An adaptive genetic assembly-sequence planner

Assembly sequence planning is a combinatorial optimization problem with hig hly nonlinear geometric constraints. Most proposed solution methodologies a re based on graph theory and involve complex geometric and physical analyse s. As a result, even for a simple structure, it is difficult to take all im portant criteria into account and to find real-world solutions. This paper proposes an adaptive genetic algorithm (AGA) for efficiently finding global -optimal or near-global-optimal assembly sequences. The difference between an adaptive genetic algorithm and a classical genetic algorithm is that gen etic-operator probabilities for an adaptive genetic algorithm are varied ac cording to certain rules, but genetic operator probabilities for a classica l genetic algorithm are fixed. For our AGA, we build a simulation function to pre-estimate our GA search process, use our simulation function to calcu late optimal genetic-operator probability settings for a given structure, a nd then use our calculated genetic-operator probability settings to dynamic ally optimize our AGA search for an optimal assembly sequence. Experimental results show that our adaptive genetic assembly-sequence planner solves co mbinatorial assembly problems quickly, reliably, and accurately.