MO-COMPASS: a fast convergent search algorithm for multi-objective discrete optimization via simulation

Haobin Li, Loo Hay Lee, Ek Peng Chew and Peter Lendermann

IISE Transactions, 2015, vol. 47, issue 11, 1153-1169

Abstract: Discrete Optimization via Simulation (DOvS) has drawn considerable attention from both simulation researchers and industry practitioners, due to its wide application and significant effects. In fact, DOvS usually implies the need to solve large-scale problems, making the efficiency a key factor when designing search algorithms. In this research work, MO-COMPASS (Multi-Objective Convergent Optimization via Most-Promising-Area Stochastic Search) is developed, as an extension of the single-objective COMPASS, for solving DOvS problems with two or more objectives by taking into consideration the Pareto optimality and the probability of correct selection. The algorithm is proven to be locally convergent, and numerical experiments have been carried out to show its ability to achieve high convergence rate.

Date: 2015
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DOI: 10.1080/0740817X.2015.1005778

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