دانلود رایگان مقاله یک رویکرد مدل سازی بهینه برای بهینه سازی آنلاین با جستجوگر

abstract

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.

6. Conclusion

Although tremendous research effort has been spent on online optimization over the past two decades, it is still widely believed that the state of the art is yet far from reaching maturity [30]. In particular, there is no agreed groundwork of methods and tools for comprehensive algorithm analysis in online optimization, not to mention in online optimization with lookahead. This paper aimed at contributing towards the elimination of this deficiency. We first elaborated a clear definition of lookahead in optimization: lookahead is a mechanism of information release that specifies the difference in the process of information disclosure as compared to a reference optimization problem (instance revelation rule substitution) and that might impose a set of constraints differing from the set of constraints in the reference online optimization problem upon the processing of the input elements (rule set substitution).