A Novel Crowding Genetic Algorithm and Its Applications to Manufacturing Robots

Abstract

A niche genetic algorithm (GA) based on a novel twin-space crowding (TC) approach is proposed for solving multimodal manufacturing optimization problems. The proposed TC method is designed in a parameter-free paradigm. That is, when cooperatively exploring solutions with GAs, it does not require prior knowledge related to the solution space to design additional problem-dependent parameters in the evolutionary process. This feature makes the proposed TC method suitable for assisting GAs in solving real-world engineering optimization problems involving intractable solution landscapes. A set of numerical benchmark functions is used to compare effectiveness and efficiency in the proposed TCGA, in different niche GAs, and in several evolutionary computation methods. The TCGA is then used to solve multimodal joint-space inverse problems in serial-link robots to compare its convergence performance with that of conventional methods that apply the sharing function. Finally, the TCGA is used to solve iterative collision-free design problems for linkage-bar robotic hands to demonstrate its effectiveness for generating diverse solutions during the design process.