An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

Yintong Li,Hanjie Gao,Tong Han,Hui Zhao

doi:10.1109/access.2019.2933661

Abstract

To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

Highlights

Optimization refers to the process of obtaining a global optimal solution for a problem under the given conditions
In order to avoid the local optimum in solving complex optimization problems and improve the convergence accuracy of Whale Optimization Algorithm (WOA), we propose an adaptive WOA based on Gaussian distribution strategies
SIMULATION CONDITION In order to verify the rationality of the heterogeneous unmanned combat aerial vehicles (UCAVs) task allocation model proposed in this paper, we program based on MATLAB 2013a and perform simulation experiments on a computer with the Intel(R)Core(TM) i7- 4770K CPU@3.50GHz 8GB RAM

Summary

INTRODUCTION

Optimization refers to the process of obtaining a global optimal solution for a problem under the given conditions. The improved algorithm in terms of the local optimal avoidance ability and the local search ability had been improved at the expense of a large amount of computational cost; Khalil et al [16] proposed a distributed implementation of WOA, called MR-WOA, by using Hadoop MapReduce to improve the scalability of WOA for solving large-scale complex problems; For the defect of WOA premature convergence, Chen et al [17] introduced Lévy flight and chaotic local search into WOA to promote the balance exploration and exploitation These two strategies have been widely used to improve the performance of intelligent optimization algorithms. In order to avoid the local optimum in solving complex optimization problems and improve the convergence accuracy of WOA, we propose an adaptive WOA based on Gaussian distribution strategies.

MATHEMATICAL PRESENTATION OF GDS-WOA

PROPOSED UCAVs TASK ALLOCATION MODEL

TASK ALLOCATION CODING

CONCLUSION AND FUTURE WORK