Novel search scheme for multi-objective evolutionary algorithms to obtain well-approximated and widely spread Pareto solutions

Hiwa, Satoru; Nishioka, Masashi; Hiroyasu, Tomoyuki; Miki, Mitsunori

doi:10.1016/j.swevo.2015.01.004

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…A conceptual multiobjective evolutionary algorithm is suggested that uses the group features of the intermediate Pareto-set estimate to obtain a consistent final estimate. [24] suggested a design mode analysis of Pareto solution sets to help humans make decisions. PCA was used to extract the design mode of the Pareto solution set.…”

Section: Pareto Setsmentioning

confidence: 99%

MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Durairaj

Ramaswamy

2023

Cluster Comput

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Virtual Machine Placement (VMP) is crucial in a cloud data center(CDC). It is a critical step carried out as part of the Virtual Machine (VM) placement to allocate the best Physical Machine (PM) to host the VMs. The efficacy of the virtual machine placement strategy has a considerable impact on cloud computing efficiency. The ineffectiveness of the VMP approach has a major negative impact on the CDC.Virtualization facilitated VM migration has met the ever-increasing demands of dynamic workload by transferring VMs inside CDC. Many resource management goals, including power efficiency, load balancing, fault tolerance, and system maintenance, are aided VM placement. As a result, VMP needs to assess characteristics that may impact placement performance and energy efficiency. Most past research has concentrated solely on reducing energy consumption while ignoring SLA (service level agreement) breaches, enhancing the resource usage of PMs, and ignoring the over-commitment issue.

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Section: Pareto Setsmentioning

confidence: 99%

MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

Durairaj

Ramaswamy

2023

Cluster Comput

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“…Thus, the goal of multi-objective problems (MOP)s is to find the Pareto front and present such set of multiple alternative solutions to the decision maker for consideration. Over the past decades, a variety of multi-objective evolutionary algorithms (MOEA)s have been developed to solve MOPs (Hiwa et al 2015). MOEA is capable of returning a set of Pareto-optimal solutions in just a single run of the algorithm.…”

Section: Multi-objective Evolutionary Algorithmsmentioning

confidence: 99%

A multi-objective evolutionary approach to Pareto-optimal model trees

Czajkowski

Krȩtowski

2018

Soft Comput

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This paper discusses the multi-objective evolutionary approach to induction of model trees. The model tree is a particular case of a decision tree designed to solve regression problems. Although the decision tree induction is inherently a multiobjective task, most of the conventional learning algorithms can only deal with a single objective that may possibly aggregate multiple objectives. The goal of this paper is to demonstrate how a set of non-dominated model trees can be obtained using the Global Model Tree (GMT) system. The GMT framework can be used for the evolutionary induction of different types of decision trees, including univariate, oblique or mixed; regression and model trees. Proposed Pareto approach for GMT allows the decision maker to select desired output model according to his preferences on the conflicting objectives. Performed study covers the regression trees and the model trees with two or three objectives that relate to the tree error and the tree comprehensibility. Experimental evaluation discusses the importance of multi-objective components like crowding function and archive elitist selection, using real-life datasets. Finally, the presented multi-objective GMT solution is confronted with competitive regression and model tree inducers.

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“…An enhanced GA method is introduced and utilized to solve this problem. [29][30][31][32] However, another difficulty is shown in this situation. The exact definition of the upper and lower bounds cannot be given by the binary relationship of preference components.…”

Section: Introductionmentioning

confidence: 99%

An enhanced genetic algorithm–based multi-objective design optimization strategy

Yuan

Wang

2018

Advances in Mechanical Engineering

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In this study, an enhanced genetic algorithm is proposed to solve multi-objective design and optimization problems in practical engineering. In the given approach, designers choose available design results from the given samples first. These samples are reordered according to their mutual relationships. After that, designers choose an exact ratio of conformity as available field. Furthermore, more weight information can be obtained through finding the minimum value of the norm of unconformity and satisfactory samples. These samples can be used to reflect the preference chosen for Pareto design solutions. A structure design problem of speed increaser used in wind turbine generator systems is solved to show the application of the given design strategy.

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Novel search scheme for multi-objective evolutionary algorithms to obtain well-approximated and widely spread Pareto solutions

Cited by 11 publications

References 25 publications

MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

MOM-VMP: multi-objective mayfly optimization algorithm for VM placement supported by principal component analysis (PCA) in cloud data center

A multi-objective evolutionary approach to Pareto-optimal model trees

An enhanced genetic algorithm–based multi-objective design optimization strategy

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