The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Nature Inspired Methods for Multi-Objective Optimization
|
Author(s): Sanjoy Das (Kansas State University, USA), Bijaya K. Panigrahi (Indian Institute of Technology, India)and Shyam S. Pattnaik (National Institute of Technical Teachers’ Training & Research, India)
Copyright: 2010
Pages: 14
Source title:
Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques
Source Author(s)/Editor(s): Emilio Soria Olivas (University of Valencia, Spain), José David Martín Guerrero (University of Valencia, Spain), Marcelino Martinez-Sober (University of Valencia, Spain), Jose Rafael Magdalena-Benedito (University of Valencia, Spain)and Antonio José Serrano López (University of Valencia, Spain)
DOI: 10.4018/978-1-60566-766-9.ch004
Purchase
|
Abstract
This chapter focuses on the concepts of dominance and Pareto-optimality. It then addresses key issues in applying three basic classes of nature inspired algorithms – evolutionary algorithms, particle swarm optimization, and artificial immune systems, to multi-objective optimization problems. As case studies, the most significant multi-objective algorithm from each class is described in detail. Two of these, NSGA-II and MOPSO, are widely used in engineering optimization, while the others show excellent performances. As hybrid algorithms are becoming increasingly popular in optimization, this chapter includes a brief discussion of hybridization within a multi-objective framework.
Related Content
Bhargav Naidu Matcha, Sivakumar Sivanesan, K. C. Ng, Se Yong Eh Noum, Aman Sharma.
© 2023.
60 pages.
|
Lavanya Sendhilvel, Kush Diwakar Desai, Simran Adake, Rachit Bisaria, Hemang Ghanshyambhai Vekariya.
© 2023.
15 pages.
|
Jayanthi Ganapathy, Purushothaman R., Ramya M., Joselyn Diana C..
© 2023.
14 pages.
|
Prince Rajak, Anjali Sagar Jangde, Govind P. Gupta.
© 2023.
14 pages.
|
Mustafa Eren Akpınar.
© 2023.
9 pages.
|
Sreekantha Desai Karanam, Krithin M., R. V. Kulkarni.
© 2023.
34 pages.
|
Omprakash Nayak, Tejaswini Pallapothala, Govind P. Gupta.
© 2023.
19 pages.
|
|
|