Generating optimal topologies in structural design using a homogenization method
Computer Methods in Applied Mechanics and Engineering
Structural boundary design via level set and immersed interface methods
Journal of Computational Physics
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Compact Unstructured Representations for Evolutionary Design
Applied Intelligence
A Hybrid Multi-objective Evolutionary Approach to Engineering Shape Design
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multi-objective optimization of structures topology by genetic algorithms
Advances in Engineering Software - Special issue on evolutionary optimization of engineering problems
On initial populations of a genetic algorithm for continuous optimization problems
Journal of Global Optimization
Finite Elements in Analysis and Design
An improved genetic algorithm with initial population strategy and self-adaptive member grouping
Computers and Structures
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Hybrid Evolutionary Algorithms
Hybrid Evolutionary Algorithms
Initial population construction for convergence improvement of MOEAs
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A Sequential Element Rejection and Admission (SERA) method for compliant mechanisms design
Structural and Multidisciplinary Optimization
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Genetic algorithms (GAs) can precisely handle the discrete structural topology optimization of single-piece elastic structures called compliant mechanisms. The initial population of these elastic structures is mostly generated by assigning the material at random. This causes disconnected or unfeasible designs and further rule-based repairing can result in representation degeneracy. However, the problem-specific initial population can affect the performance of GAs like other operators. In this paper, a domain-specific initial population strategy is developed that generates geometrically feasible structures for path generating compliant mechanisms (PGCMs). It is coupled with the elitist non-dominated sorting genetic algorithm (NSGA-II) which has been customized for structural topology optimization. The performance of initial population strategy over random initialization using customized NSGA-II is checked on single and bi-objective optimization problems. Based on the results, it is observed that the custom initialization outperforms the random initialization by dominating all the solutions and exploring larger area of posed objectives. The elastic structures obtained by solving two examples of PGCMs using domain specific initial population strategy are also presented.