2002
DOI: 10.1109/tsmcb.2002.804372
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Multiobjective evolutionary algorithm for the optimization of noisy combustion processes

Abstract: Evolutionary Algorithms have been applied to single and multiple objectives optimization problems, with a strong emphasis on problems, solved through numerical simulations. However in several engineering problems, there is limited availability of suitable models and there is need for optimization of realistic or experimental configurations. The multiobjective optimization of an experimental setup is addressed in this work. Experimental setups present a number of challenges to any optimization technique includi… Show more

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Cited by 135 publications
(66 citation statements)
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“…Furthermore, MOSFP has the ability more than other algorithms in solving the problems with more than two objective functions, such as finding the optimal quality of services (QoS) of wireless, mobile and telecommunication networks [64][65][66], finding the optimal length and spacing of Yagi-Uda antenna design [12], engineering applications [63], finding the optimal products distribution through oil pipeline networks [19], and finding the optimal task allocation of stationary gas turbine [16].…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, MOSFP has the ability more than other algorithms in solving the problems with more than two objective functions, such as finding the optimal quality of services (QoS) of wireless, mobile and telecommunication networks [64][65][66], finding the optimal length and spacing of Yagi-Uda antenna design [12], engineering applications [63], finding the optimal products distribution through oil pipeline networks [19], and finding the optimal task allocation of stationary gas turbine [16].…”
Section: Discussionmentioning
confidence: 99%
“…• Type B: is the noisy objective function that probabilistic noise values z = {z 1 , z 2 ,…,z m } vary the original objective function values f(x) = {f 1 (x), f 2 (x),…,f m (x)} after the calculation of the original objective functions f (Hughes, 2001a(Hughes, , 2001bBuche et al, 2002;Babbar et al, 2003;Basseur and Zitzler, 2006;Bui et al, 2005;Park and Ryu, 2011). This type of noisy objective function is formulated by ( ) ( ) ( 1, 2, , ).…”
Section: Three Types Of Noisy Objective Functionsmentioning
confidence: 99%
“…This is because, several unknown factors not considered as decision variables affect the objective values. For these noisy multi-objective optimisation problems (NMOPs), several algorithms optimising solutions based on the estimation of the true objective function values by considering the influence of noise have been studied so far (Babbar et al, 2003;Teich, 2001;Basseur and Zitzler, 2006;Bui et al, 2005;Buche et al, 2002;Goh and Tan, 2009). However, in conventional approaches, the noise level of each solution cannot be considered in the decision-making process when a decision maker tries to select the final solution from the obtained solutions.…”
Section: Introductionmentioning
confidence: 99%
“…When a system is subject to noise, repeated evaluations of the same solution over time will result in different objective values. This effect is exemplified in Figure 1 (adopted from Büche et al, 2002), where the objective value returned from the evaluation function f has an error that is governed by a normal distribution and therefore varies from time to time. A noisy evaluation function is also illustrated in Figure 2 (adopted from Pietro et al 2004).…”
Section: Noisy Optimisation Problemsmentioning
confidence: 99%
“…In the technique of dominance-dependent lifetime, each solution is assigned a maximal lifetime based on the number of solutions it dominates (Büche et al, 2002). A solution dominating a large number of solutions is assigned a short lifetime, and vice verse.…”
Section: Domination-dependent Lifetimementioning
confidence: 99%