Hierarchical Parallel Processes of Genetic Algorithms for Design Optimization of Large-Scale Products

Abstract

A large-scale machine system often has a general hierarchical structure. For hierarchical structures, optimization is difficult because many local optima almost always arise, however genetic algorithms that have a hierarchical genotype can be applied to treat such problems directly. Relations between the structural components are analyzed and this information is used to partition the hierarchical structure. Partitioning large-scale problems into sub-problems that can be solved using parallel processed GAs increases the efficiency of the optimization search. The optimization of large-scale systems then becomes possible due to information sharing of Pareto optimum solutions for the sub-problems.