APPLICATION OF IMPROVED WHALE OPTIMIZATION ALGORITHM IN MULTI-RESOURCE ALLOCATION

摘要

In this paper, we propose an Improved Whale Optimization Algorithm (I-WOA) to improve the performance of WOA in three aspects. First, nonlinearly changed convergence factor is introduced to preferably adjust exploration and exploitation process. Then, inspired by the inertia weight factor of Particle Swarm Optimization (PSO), we add new inertia weight factor to further enhance the exploitation and exploration ability of WOA. Finally, random variation of current optimal individual is conducted during exploitation iterations to reduce the possibility of falling into local optimum. The IWOA is benchmarked on 29 well-known test functions and the results are verified by comparing IWOA with basic WOA, Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO). We also apply the proposed IWOA to multi-resource allocation problem in resource-constrained embedded system. The results demonstrate that the proposed IWOA performs much better than WOA, GWO or PSO on most test functions and provides best performance in multi-resource allocation problem.