Call for Paper - July 2022 Edition
IJCA solicits original research papers for the July 2022 Edition. Last date of manuscript submission is June 20, 2022. Read More

Review of Multi-objective Optimization using Genetic Algorithm and Particle Swarm Optimization

Print
PDF
IP Multimedia Communications
© 2011 by IJCA Journal
ISBN : 978-93-80864-99-3
Year of Publication: 2011
Authors:
Monika Shukla
B.S.Dhaliwal

Monika Shukla and B.S.Dhaliwal. Review of Multi-objective Optimization using Genetic Algorithm and Particle Swarm Optimization. Special issues on IP Multimedia Communications (1):72-74, October 2011. Full text available. BibTeX

@article{key:article,
	author = {Monika Shukla and B.S.Dhaliwal},
	title = {Review of Multi-objective Optimization using Genetic Algorithm and Particle Swarm Optimization},
	journal = {Special issues on IP Multimedia Communications},
	month = {October},
	year = {2011},
	number = {1},
	pages = {72-74},
	note = {Full text available}
}

Abstract

Many real-world problems involve simultaneous optimization of multiple objectives that often are competing. In such problems, the objectives to be optimized are normally in conflict with respect to each other, which means that there is no single solution for these problems and optimizing a particular solution with respect to a single objective can result in unacceptable results with respect to the other objectives. So the solution to this problem is to find a set of solutions , each of which satisfies the objectives at an acceptable level without being affected by any other solution. This review paper presents an overview of multi-objective optimization using GA and PSO.

Reference

  1. Margarita Reyes-Sierra and Carlos A. Coello Coello,“ Multi-Objective Particle Swarm Optimizers: A Survey of the State-of the-Art”, International Journal of Computational Intelligence Research. ISSN 0973-1873 Vol.2, No.3 (2006), pp. 287–308 °c Research India Publications http://www.ijcir.info
  2. Blas J. Galván,“ New Trends in Multi-objective Evolutionary Algorithms”, http://www.step.es/~bgalvan/
  3. MarcoFarina, Kalyanmoy Deb and Paolo Amato, “Dynamic multi-objective optimization problems:Test cases, Approximations, Applications”, IEEE Transactions on evolutionary Computation Vol.8,No.5,October 2004.
  4. Alexandre H. F. Dias and Jõao A. de Vasconcelos, “Multi-objective Genetic Algorithms Applied to Solve Optimization Problems”, IEEE TRANSACTIONS ON MAGNETICS, VOL. 38, NO. 2, MARCH 2002.
  5. S.G.Ponnambalam, S.Saravana Sankar, S.Sriram and M.Gurumarimuthu, “PARALLEL POPULATIONS GENETIC ALGORITHM FOR MINIMIZING ASSEMBLY VARIATION IN SELECTIVE ASSEMBLY”, Proceeding of the 2006 IEEE International Conference on Automation Science and Engineering Shanghai, China, October 7-10, 2006.
  6. Abdullah Konak, David W. Coit, and Alice E. Smith, “Multi-objective optimization using genetic algorithms: A tutorial”, Reliability Engineering and System Safety 91 (2006) 992–1007.
  7. Xiaohui Hu , and Russell Eberhart, “Multi-objective Optimization Using Dynamic Neighborhood Particle Swarm Optimization”, 0-7803-7282-4/02/$10.00 02002 IEEE.
  8. Jonathan E. Fieldsend,“ Multi-objective particle swarm optimisation methods”, http://www.computelligence.org/pso/bibliography.htm.
  9. Carlos A. Coello Coello, Gregorio Toscano Pulido, and Maximino Salazar Lechuga, “Handling Multiple Objectives With Particle Swarm Optimization”, IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 8, NO. 3, JUNE 2004.
  10. M. Janga Reddy and D. Nagesh Kumar, “Multi-objective particle swarm optimization for generating optimal trade-offs in reservoir operation”, HYDROLOGICAL PROCESSES Hydrol. Process. 21, 2897–2909 (2007) Published online 10 January 2007 in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/hyp.6507.
  11. Zhan Si Jiang, Jia Wei Xiang and Hui Jiang, “Multi-objective Particle Swarm Optimization Method Based on Fitness Function and Sequence Approximate Model”, 978-0-7695-3899-0/09 $29.00 © 2009 IEEE DOI 10.1109/WGEC.2009.115.