Pareto optimal design of reconfigurable rice seedling transplanting mechanisms using multi-objective genetic algorithm. Issue 7 (October 2016)

• Record Type: Journal Article
• Authors: Felezi, M.; Vahabi, S.; Nariman-zadeh, N.
• Is Part Of: Neural computing & applications. Volume 27:Issue 7(2016)
• Page Start: 1907
• Page End: 1916
• Publication Date: 2016-10
• Subjects: Optimization -- Genetic algorithm -- Four-bar mechanism -- Transplanting machine -- Pareto; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1982-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract In this paper, an optimal reconfigurable four-bar linkage for a rice seedling transplanting machine is synthesized by a multi-objective uniform-diversity genetic algorithm. The design procedure has consisted of two stages. At the first stage of the design, a multi-objective synthesis of a four-bar linkage as a rice seedling transplant mechanism is accomplished considering two important objective functions of tracking error of an efficient path for planting of rice seedling and transmission angle's deviation from 90° of transplant mechanism. At the second stage of the design, a further single-objective optimization is performed to refine the selected design from the first stage so that a reconfigurable four-bar linkage can be constructed for efficient and optimum operation condition as a seedling transplantation machine. Consequently, some practical curves for optimum design configuration of the mechanism are presented meeting the different specifications of both depth of seedling planting and forward velocity of the transplanter.