Please use this identifier to cite or link to this item: https://doi.org/10.1162/EVCO_a_00066
Title: Multi-objective optimization with estimation of distribution algorithm in a noisy environment
Authors: Shim, V.A.
Tan, K.C. 
Chia, J.Y.
Al Mamun, A. 
Keywords: Estimation of distribution algorithm
Evolutionary algorithm
Multi-objective optimization
Noisy fitness function
Particle swarm optimization
Restricted Boltzmann machine
Issue Date: Mar-2013
Source: Shim, V.A.,Tan, K.C.,Chia, J.Y.,Al Mamun, A. (2013-03). Multi-objective optimization with estimation of distribution algorithm in a noisy environment. Evolutionary Computation 21 (1) : 149-177. ScholarBank@NUS Repository. https://doi.org/10.1162/EVCO_a_00066
Abstract: Many real-world optimization problems are subjected to uncertainties that may be characterized by the presence of noise in the objective functions. The estimation of distribution algorithm (EDA), which models the global distribution of the population for searching tasks, is one of the evolutionary computation techniques that deals with noisy information. This paper studies the potential of EDAs; particularly an EDAbased on restricted Boltzmann machines that handles multi-objective optimization problems in a noisy environment. Noise is introduced to the objective functions in the form of a Gaussian distribution. In order to reduce the detrimental effect of noise, a likelihood correction feature is proposed to tune the marginal probability distribution of each decision variable. The EDA is subsequently hybridized with a particle swarm optimization algorithm in a discrete domain to improve its search ability. The effectiveness of the proposed algorithm is examined via eight benchmark instances with different characteristics and shapes of the Pareto optimal front. The scalability, hybridization, and computational time are rigorously studied. Comparative studies show that the proposed approach outperforms other state of the art algorithms. © 2013 by the Massachusetts Institute of Technology.
Source Title: Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/56720
ISSN: 10636560
DOI: 10.1162/EVCO_a_00066
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