Title :
A New Multi-Criteria Mechatronic Design Methodology Using Niching Genetic Algorithm
Author :
Behbahani, Saeed ; De Silva, Clarence W.
Author_Institution :
Mechanical Engineering Department of the University of British Columbia (UBC), Vancouver, BC, Canada
Abstract :
Due to the presence of a wide range of interactive criteria involved in a mechatronic system, a system-based design methodology is needed to achieve optimum mechatronic design. Mechatronic Design Quotient (MDQ) is employed as a multi-criteria design evaluation index in order to develop a concurrent and system-based design approach. MDQ is a multi-criteria index reflecting the global sense of design satisfaction, which is computed by a nonlinear fuzzy integral for aggregation of different criteria. It can be used for the purposes of optimization and/or decision making in different stages of design. In this paper, it serves to evaluate the fitness of design trials in an optimization process. Optimization process is performed in two stages because comprehensive MDQ evaluation of each design trial is time consuming. In the first stage, niching genetic algorithm is used to find local and global optimal design alternatives with respect to some essential MDQ attributes. In the second stage, these local optima will compete with each other, with respect to all criteria involved in MDQ. The developed design methodology offers a concurrent, integrated, and multi-criteria approach, which will provide a mechatronic design that is optimal with respect to the design criteria included in the MDQ. The performance of the developed methodology is validated by applying it to the design of the motion system of an industrial fish cutting machine called Iron Butcher an electromechanical system which falls into the class of mixed or multi-domain.
Keywords :
genetic algorithms; mechatronics; design satisfaction; electromechanical system; mechatronic design quotient; multi-criteria mechatronic design; multi-domain systems; niching genetic algorithm; Algorithm design and analysis; Decision making; Design methodology; Design optimization; Genetic algorithms; Iron; Marine animals; Mechatronics; Metals industry; Performance evaluation;
Conference_Titel :
Evolutionary Computation, 2006. CEC 2006. IEEE Congress on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9487-9
DOI :
10.1109/CEC.2006.1688326