Generalized predictive control—Part I. The basic algorithm
Automatica (Journal of IFAC)
Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Integrated multiobjective optimization and a priori preferences using genetic algorithms
Information Sciences: an International Journal
Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Multiobjective evolutionary algorithms for multivariable PI controller design
Expert Systems with Applications: An International Journal
Comparison of design concepts in multi-criteria decision-making using level diagrams
Information Sciences: an International Journal
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The multi-objective optimization strategy called physical programming (PP) provides engineers with a flexible tool to express design preferences with a 'physical' meaning. For each objective or specification design, preferences are established through linguistic categories to which numerical values are assigned. In PP, this mapping is made using preference functions as piecewise splines whose curvatures are calculated with an expensive and iterative algorithm. However, mapping between design parameter space and objective space may be largely non-convex and is uninfluenced by the use of gradient-based methods for solving the optimization problem. In this paper, the philosophy of the PP method has been used, but two components have been totally redesigned: a simpler algorithm is used for the construction of preference functions; and the optimizer is replaced by a genetic algorithm that avoids possible local minima problems. Three engineering applications are shown to illustrate the value of this new method.