Evolutionary optimisation of noisy multi-objective problems using confidence-based dynamic resampling

• Published in: European journal of operational research Vol. 204; no. 3; pp. 533 - 544
• Main Authors: Syberfeldt, Anna, Ng, Amos, John, Robert I., Moore, Philip
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.08.2010; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Automobile industry; Convergence; Decision theory. Utility theory; Evolutionary computations; Evolutionary computations Multi-objective optimisation Noise Simulation; Exact sciences and technology; Manufacturing; Multi-objective optimisation; Noise; Operational research and scientific management; Operational research. Management science; Optimization algorithms; Simulation; Studies; Technology; Teknik; Evolutionary computations; Simulation; Noise; Multi-objective optimisation; Evolutionary algorithm; Multiobjective programming; Machining; Process selection; Iterative method; Confidence interval; Manufacturing industries; Genetic algorithm; Automobile industry
• ISSN: 0377-2217; 1872-6860; 1872-6860
• DOI: 10.1016/j.ejor.2009.11.003

Many real-world optimisation problems approached by evolutionary algorithms are subject to noise. When noise is present, the evolutionary selection process may become unstable and the convergence of the optimisation adversely affected. In this paper, we present a new technique that efficiently deals with noise in multi-objective optimisation. This technique aims at preventing the propagation of inferior solutions in the evolutionary selection due to noisy objective values. This is done by using an iterative resampling procedure that reduces the noise until the likelihood of selecting the correct solution reaches a given confidence level. To achieve an efficient utilisation of resources, the number of samples used per solution varies based on the amount of noise in the present area of the search space. The proposed algorithm is evaluated on the ZDT benchmark problems and two complex real-world problems of manufacturing optimisation. The first real-world problem concerns the optimisation of engine component manufacturing in aviation industry, while the second real-world problem concerns the optimisation of a camshaft machining line in automotive industry. The results from the optimisations indicate that the proposed technique is successful in reducing noise, and it competes successfully with other noise handling techniques.