TY  - JOUR
TI  - Analyzing the performances of evolutionary multi-objective optimizers on design
optimization of robot gripper configurations
AB  - Robot grippers are widely used in a variety of areas requiring automation, precision, and safety. The perfor-
mance of the grippers is directly associated with their design. In this study, four different multiobjective metaheuristic
algorithms including particle swarm optimization (MOPSO), artificial algae algorithm (MOAAA), grey wolf optimizer
(MOGWO) and nondominated sorting genetic algorithm (NSGA-II) were applied to two different configurations of highly
nonlinear and multimodal robot gripper design problem including two objective functions and a certain number of con-
straints. The first objective is to minimize the difference between minimum and maximum forces for the assumed range
in which the gripper ends are displaced. The second objective is force transmission rate that is the ratio of the actuator
force to the minimum holding force obtained at the gripper ends. The performance of the optimizers was examined
separately for each configuration by using pareto-front curves and hyper-volume (HV) metric. Performances of the op-
timizers on the specific problem were compared with results of previously proposed algorithms under equal conditions.
With respect to these comparisons, the best-known results of the configurations were obtained. Furthermore, the pareto
optimal solutions are thoroughly examined to present the relationship between design variables and objective functions.
AU  - sahin, ismail
AU  - Dörterler, Murat
AU  - Durgut, Rafet
AU  - ATILA, ÜMIT
DO  - 10.3906/elk-2003-140
PY  - 2021
JO  - Turkish Journal of Electrical Engineering and Computer Sciences
VL  - 29
IS  - 1
SN  - 1300-0632
SP  - 349
EP  - 369
DB  - TRDizin
UR  - http://search/yayin/detay/514179
ER  -