A Heuristic Initialized Memetic Algorithm for the Joint Allocation of Heterogeneous Stochastic Resources
Yipeng Wang, Bin Xin, Lihua Dou, Zhihong Peng · 2019
In this paper, a mathematical model for the joint allocation of two heterogeneous stochastic resources (namely, sensors and actuators) is presented, addressing the interdependencies between sensors and actuators, the resource constraints, the capability constraints as well as the strategy constraints. A heuristic initialized memetic algorithm (MA) is proposed to solve the joint allocation problem about stochastic resources (JASR). The integer-based dual-permutation encoding method is adopted and several permutation-based operators are involved in the process of crossover, mutation and local search. Besides, a hybrid initialization method is employed to maintain a balance between exploration and exploitation. For the performance evaluation, we build a general Monte Carlo simulation based JASR framework. Furthermore, we employ an extension of the state-of-the-art algorithm Swt_opt, MRBCH and BMA as competitors. Computational results show that the proposed MA performs very well in solving JASR instances of different scales, and it can generate better assignment schemes in most cases than its competitors in limited time.