Whale optimization algorithm with applications to power allocation in interference networks

Abstract

Power allocation plays a pivotal role in improving the communication performance of interference-limited wireless network (IWN). However, the optimization of power allocation is usually formulated as a mixed-integer non-linear programming (MINLP) problem, which is hard to solve. Whale optimization algorithm (WOA) has recently gained the attention of the researcher as an efficient method to solve a variety of optimization problems. WOA algorithm also has the disadvantages of low convergence accuracy and easy to fall into local optimum. To solve the above problems, we propose Cosine Compound Whale Optimization Algorithm (CCWOA). First of all, its unique cosine nonlinear convergence factor can balance the rate of the whole optimization process and prevent the convergence speed from being too fast. Secondly, the inertia weight and sine vector can increase the probability of jumping out of the local optimal solution. Finally, the Archimedean spiral can reduce the risk of losing the optimal solution. A representative benchmark function is selected to test the convergence rate of CCWOA algorithm and the optimization performance of jumping out of local optimum. Compared with the representative algorithms PFP and GAP, the optimization effect of CCWOA is almost consistent with the above two algorithms, and even exceeds 4%-6% in numerical value. The advantage of CCWOA is that it has lower algorithm complexity, which has a good advantage when the network computing resources are fixed. In addition, the optimization effect of CCWOA is higher than that of WOA, which lays a good foundation for further application of swarm intelligence optimization algorithm in network resource allocation.