Experiments with repeating weighted boosting search for optimization in signal processing applications.
Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations, and optimization methods that require no gradient and can achieve a global optimal solution are highly desired to tackle these difficult problems. The paper proposes a guided global search optimization technique, referred to as the repeated weighted boosting search. The proposed optimization algorithm is extremely simple and easy to implement, involving a minimum programming effort. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used guided global search techniques, known as the genetic algorithm and adaptive simulated annealing, in terms of the requirements for algorithmic parameter tuning. The effectiveness of the proposed algorithm as a global optimizer are investigated through several application examples.
Chen
Sheng
S
School of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.
Wang
Xunxian
X
Harris
Chris J
CJ
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003920:Diabetes Mellitus / Q000145:classification / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes