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Abstract
This article presents a hybrid swarm intelligence of artificial immune system tuned with Taguchi-genetic algorithm and its field-programmable gate array realization to optimal inverse kinematics for a 5-degree-of-freedom industrial robotic manipulator using system-on-a-programmable-chip technology. This hybridization strategy with a Taguchi-genetic algorithm parameter tuner improves the performance in conventional artificial immune system paradigms. The fieldprogrammable gate array realization of the proposed artificial immune system tuned with Taguchi-genetic algorithm is more effective in practice for real-world embedded applications. This system-on-a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm is then applied to the optimal inverse kinematics redundancy solver of a 5-degree-of-freedom robotic manipulator. The optimal joint configuration is obtained by minimizing the predefined affinity function in artificial immune system tuned with Taguchi-genetic algorithm for real-world embedded robotics application. The experimental results and comparative works are presented to show the effectiveness and merit of the proposed system-on-a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm intelligent inverse kinematics redundancy solver for a 5-degree-of-freedom industrial robotic manipulator. This systemon- a-programmable-chip-based artificial immune system tuned with Taguchi-genetic algorithm solver outperforms the conventional solvers, such as geometric solvers, Jacobian-based solvers, hybrid genetic algorithm solvers, and particle swarm optimization solvers.
Keywords
Artificial immune system, inverse kinematics, Taguchi, system-on-a-programmable-chip
Date received: 22 March 2015; accepted: 22 November 2015
Academic Editor: Jianbo Yu
(ProQuest: ... denotes formulae omitted)
Introduction
Swarm intelligence is a set of nature-inspired computational methodologies for solving the optimization problems in a wide variety of real-world applications.1-3 Over the past few years, some of the well-known optimization paradigms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) have been successfully employed to address the engineering optimization problems.1-7 Among these modern metaheuristic-based approaches, artificial immune system (AIS) inspired by the biological immune system was introduced by De Castro and Timmis8 and has emerged as an efficient computational paradigm to NP-hard combinatorial optimization problems. These adaptive and robust AISs have been successfully applied in many disciplines by exploiting its strong optimization ability.8-12
To date, there are some modified hybrid AISs proposed to improve the performance, for example, Yap et al.13 proposed a hybrid PSO-based AIS for multimodal function optimization and engineering application problem in which half the population of AIS is generated from PSO. Mohammed et al.14 presented...