A time-efficient solution approach for multi/many-task reliability redundancy allocation problems using the online transfer parameter estimation based multifactorial evolutionary algorithm

This paper introduces a time efficient solution approach for multi/many-task RRAP under the framework of the novel online transfer parameter estimation based multi-factorial evolutionary algorithm (MFEA-II). To represent similarity between tasks, the basic MFEA utilizes a single value for transfer parameter leading to negative knowledge transfer during the evolution process as different pair of tasks often have different level of similarity. Proposed MFEA-II based solution approach avoids above problem while solving RRAPs simultaneously by employing online transfer parameter estimation based MFEA-II. To demonstrate the efficiency of the proposed approach, two set of problems (or test sets) are considered with more than two RRAPs. The test set-1 (TS-1) portray the scenario of multi-tasking by considering three problems while test set-2 (TS-2) considers the many-tasking scenario with four problems. The TS-1 includes three RRAP problems: a series system, a complex bridge system, and a series-parallel system. The TS-2 includes these three problems plus a new RRAP problem: the over-speed protection system of a gas turbine. We address each test set using the MFEA-II framework by incorporating the solution structures of all problems into a single solution. For comparison, basic MFEA is utilized to solve each test sets similar to MFEA-II. Subsequently, each problem is also solved independently using genetic algorithms (GA) and particle swarm optimization (PSO). The simulation results are evaluated based on the average of the best reliability, total computation time, performance ranking, and statistical significance tests. The outcome shows that even if the number of tasks increases in a multi-tasking environment, our proposed approach can generate better results compared to basic MFEA as well as single-task optimizer. Moreover, in terms of computation time, the proposed approach provides 6.96 % deteriorated and 2.46 % improved values compared to basic MFEA in TS-1 & TS-2, respectively. In comparison to single task optimizer, proposed MFEA-II provides 40.60 % and 53.43 % faster than GA and 52.25 % and 62.70 % faster than PSO for TS-1 and TS-2, respectively. Further, to rank the algorithm in terms of quality of reliability values and computation time, the multi-criteria decision-making method named TOPSIS method is utilized, where the proposed approach secured the top rank.