Hybrid Metaheuristic for Designing an End Effector as a Constrained Optimization Problem

Hybrid metaheuristics, explored in recent literature, are optimization methods that combine a global search metaheuristic with algorithms for refinement that in turn can be stochastic or deterministic. Although initially they were applied to combinatorial optimization, nowadays there are hybrid algorithms for a wide range of numerical problems: static or dynamic, mono or multi-objective, unconstrained or constrained, among others. In this paper a novel application of a hybrid method, MemMABC, is applied as a tool in a case study for the synthesis of an end effector presented as a constrained optimization problem, using a model for a two-finger gripper. The objective is to show the ability of hybrid metaheuristics as an alternative method for solving hard problems, specifically of numerical optimization. MemMABC is a memetic algorithm, that uses the modified artificial bee colony algorithm(MABC) for global searching and a version of random walk as local searcher, adapted to handle design constraints with an $\epsilon $ -constraint scheme. Grippers are end effectors used in a wide variety of robots, and are a good example of hard optimization problems. The simulation of results shows an accurate control of the gripping force along the opening range of the calculated mechanisms, suggesting that MemMABC can produce quality solutions for real-world engineering cases.