Abstract
The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation. Although computing devices continue to increase in power, the complexity of evaluating a solution continues to keep pace. Ordinal optimization (OO) is developed as an efficient framework for solving CESOP. In this work, a heuristic algorithm integrating ordinal optimization with ant lion optimization (OALO) is proposed to solve the CESOP within a short period of time. The OALO algorithm comprises three parts: approximation model, global exploration, and local exploitation. Firstly, the multivariate adaptive regression splines (MARS) is adopted as a fitness estimation of a design. Next, a reformed ant lion optimization (RALO) is proposed to find N exceptional designs from the solution space. Finally, a ranking and selection procedure is used to decide a quasi-optimal design from the N exceptional designs. The OALO algorithm is applied to optimal queuing design in a communication system, which is formulated as a CESOP. The OALO algorithm is compared with three competing approaches. Test results reveal that the OALO algorithm identifies solutions with better solution quality and better computing efficiency than three competing algorithms.
Highlights
The optimization of several practical large-scale engineering systems is computationally expensive
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The multivariate adaptive regression splines (MARS) was trained by randomly sampling M = 384 designs
Summary
The optimization of several practical large-scale engineering systems is computationally expensive. The computationally expensive simulation optimization problems (CESOP) are concerned about the limited budget being effectively allocated to meet a stochastic objective function which required running computationally expensive simulation [1,2]. Several methods are adopted to resolve CESOP, such as the gradient descent approaches [3], metaheuristic algorithms [4], evolutionary algorithms (EA) [5], and swarm intelligence (SI) [6]. The gradient descent approaches [3], including steepest descent method and conjugated gradient approach, maybe get stuck in a local optimum and fail to obtain the global optimum The metaheuristic algorithms [4], including Tabu search (TS) and simulated annealing (SA), are developed to find the global optimum. EA [5] are stochastic search optimization techniques inspired by the biological principle of evolution, survival of the fittest. SI methods have been applied in different domains [11], the identification of barriers and limitations have been found [12]
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