Survey of multi objective evolutionary algorithms

Vachhani, Vimal L.; Dabhi, Vipul K.; Prajapati, Harshadkumar B.

doi:10.1109/iccpct.2015.7159422

Cited by 32 publications

(20 citation statements)

References 23 publications

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“…Multi objective optimization (MOO) has more complex implementations and constraints compared to single objective optimization. There are many MOO algorithms which has different types of approach, and most of them require at least two conflicting objective functions to operate [18,19].…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

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Computational BIM for Building Envelope Sustainability Optimization

et al. 2019

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Building envelope plays an important role to protect a building from external climatic factors while providing a comfortable indoor environment. However, the choices of construction materials, opening sizes, and glazing types for optimized sustainability performance require discrete analyses and decision-making processes. Thereby this study explores the use of computational building information modelling (BIM) to automate the process of design decision-making for building envelope sustainability optimization. A BIM tool (Revit), a visual programming tool (Dynamo) and multi objective optimization algorithm were integrated to create a computational BIM-based optimization model for building envelope overall thermal transfer value (OTTV) and construction cost. The proposed model was validated through a test case; the results showed that the optimized design achieved 44.78% reduction in OTTV but 19.64% increment in construction cost compared to the original design. The newly developed computational BIM optimization model can improve the level of automation in design process for sustainability.

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Section: Multi Objective Optimizationmentioning

confidence: 99%

“…The concept of elitism is used in MOO to overcome the problem. Elitism is the mechanism used to preserve good solution in every generation, thus giving better performance than nonelitism algorithm [19]. An elitism MOO algorithm called Non-Dominated Sorting Genetic Algorithm II (NSGA-II) was developed by Deb et al [20].…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

Computational BIM for Building Envelope Sustainability Optimization

et al. 2019

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“…This, however, could be tackled, for example, with game-theoretic approaches [57,58], including a hybrid approach with evolutionary algorithms [56]. In order to enhance our optimization process (e.g., stability [56] and techniques aiming at big data [59]), in the future we would like to perform experiments with other multi-objective optimization approaches [60][61][62][63].…”

Section: Pareto-optimal Frontmentioning

confidence: 99%

MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Nascimento¹,

Sadok²

2018

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Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods.

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“…The crowding distance based method does not select diverse set of solutions from the last Pareto front(f L ) in some of the cases. For example, assume that (1) there are six non-dominated solutions available in the last Pareto front as shown in Figure 1 [17]. And (2) The SPEA [10] algorithm uses agglomerative hierarchical average linkage based clustering method for maintaining solutions diversity in the Pareto front.…”

Section: Nsga-ii Algorithmmentioning

confidence: 99%

Improving NSGA-II for solving multi objective function optimization problems

Vachhani

Dabhi

Prajapati

2016

2016 International Conference on Computer Communication and Informatics (ICCCI)

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It is important for any MOEA (Multi Objective Evolutionary Algorithm) to improve convergence and diversity of solutions of Pareto front, which is obtained at the termination of MOEA. There are many MOEA available in the literature: NSGA-II, SPEA, SPEA2, PESAII and IBEA. This paper aims at improving solutions diversity of Pareto front of a well known multi-objective optimization algorithm, NSGA-II. The standard NSGA-II algorithm uses crowding distance based method for maintaining solutions diversity. The limitation of crowding distance based method is that it selects two nearer solutions from the Pareto front for the mating. The SPEA algorithm uses agglomerative hierarchical average linkage based clustering method for maintaining solutions diversity. The method sometimes may not preserve extreme solutions in Pareto front. In this paper, we propose a new diversity method based on agglomerative hierarchical clustering with extreme solutions preservation. The proposed method is tested on standard test problems of MOEA. It is observed that the proposed method gives good solution diversity on two objectives test problems compared to the existing diversity method of NSGA-II.

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Survey of multi objective evolutionary algorithms

Cited by 32 publications

References 23 publications

Computational BIM for Building Envelope Sustainability Optimization

Computational BIM for Building Envelope Sustainability Optimization

MODC: A Pareto-Optimal Optimization Approach for Network Traffic Classification Based on the Divide and Conquer Strategy

Improving NSGA-II for solving multi objective function optimization problems

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