Solving a Redundancy Allocation Problem by a Hybrid Multi-objective Imperialist Competitive Algorithm

Abstract

A redundancy allocation problem (RAP) is a well-known NP-hard problem that involves the selection of elements and redundancy levels to maximize the system reliability under various system-level constraints. In many practical design situations, reliability apportionment is complicated because of the presence of several conflicting objectives that cannot be combined into a single-objective function. As telecommunications, manufacturing and power systems are becoming more and more complex, while requiring short developments schedules and very high reliability, it is becoming increasingly important to develop efficient solutions to the RAP. In this paper, a new hybrid multi-objective imperialist competition algorithm (HMOICA) based on imperialist competitive algorithm (ICA) and genetic algorithm (GA) is proposed for the first time in multi-objective redundancy allocation problems. In the multi-objective formulation, the system reliability is maximized while the cost and volume of the system are minimized simultaneously. Additionally, a response surface methodology (RSM) is employed to tune the ICA parameters. The proposed HMOICA is validated by some examples with analytical solutions. It shows its superior performance compared to a non-dominated sorting genetic algorithm (NSGA-II) and Pareto archive evolution strategy algorithm (PAES). Finally, the conclusion is given.