An Improved Fireworks Algorithm for Large-Scale Global Optimization Problems

Abstract

Due to the complex structure of modern systems, large-scale optimization has become the hot spot of practical engineering problems. An improved fireworks algorithm (FWA) is proposed to obtain the global optimal solution of large-scale optimization problems in a fast manner. To promote the ability to jump out of the local optimal solution, a differential sparks generation mechanism and a novel reinitialization mechanism are developed. To speed up the convergence, a dual-channel selection mechanism is proposed, in which an exclusive channel is given for differential sparks. The combination of differential sparks generation and dual-channel selection promotes the usage of known information, balances the diversity and convergence of the fireworks. Typical test suites and unmanned aerial vehicle (UAV) path planning are used to test the effectiveness and efficiency of the proposed algorithm. Simulation results demonstrate that the proposed algorithm can search for the global optimal solution in a large variable space and can obtain a short-distance path for the UAV.