Web Service Composition Optimization with the Improved Fireworks Algorithm

Abstract

Even though the number of services is increasing, a single service can just complete simple tasks. In the face of complex tasks, we require composite multiple services to complete them. For the purpose of improving the efficiency of web service composition, we propose a service composition approach based on an improved fireworks algorithm (FWA++). First, we use the strategy of random selection to keep N − 1 individuals for the next generation, and the purpose is to speed up the convergence speed of the FWA++ and enhance the search ability. Second, we randomize the total number of sparks and maximum amplitude of sparks for each generation. In this way, the search ability and the ability to jump out of the local optimal solution are dynamically balanced throughout the execution of the algorithm. Our experimental results show that compared with other existing approaches, the approach proposed in this paper is more efficient and stable.

Assessment of college students’ mental health status based on temporal perception and hybrid clustering algorithm under the impact of public health events

“…The Fireworks Algorithm (FWA) is a novel form of swarm optimization technique that simulates the search process of neighborhood space during the explosion of fireworks. It is capable of balancing global and local search, and has the benefits of simple implementation, straightforward operation, and powerful search capabilities ( Jiang et al, 2022 ; Li & Tan, 2019 ; Xue, 2020 ). The Fireworks Algorithm, inspired by the explosion of fireworks in the night sky, has found promising applications in optimization problems.…”

Application of BPNN Optimized by Fireworks Algorithm on Structural Bearing Capacity Analysis of Transmission Tower under Icing Conditions

“…A novel hybrid optimization algorithm named GPOFWA integrates political optimizer (PO) with FWA to solve numerical and engineering optimization problems [39][40][41]. Multimodal multiobjective optimization problems (MMOPs) have received increasing attention, which can be solved by FWA [42][43][44][45]. Furthermore, FWA has been applied in many fields since it was proposed, and has good performance in prediction accuracy and local optimization prevention [46][47][48][49][50].…”