An Improved Whale Optimization Algorithm Based on Aggregation Potential Energy for QoS-Driven Web Service Composition

Abstract

With more complex user needs, the web service composition (WSC) has become a key research area in the current circumstance. The swarm intelligence algorithms are proved to solve this problem well. However, no researchers have applied the whale optimization algorithm (WOA) to the WSC problem. In this work, we propose a logarithmic energy whale optimization algorithm (LEWOA) based on aggregation potential energy and logarithmic convergence factor to solve this problem. Firstly, the improved algorithm uses a chaotic strategy to enhance the initial swarm diversity. After that, a logarithmic convergence factor is applied to obtain the nonlinear search step. Furthermore, aggregation potential energy as the spatial evaluation is employed in the swarm intelligence algorithms for the first time. Finally, the aggregation potential energy is used to dynamically adjust the nonlinear weight, which improves the search efficiency and prevents the algorithm from falling into local optimization. The experimental results of the benchmark functions show that the LEWOA has better optimization ability and convergence speed than other swarm intelligence algorithms. In the second experiment of the WSC optimization, the effectiveness and superiority of the LEWOA are verified.