2015
DOI: 10.1016/j.artint.2015.06.004
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Differential evolution for noisy multiobjective optimization

Abstract: We propose an extension of multiobjective optimization realized with the differential evolution algorithm to handle the effect of noise in objective functions. The proposed extension offers three merits with respect to its traditional counterpart. First, an adaptive selection of the sample size for the periodic fitness evaluation of a trial solution based on the fitness variance in its local neighborhood is proposed. This avoids the computational complexity associated with the unnecessary reevaluation of quali… Show more

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Cited by 29 publications
(4 citation statements)
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“…L-SAHDE, Tanabe and Fukunaga 2014 ) achieved great successes in wide scale competitions among metaheuristics, for numerical problems related with COVID-19 almost solely the basic DE and PSO algorithms were applied. It seems that successful noisy multi-objective variants (Rakshit and Konar 2015 ) are also ignored. The only explanation seems to be simplicity, popularity and availability of the codes implemented in various languages or computing platforms.…”
Section: Methodological Aspects Of Differential Evolution and Particle Swarm Optimization Applicationsmentioning
confidence: 99%
“…L-SAHDE, Tanabe and Fukunaga 2014 ) achieved great successes in wide scale competitions among metaheuristics, for numerical problems related with COVID-19 almost solely the basic DE and PSO algorithms were applied. It seems that successful noisy multi-objective variants (Rakshit and Konar 2015 ) are also ignored. The only explanation seems to be simplicity, popularity and availability of the codes implemented in various languages or computing platforms.…”
Section: Methodological Aspects Of Differential Evolution and Particle Swarm Optimization Applicationsmentioning
confidence: 99%
“…To emulate the measurement noises in the RVS, the objective functions are contaminated with noise samples taken from the representative Gaussian distributions. Mathematically, the noisy level of the kth objective function with a trial solution 𝑥 is defined by [34] 𝑦 𝑘 (𝑥) = 𝑓 𝑘 (𝑥) + 𝛿 𝑘 𝛿 * , 𝛿~𝑁(𝜇, 𝜎 2 ) (17…”
Section: A Noise Modelsmentioning
confidence: 99%
“…To forestall misunderstanding, it is to be noted that, in answering this question, we do not have to solve a multi-objective optimization problem, with speed and accuracy as objectives (Deb, 2001;Qian, Yu, & Zhou, 2013;Rakshit & Konar, 2015). As mentioned repeatedly, we are thinking of striking the best balance between speed and accuracy as our objective.…”
Section: The Hk Model and Beyondmentioning
confidence: 99%