Optimization of complex system reliability by a modified great deluge algorithm

In this paper, a global optimization meta-heuristic, the great deluge algorithm, is extended and applied to optimize the reliability of complex systems. Two different kinds of optimization problems (i) Reliability optimization of a complex system with constraints on cost and weight (ii) Optimal redundancy allocation in a multi-stage mixed system with constraints on cost and weight are solved to demonstrate the effectiveness of the algorithm. A software developed in ANSI C implements the algorithm. In terms of both accuracy and speed, it is observed that the present algorithm, the modified great deluge algorithm (MGDA), yielded far superior results compared to those obtained by the simulated annealing, the improved non-equilibrium simulated annealing and other optimization algorithms. Further, when both accuracy and speed are considered simultaneously, both MGDA and another meta-heuristic, ant colony optimization (ACO) yielded comparable results. In conclusion, the MGDA can be used as an efficient alternative to ACO and other existing optimization techniques.