Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
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This work presents a method to develop computational models for topology optimization of linear elastic structures, when we have more than one objective function, each of them with different individual optimal solution.The method is developed for multi-objective optimization problems. Its purpose is to evolve an evenly distributed group of solutions to determinate the optimum Pareto set for the given problem.The algorithm evaluates a set of solutions (population) sorting it by considering domination properties and using a definition of filter to retain Pareto solutions.To reduce computational effort, it is introduced, in the initial population, optimal solutions of each of the single-objective problems.The computational model is applied and tested in two numerical applications.