“…The majority of the literature on design optimization of robotic manipulators use nonlinear and nonconvex methods to attack the problem. A brief account of such methods include the culling algorithm [2,3,4], different variations of the genetic algorithm [5,6,7,8], the differential evolution algorithm [9], performance-chart based methods [10], workspace atlases [11], controlled random search technique [12], and Monte Carlo method [13,14]. These methods have relative strengths and weaknesses;…”