Open AccessFireworks Algorithm Based on Opposition-Based Learning and Quantum Optimization Strategy
Author(s) -
Haoran Jin,
Hao Li,
Yanyan Ma,
Xi Fang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1550/2/022013
Subject(s) - fireworks , bottleneck , computer science , mathematical optimization , algorithm , optimization algorithm , opposition (politics) , convergence (economics) , mathematics , chemistry , organic chemistry , politics , political science , law , economics , embedded system , economic growth
Aiming at the bottleneck of optimization performance and slow convergence speed of fireworks algorithm, a new fireworks algorithm (FWA) is proposed by integrating Opposition-Based Learning and Quantum Optimization strategy (OQFWA). The new algorithm optimizes the original fireworks algorithm in the aspects of the selection of sparks and the local improvement of the optimal individuals, which effectively improves the convergence accuracy and speed of the algorithm. The simulation results of extremum optimization of typical test functions with different characteristics show that the fireworks algorithm, which integrates the Opposition-Based Learning and Quantum Optimization strategy, and has good optimization performance.