Abstract
In complex systems, failure dependencies play a crucial role in determining their overall performance. This paper explores the multi-objective optimization of series-parallel systems with mixed failure dependencies. By optimizing system cost and availability, the study aims to identify the most efficient redundancy and repair strategies. Two optimization algorithms, the non-dominated sorting genetic algorithm II (NSGA-II) and a novel multi-objective algorithm named the multi-objective hoopoe heuristic (MOHH), are utilized alongside constraint handling techniques to produce Pareto fronts. These fronts illustrate the trade-offs between cost and availability. Additionally, a fuzzy decision method is utilized to determine the best compromise solutions from each optimization technique. Comparing the results, NSGA-II consistently outperforms MOHH in providing better compromise solutions across five independent runs. However, MOHH demonstrates a better standard deviation in its performance.
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