Spreading Algorithm for Single-Objective Problems

This paper addresses the problem of finding several different solutions with the same optimum performance in single objective real-world engineering problems. In this paper a parallel robot design was proposed. Thereby, this paper presents a genetic algorithm to optimize uni-objective problems with an infinite number of optimal solutions. The algorithm uses the maximin concept and -dominance to promote diversity over the admissible space. The performance of the proposed algorithm is analyzed with three well-known test functions and one function obtained from practical realworld engineering optimization problems. A spreading analysis is performed showing that the solutions drawn by the algorithm are well dispersed.