A suitable use of software packages for optimization problems can give the possibility to formulate design problems of robotic mechanical systems by taking into account the several aspects and behaviours for optimum solutions both in design and operation. However, an important issue that can be even critical to obtain practical solutions can be recognized in a proper identification and formulation of criteria for optimability purposes and numerical convergence feasibility. In this paper, we have reported experiences that have been developed at LARM in Cassino by referring to the abovementioned issues of determining a design procedure for manipulators both of serial and parallel architectures. The optimality criteria are focused on the well-recognized main aspects of workspace, singularity, and stiffness. Computational aspects are discussed to ensure numerical convergence to solutions that can be also of practical applications. In particular, optimality criteria and computational aspects have been elaborated by taking into account the peculiarity and constraint of each other. The general concepts and formulations are illustrated by referring to specific numerical examples with satisfactory results.
The present study has been taken up to emphasize the role of the hybridization process for optimizing a given reinforced concrete (RC) frame. Although various primary techniques have been hybrid in the past with varying degree of success, the effect of hybridization of enhanced versions of standard optimization techniques has found little attention. The focus of the current study is to see if it is possible to maintain and carry the positive effects of enhanced versions of two different techniques while using their hybrid algorithms. For this purpose, enhanced versions of standard particle swarm optimization (PSO) and a standard gravitational search algorithm (GSA), were considered for optimizing an RC frame. The enhanced version of PSO involves its democratization by considering all good and bad experiences of the particles, whereas the enhanced version of the GSA is made self-adaptive by considering a specific range for certain parameters, like the gravitational constant and a set of agents with the best fitness values. The optimization process, being iterative in nature, has been coded in C++. The analysis and design procedure is based on the specifications of Indian codes. Two distinct advantages of enhanced versions of standard PSO and GSA, namely, better capability to escape from local optima and a faster convergence rate, have been tested for the hybrid algorithm. The entire formulation for optimal cost design of a frame includes the cost of beams and columns. The variables of each element of structural frame have been considered as continuous and rounded off appropriately to consider practical limitations. An example has also been considered to emphasize the validity of this optimum design procedure.