Experiments with repeating weighted boosting search for optimization signal processing applications

IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society(2005)

引用 85|浏览5
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.
proposed optimization algorithm,genetic algorithm,boosting,optimization signal processing application,optimization,optimization method,global optimizer,proposed algorithm,index terms—adaptive simulated annealing,stochastic algorithm.,multistart,optimization problem,global search optimization technique,evolu- tionary computation,global search,global optimal solution,global search capability,global search technique,gradient method,adaptive simulated annealing,stochastic programming,indexing terms,global optimization,signal processing,evolutionary computation,cost function
AI 理解论文