Topology optimization of compliant mechanism using multi-objective particle swarm optimization

ABSTRACT

In this paper, a multi-objective particle swarm optimization approach (popularly known as MOPSO) for topology optimization of compliant mechanism is proposed. Multi-objective strategy has a great advantage, over other single objective approaches, in finding a well distributed set of non-dominated solutions in a single run which makes post-processing and decision making convenient. The stochastic multi-objective strategy also overcomes the issue of 'initialization of design space' upon which the final solutions may depend. Here, MOPSO is coupled with Material-Mask overlay strategy using honeycomb discretization to obtain optimal single-material compliant topologies that are free from the pathologies of .checker board. and 'point flexure'. An attempt to study the performance of proposed MOPSO is made by employing different techniques, both existing and newly proposed, of selecting the 'personal best' and 'global best'. In particular, a newer idea of allowing each particle to memorize all non-dominated personal best particles which it has encountered is introduced, i.e. if updated personal best position be indifferent to the old one, we keep both in the personal archive. This newly proposed strategy of particle memory seems to outperform the existing ones significantly.

REFERENCES

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INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.1 NUMERICAL ANALYSIS
      G.1.6 Optimization

General Terms:
Algorithms

Keywords:
topology optimization