Abstract
In this paper, we present a new stochastic hybrid technique for constrained global optimization. It is a combination of the electromagnetism-like (EM) mechanism with a random local search, which is a derivative-free procedure with high ability of producing a descent direction. Since the original EM algorithm is specifically designed for solving bound constrained problems, the approach herein adopted for handling the inequality constraints of the problem relies on selective conditions that impose a sufficient reduction either in the constraints violation or in the objective function value, when comparing two points at a time. The hybrid EM method is tested on a set of benchmark engineering design problems and the numerical results demonstrate the effectiveness of the proposed approach. A comparison with results from other stochastic methods is also included.
Acknowledgements
The authors would like to thank the two anonymous referees for their constructive comments and suggestions.
Notes
We used a value of 32 for the maximum stopping time constraint, taken from the Pareto front in Citation23.
We used a value of 0.04 for the maximum joint displacement constraint, taken from the Pareto front Citation23.
, as shown in Citation16.