Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

Resumé

Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance of all aforementioned algorithms is assessed and compared using
performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical findings of this comparative study show that based on the performance indicators, three algorithms, eMOEA, eNSGAII and NSGAII outperform the other algorithms and provide high
quality solution sets in an appropriate time.
OriginalsprogEngelsk
Titel2015 IEEE Congress on Evolutionary Computation
ForlagIEEE
Publikationsdato2015
Sider1909-1917
DOI
StatusUdgivet - 2015
BegivenhedIEEE Congress on Evolutionary Computation: CEC 2015 - Sendai International Center, Sendai, Japan
Varighed: 25. maj 201528. maj 2015

Konference

KonferenceIEEE Congress on Evolutionary Computation
LokationSendai International Center
LandJapan
BySendai
Periode25/05/201528/05/2015

Fingeraftryk

Climate control
Greenhouses
Multiobjective optimization
Evolutionary algorithms
Decision making

Citer dette

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title = "Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem",
abstract = "Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance of all aforementioned algorithms is assessed and compared usingperformance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical findings of this comparative study show that based on the performance indicators, three algorithms, eMOEA, eNSGAII and NSGAII outperform the other algorithms and provide highquality solution sets in an appropriate time.",
keywords = "Multi-objective Optimization, Multi-objective Evolutionary Algorithms, Control systems, comparative Study, Performance Analysis",
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Ghoreishi, N, Sørensen, JC & Jørgensen, BN 2015, Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem. i 2015 IEEE Congress on Evolutionary Computation. IEEE, s. 1909-1917, IEEE Congress on Evolutionary Computation, Sendai, Japan, 25/05/2015. https://doi.org/10.1109/CEC.2015.7257119

Comparative Study of Evolutionary Multi-objective Optimization Algorithms for a Non-linear Greenhouse Climate Control Problem. / Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard.

2015 IEEE Congress on Evolutionary Computation. IEEE, 2015. s. 1909-1917.

Publikation: Bidrag til bog/antologi/rapport/konference-proceedingKonferencebidrag i proceedingsForskningpeer review

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