A Hybrid Simplex Non-dominated Sorting Genetic Algorithm for Multi- Objective OptimizationSeid H Pourtakdoust* and Seid M Zandavi
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
- *Corresponding Author:
- Seid H Pourtakdoust
Department of Aerospace Engineering
Sharif University of Technology, Tehran, Iran
E-mail: [email protected]
Received date: August 26, 2016; Accepted date: August 29, 2016; Published date: August 31, 2016
Citation: Pourtakdoust SH, Zandavi SM (2016) A Hybrid Simplex Non-dominated Sorting Genetic Algorithm for Multi-Objective Optimization. Int J Swarm Intel Evol Comput 5:138. doi:10.4172/2090-4908.1000138
Copyright: ©2016 Pourtakdoust SH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
This paper introduces a hybrid scheme for multi-objective optimization problems via utility of two established heuristic algorithms. The proposed hybrid scheme consists of two parts that include the Nelder-Mead simplex algorithm (SA) as well as the Non-dominated Sorting Genetic Algorithm II (NSGA II). In this respect, subsequent to NSGA II sorting for the optimum points, the SA searches the optimum set to find the local optimal points and thus localize a promising area that is likely to contain the global minimum. This is especially helpful since SA is an efficient algorithm that can accurately and quickly exploit the promising area for the optimum point. The proposed hybrid scheme is applied to multi- objective optimization of some bench mark functions and its performance is compared against those of the classical NSGA II as well as the Multi-Objective Particle Swarm Optimization (MOPSO). The numerical results show that the proposed hybrid scheme provides competitive results that outperform those of the existing algorithms.