An improved multi-objective optimization method based on adaptive mutation particle swarm optimization and fuzzy statistics algorithm

ABSTRACT This paper proposes an adaptive mutation particle swarm optimization (AMPSO) to realize multi-objective optimization design method through scale-based product platform theory model. The Pareto-optimal solution was obtained via AMPSO, then the fuzzy statistics algorithm is presented to extract the optimal solution of multi-objective optimization problem. The Multi-objective Optimization Method was carried out in two stages. In the first stage, each product is optimized independently via AMPSO and the product platform constant parameter and its value is obtained according to the change ratio of design variables; In the second stage, the scaling variables of each product are solved via AMPSO based on the optimization objectives improving the performance in constraint of restrictions and the best compromise solution is extracted based on fuzzy statistics algorithm.

[1]  Yuan Ji-jun.  Optimal design for scale-based product family based on multi-objective firefly algorithm , 2012 .

[2]  Andrew Y. C. Nee,et al.  A hybrid group leader algorithm for green material selection with energy consideration in product design , 2016 .

[3]  Javier P. Gonzalez-Zugasti,et al.  Assessing value in platformed product family design , 2001 .

[4]  Seung Ki Moon,et al.  A Market-Based Design Strategy for a Universal Product Family , 2012 .

[5]  Marc H. Meyer,et al.  The power of product platforms : building value and cost leadership , 1997 .

[6]  Timothy W. Simpson,et al.  A Methodology to Support Product Family Redesign Using Genetic Algorithm and Commonality Indices , 2005, DAC 2005.

[7]  Timothy W. Simpson,et al.  A Variation-Based Method for Product Family Design , 2002 .

[8]  Yixiong Feng,et al.  Anti-vibration optimization of the key components in a turbo-generator based on heterogeneous axiomatic design , 2017 .

[9]  Gülşen Akman,et al.  Advances in Product Family and Product Platform Design , 2013 .

[10]  Saman K. Halgamuge,et al.  Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.

[11]  Wenjun Zhang,et al.  Dissipative particle swarm optimization , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).

[12]  Seung Ki Moon,et al.  Platform design variable identification for a product family using multi-objective particle swarm optimization , 2014 .

[13]  Michael J. Scott,et al.  Effective Product Family Design Using Preference Aggregation , 2006 .

[14]  Yixiong Feng,et al.  Interval multiobjective optimization of structures based on radial basis function, interval analysis, and NSGA-II , 2014 .

[15]  Gui Wei-hua.  Particle Swarm Optimization Algorithm with Adaptive Mutation , 2008 .

[16]  Timothy W. Simpson,et al.  Assessing Variable Levels of Platform Commonality Within a Product Family Using a Multiobjective Genetic Algorithm , 2004, Concurr. Eng. Res. Appl..

[17]  Li Li,et al.  Genetic algorithm-based optimisation method for product family design with multi-level commonality , 2008 .

[18]  Timothy W. Simpson,et al.  Robust Design of Families of Products With Production Modeling and Evaluation , 2001 .

[19]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[20]  Zahed Siddique,et al.  Advances in product family and product platform design: Methods & applications , 2014 .

[21]  Harrison Hyung Min Kim,et al.  Incorporating security considerations into optimal product architecture and component sharing decision in product family design , 2012 .

[22]  Ajith Abraham,et al.  Particle Swarm Optimization Using Adaptive Mutation , 2008, 2008 19th International Workshop on Database and Expert Systems Applications.