Evolutionary optimization methods, for example, genetic algorithms and particle swarm optimization have been applied for solving multi-objective optimization problems, and have been observed to be useful for generating Pareto optimal solutions. In order to generate good approximate and well-distributed Pareto optimal solutions with a small number of function evaluations, this paper suggests a new recombination method by utilizing expected improvement and generalized data envelopment analysis in a real-coded genetic algorithm. In addition, the effectiveness of the proposed method will be investigated through several numerical examples, by comparison with the conventional methods.
In this paper, we suggest to apply artificial nonholonomic mechanical constraints to master-slave manipulation systems, so that the slave system can be operated by a dissimilar master system with relatively less numbers of degree-of-freedom. The cost for this reduction of degree-of-freedom, in general, should be compensated by skillful (or temporally complicated) operation of the master system. For this purpose, we propose an extended definition of manipulability considering the nature of small-time controllability of nonholonomic systems, and derive its numerical analysis as well. The proposed approach is illustrated with numerical simulations.
This paper proposes a constructing method of a petri net model based on HTPN(hierarchical timed petri net) and GA(genetic algorithm). When various design factors which have to be described in a petri net exist, the combination of the factors increases exponentially. In this research, the design factors are defined in the subnets and the optimal combination of subnets is selected by GA. In this way, the wide range design of petri net that had not been developed can be achieved. Moreover, as an application study, the effectiveness of proposal method has been inspected by applying to manufacturing process design problem. As a result, it is confirmed that the constructed petri net model can optimize the manufacturing lead time and its labor cost.