MPI-based parallel synchronous vector evaluated particle swarm optimization for multi-objective design optimization of composite structures

2012 ◽  
Vol 25 (8) ◽  
pp. 1611-1627 ◽  
Author(s):  
S.N. Omkar ◽  
Akshay Venkatesh ◽  
Mrunmaya Mudigere
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Composite Structures ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective

Quantum behaved Particle Swarm Optimization (QPSO) for multi-objective design optimization of composite structures

2009 ◽  
Vol 36 (8) ◽  
pp. 11312-11322 ◽  
Author(s):  
S.N. Omkar ◽  
Rahul Khandelwal ◽  
T.V.S. Ananth ◽  
G. Narayana Naik ◽  
S. Gopalakrishnan
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Composite Structures ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Multi Objective

Multi-objective collaborative multidisciplinary design optimization using particle swarm techniques and fuzzy decision making

2012 ◽  
Vol 226 (9) ◽  
pp. 2281-2295 ◽  
Author(s):  
Mohammad Reza Farmani ◽  
Jafar Roshanian ◽  
Meisam Babaie ◽  
Parviz M Zadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Collaborative Optimization ◽  
Multidisciplinary Design ◽  
Fuzzy Decision ◽  
Swarm Optimization ◽  
Multi Objective

This article focuses on the efficient multi-objective particle swarm optimization algorithm to solve multidisciplinary design optimization problems. The objective is to extend the formulation of collaborative optimization which has been widely used to solve single-objective optimization problems. To examine the proposed structure, racecar design problem is taken as an example of application for three objective functions. In addition, a fuzzy decision maker is applied to select the best solution along the pareto front based on the defined criteria. The results are compared to the traditional optimization, and collaborative optimization formulations that do not use multi-objective particle swarm optimization. It is shown that the integration of multi-objective particle swarm optimization into collaborative optimization provides an efficient framework for design and analysis of hierarchical multidisciplinary design optimization problems.

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