Robust optimization – A comprehensive survey.
  13. J Knowles,R Watson,D Corne (2001). Reducing local optima in single-objective problems by multi-objectivization.
  14. Kalyanmoy Deb,David Goldberg (1993). Analyzing Deception in Trap Functions.
  15. C Coello (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art.
  16. D Wolpert,W Macready (1997). No free lunch theorems for optimization.
  17. Seyedali Mirjalili,Andrew Lewis (2016). The Whale Optimization Algorithm.
  18. A Panda,S Pani (2015). Multiobjective colliding bodies optimization.
  19. C Coello Coello,G Pulido,M Lechuga (2004). Handling multiple objectives withparticle swarm optimization.
  20. K Deb,A Pratap,S Agarwal,T Meyarivan (2002). A fast and elitistmultiobjective genetic algorithm: NSGA-II.
  21. Arnapurna Panda,Sabyasachi Pani (2016). A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems.
  22. P Ngatchou,A Zarei,A El-Sharkawi (2005). Pareto Multi Objective Optimization.
  23. Pareto (1964). Cours d'economie politique.
  24. F Edgeworth (1881). To Mrs. Edgeworth.
  25. S Mirjalili,S Saremi,S Mirjalili,L Coelho (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization.
  26. X.-S Yang (2011). Bat algorithm for multi-objective optimisation.
  27. Reza Akbari,Ramin Hedayatzadeh,Koorush Ziarati,Bahareh Hassanizadeh (2012). A multi-objective artificial bee colony algorithm.
  28. J Knowles,D Corne (2000). Approximating the nondominated front using the Pareto archived evolution strategy.
  29. H Abbass,R Sarker,C Newton (2001). PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems.
  30. Qingfu Zhang,Hui Li (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition.
  31. E Zitzler (1999). Unknown Title.
  32. E Zitzler,L Thiele (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach.
  33. Ali Sadollah,Hadi Eskandar,Ardeshir Bahreininejad,Joong Kim (2014). Water cycle algorithm for solving multi-objective optimization problems.
  34. Ali Sadollah,Hadi Eskandar,Joong Kim (2015). Water cycle algorithm for solving constrained multi-objective optimization problems.
  35. D Van Veldhuizen,G Lamont (1998). Multiobjective evolutionary algorithm research: A history and analysis.
  36. J Schott (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
  37. C Coello (2000). Use of a self-adaptive penalty approach for engineering optimization problems.
  38. C Coello,G Pulido (2005). Multiobjective structural optimization using a microgenetic algorithm.
  39. A Kurpati,S Azarm,J Wu (2002). Constraint handling improvements for multiobjective genetic algorithms.
  40. T Ray,K Liew (2002). A swarm metaphor for multiobjective design optimization.
  41. B Qu,J Liang,Y Zhu,Z Wang,P Suganthan (2016). Economic emission dispatch problems with stochastic wind power using summation based multi-objective evolutionary algorithm.
  42. P Hota,A Barisal,R Chakrabarti (2010). Economic emission load dispatch through fuzzy based bacterial foraging algorithm.
  43. M Abido (2003). Environmental / economic power dispatch using multiobjective evolutionary algorithms: a comparative study.
  44. T Binh,U Korn (1997). Multiobjective Optimization Problems.
  45. A Osyczka,S Kundu (1995). A new method to solve generalized multicriteria optimization problems using the simple genetic algorithm.
  46. N Srinivasan,K Deb (1994). Multi-objective function optimisation using non-dominated sorting genetic algorithm.
  47. Y Zhu,J Wang (2014). Multi-objective economic emission dispatch considering wind power using evolutionary algorithm based on decomposition.
  48. R Bhesdadiya,I Trivedi,P Jangir,N Jangir,A Kumar (2016). An NSGA-III algorithm for solving multi-objective economic/environmental.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

Pradeep Jangir. 2017. \u201cNon-Dominated Sorting Whale Optimization Algorithm (NSWOA): A Multi-Objective Optimization algorithm for Solving Engineering Design Problems\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 17 (GJRE Volume 17 Issue F4): .

Download Citation

Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords
Classification
GJRE-F Classification: FOR Code: 290901
Version of record

v1.2

Issue date

September 11, 2017

Language

English

Experiance in AR

The methods for personal identification and authentication are no exception.

Read in 3D

The methods for personal identification and authentication are no exception.

Article Matrices
Total Views: 3382
Total Downloads: 1744
2026 Trends
Research Identity (RIN)
Related Research

Published Article

This novel article presents the multi-objective version of the recently proposed the Whale Optimization Algorithm (WOA) known as Non-Dominated Sorting Whale Optimization Algorithm (NSWOA). This proposed NSWOA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutionsin achieve till the evolution of last iteration limit. The best solutions are then chosen from the collection of all Pareto optimal solutions using a crowding distance mechanism based on the coverage of solutions and bubblenet hunting strategy to guide humpback whales towards the dominated regions of multi-objective search spaces.For validate the efficiency and effectiveness of proposed NSWOA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSWOA algorithm against Multi objective Colliding Bodies Optimizer (MOCBO), Multi objective Particle Swarm Optimizer (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) and Multi objective Symbiotic Organism Search (MOSOS).The results of proposed NSWOAalgorithm validates its efficiency in terms of Execution Time (ET) and effectiveness in terms of Generalized Distance (GD), Diversity Metric (DM) on standard unconstraint, constraint and engineering design problem in terms of high coverage andfaster convergence.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]
×

This Page is Under Development

We are currently updating this article page for a better experience.

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Non-Dominated Sorting Whale Optimization Algorithm (NSWOA): A Multi-Objective Optimization algorithm for Solving Engineering Design Problems

Dr. Pradeep Jangir
Dr. Pradeep Jangir
Narottam Jangir
Narottam Jangir

Research Journals