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
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Simulation-based conjoint ranking for optimal decision support process under aleatory uncertainty
Journal of Intelligent Manufacturing
Advances in Engineering Software
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A new approach for an integrated optimum design of a structural control system is described in this paper. The method considers the structure and active control system as a combined or an integrated system, i.e. the structural sizing variables, locations of controllers and the feedback control gain are both treated as design variables. The size of the structural members, the required control efforts and dynamic responses of the structure are considered as objective functions to be optimized. The simultaneous optimization of the structural control system is essentially formulated as a multi-objective optimization problem. To effectively address this problem, we propose a preference-based optimization model and a genetic algorithm is applied as a numerical searching technique. In the method, for each objective criterion, preference functions are defined that delineate degrees of desirability and optimum variables in both systems are simultaneously found through a preference-guided random searching process. As an example to verify the validity of the proposed approach, an earthquake-excited 10-story building is used and the numerical results are presented.