MONSDA: – A Novel Multi-objective Non-Dominated Sorting Dragonfly Algorithm

MONSDA: – A Novel Multi-objective Non-Dominated Sorting Dragonfly Algorithm

Pradeep Jangir

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MONSDA: – A Novel Multi-objective Non-Dominated Sorting Dragonfly Algorithm

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Abstract

This novel article presents the multi-objective version of the recently proposed Dragonfly Algorithm (DA) known as Non-Dominated Sorting Dragonfly Algorithm (NSDA). This proposed NSDA algorithm works in such a manner that it first collects all non-dominated Pareto optimal solutions in 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 swarming strategy to guide dragonflies towards the dominated regions of multi-objective search spaces. For validate the efficiency and effectiveness of proposed NSDA algorithm is applied to a set of standard unconstrained, constrained and engineering design problems. The results are verified by comparing NSDA 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 NSDA algorithm 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 and faster convergence.

References

43 Cites in Article

Reference Format

C Kelley (1999). Detection and Remediation of Stagnation in the Nelder--Mead Algorithm Using a Sufficient Decrease Condition.
T Vogl,J Mangis,A Rigler,W Zink,D Alkon (1988). Accelerating the convergence of the back-propagation method.
Seyedali Mirjalili (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm.
Xin-She Yang (2010). Bat algorithm for multi-objective optimisation.
J Kennedy,R Eberhart (1995). Particle swarm optimization.
Marco Dorigo,Mauro Birattari,Thomas Stutzle (2006). Ant Colony Optimization.
H John (1992). Holland, adaptation in natural and artificial systems.
Amir Gandomi,Xin-She Yang,Amir Alavi (2013). Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems.
A Sadollah,A Bahreininejad,H Eskandar,M Hamdi (2013). Mine blast algorithm: a new population based algorithm for solving constrained engineering optimization problems.
Amir Gandomi,Amir Alavi (2012). Krill herd: A new bio-inspired optimization algorithm.
Amir Gandomi (2014). Interior search algorithm (ISA): A novel approach for global optimization.
Hans-Georg Beyer,Bernhard Sendhoff (2007). Robust optimization – A comprehensive survey.
J Knowles,R Watson,D Corne (2001). Reducing local optima in single-objective problems by multi-objectivization.
Kalyanmoy Deb,David Goldberg (1993). Analyzing Deception in Trap Functions.
C Coello (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art.
D Wolpert,W Macready (1997). No free lunch theorems for optimization.
Seyedali Mirjalili (2015). Dragonfly algorithm: a new metaheuristic optimization technique for solving singleobjective, discrete, and multi-objective problems.
A Panda,S Pani (2015). Multiobjective colliding bodies optimization.
C Coello Coello,G Pulido,M Lechuga (2004). Handling multiple objectives withparticle swarm optimization.
C Coello Coello,M Lechuga (2002). MOPSO: a proposal for multiple objectiveparticle swarm optimization.
K Deb,S Agrawal,A Pratap,T Meyarivan (2000). A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II.
K Deb,T Goel (2001). Controlled elitist nondominated sorting genetic algorithms for better convergence.
K Deb,A Pratap,S Agarwal,T Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
K Deb,A Pratap,S Agarwal,T Meyarivan (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II.
Arnapurna Panda,Sabyasachi Pani (2016). A Symbiotic Organisms Search algorithm with adaptive penalty function to solve multi-objective constrained optimization problems.
P Ngatchou,A Zarei,M El-Sharkawi (2005). Pareto multi objective optimization.
V Pareto (1964). Cours d'economie politique.
F Edgeworth (1881). To Mrs. Edgeworth.
Seyedali Mirjalili,Shahrzad Saremi,Seyed Mirjalili,Leandro Coelho (2016). Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization.
X.-S Yang (2011). Bat algorithm for multi-objective optimisation.
Reza Akbari,Ramin Hedayatzadeh,Koorush Ziarati,Bahareh Hassanizadeh (2012). A multi-objective artificial bee colony algorithm.
J Knowles,D Corne (2000). Approximating the nondominated front using the Pareto archived evolution strategy.
H Abbass,R Sarker,C Newton (2001). PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems.
Qingfu Zhang,Hui Li (2007). MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition.
E Zitzler (1999). Unknown Title.
E Zitzler,L Thiele (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach.
Ali Sadollah,Ardeshir Hadi Eskandar,Bahreininejad,Hoon Joong,Kim (2014). Water cycle algorithm for solving multi-objective optimization problems.
Ali Sadollaha,Hadi Eskandarb,Hoon Joong,Kim (2015). Water cycle algorithm for solving constrained multiobjectiveoptimization problems.
D Van Veldhuizen,G Lamont (1998). Multiobjective evolutionary algorithm research: A history and analysis.
J Schott (1995). Fault Tolerant Design Using Single and Multicriteria Genetic Algorithm Optimization.
C Coello (2000). Use of a self-adaptive penalty approach for engineering optimization problems.
C Coello,G Pulido (2005). Multiobjective structural optimization using a microgenetic algorithm.
A Kurpati,S Azarm,J Wu (2002). Constraint handling improvements for multiobjective genetic algorithms.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

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No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Pradeep Jangir. 2020. \u201cMONSDA: – A Novel Multi-objective Non-Dominated Sorting Dragonfly Algorithm\u201d. Global Journal of Research in Engineering - F: Electrical & Electronic GJRE-F Volume 20 (GJRE Volume 20 Issue F2): .

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GJRE Volume 20 Issue F2
Pg. 29- 52

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Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

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GJRE-F Classification: FOR Code: 290901p

Submission ReceivedDecember 14, 2019
Peer Review Double Blind
Handling Editor
Accepted December 31, 2019
Published January 15, 2020

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v1.2

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May 4, 2020

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