Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
About selecting the personal best in multi-objective particle swarm optimization
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A MOPSO algorithm based exclusively on pareto dominance concepts
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Rapid prototyping using evolutionary approaches: part 1
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Rapid prototyping using evolutionary approaches: part 2
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Evolutionary multi-objective optimization and decision making for selective laser sintering
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A 199-line Matlab code for Pareto-optimal tracing in topology optimization
Structural and Multidisciplinary Optimization
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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.