Design of truss-structures for minimum weight using genetic algorithms
Finite Elements in Analysis and Design
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Constrained Test Problems for Multi-objective Evolutionary Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Structural and Multidisciplinary Optimization
Multiobjective topology optimization of truss structures with kinematic stability repair
Structural and Multidisciplinary Optimization
A new multi-swarm multi-objective optimization method for structural design
Advances in Engineering Software
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This paper uses genetic algorithm to handle the topology and sizing optimization of truss structures, in which a sparse node matrix encoding approach is used and individual identification technique is employed to avoid duplicate structural analysis to save computation time. It is observed that NSGA-II could not improve the convergence of non-dominated front at latter generations when solving multi-objective topology and sizing optimization of truss structures. Therefore, an adaptive multi-island search strategy for multi-objective optimization problem (AMISS-MOP) is developed to enhance the convergence. Meanwhile, an elitist strategy based on archive set is introduced to reduce the size of non-dominated sorting to improve computation efficiency. Two numeric examples are presented to demonstrate the performance of AMISS-MOP. Results show that the global Pareto front could be found by AMISS-MOP, the convergence is improved as generation increases, and the time spent on non-dominated sorting is reduced.