Quantum and Condor-Based Brainstorming Optimization Algorithm for NOx Prediction

Abstract This paper proposed a quantum bald eagle brainstorm (QBBSO) based on initialization and combined with the optimization algorithm in light of original Brain Storm Optimization (BSO) algorithm’s strong local search ability, which would result optimization, poor effect, difficult development. To accomplish randomness number increase population, we first modified method brainstorming introduced idea code, then translated binary numbers 0 1 to decimal number. achieve best outcome, step size formula was employed for global selection, search, final selection using vulture method. The BSO optimized attract more individuals. versions were compared common benchmark function test. findings demonstrated that QBBSO had greater capacity faster convergence speed. research also applied long-term short-term memory network (LSTM) forecast NOx concentration boiler, further demonstrating superiority real-world settings.