Integration of Ordinal Optimization with Ant Lion Optimization for Solving the Computationally Expensive Simulation Optimization Problems

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.

Special Issue on Interdisciplinary Artificial Intelligence: Methods and Applications of Nature-Inspired Computing

“…Furthermore, Horng and Lee [5] propose an ordinal optimization method in combination with ant lion optimization to solve computationally expensive simulation optimization problems. The key feature of the proposal is to tackle the computationally extensive fitness functions, operate with intractable objective functions, and to work without sensitivity information.…”

Review of Nature Inspired Metaheuristic Algorithm Selection for Combinatorial t-Way Testing

“…Initially, ALOA is presented to optimize the location and sizing of distributed generation in radial distribution systems. However, some research accepted the ALOA method in some areas of optimization such as data clustering problem [142], economic dispatch optimization problems [143], simulation optimization problems [144], and so on. At the present time of this writing, no single t-way testing strategy accepted ALOA technique for combinatorial optimization problem.…”

On the Utilization of an Ensemble of Meta-Heuristics for Simulating Energy Consumption in Buildings

“…Besides, the exploitation efficiency of this algorithm is highlighted by the time-varying boundary shrinking mechanism and elitism. The major advantages of this algorithm are as follows: ease of implementation, high precision, avoidance of local optima, and reduced need for parameter adjustment (Horng & Lee, 2021).…”