Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios

Tanabe, Ryoji; Ishibuchi, Hisao; Oyama, Akira

doi:10.1109/access.2017.2751071

Cited by 87 publications

(43 citation statements)

References 91 publications

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“…In some studies [34]- [36], an unbounded external archive was used to evaluate existing EMO algorithms. In Bringmann et al [34], it was demonstrated that the performance of EMO algorithms was improved by solution selection where a pre-specified number of solutions were selected from stored non-dominated solutions.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

“…By selecting the same number of non-dominated solutions from the archive as the final result of each algorithm, different EMO algorithms with different population size were fairly compared. In Tanabe et al [36], EMO algorithms were compared in two performance comparison scenarios: One is based on the final population and the other is based on the selected solutions from the archive.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

“…Whereas an unbounded external archive was used in [34]- [36] for continuous multi-objective optimization, its purpose was for performance evaluation/comparison of existing EMO algorithms. In this paper, we propose the use of an unbounded external archive for the design of new EMO algorithms.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

“…In this manner, a pre-specified number of solutions are selected. This solution selection is similar to the method of Tanabe et al [36] where m extreme non-dominated solutions are first selected for an m-objective problem. Each extreme solution has the best objective value for one of the m-objective functions.…”

Section: Proposed Emo Frameworkmentioning

confidence: 99%

See 3 more Smart Citations

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Ishibuchi¹,

Pang²,

Shang³

2020

Preprint

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This paper proposes a new framework for the design of evolutionary multi-objective optimization (EMO) algorithms. The main characteristic feature of the proposed framework is that the optimization result of an EMO algorithm is not the final population but a subset of the examined solutions during its execution. As a post-processing procedure, a pre-specified number of solutions are selected from an unbounded external archive where all the examined solutions are stored. In the proposed framework, the final population does not have to be a good solution set. The point of the algorithm design is to examine a wide variety of solutions over the entire Pareto front and to select well-distributed solutions from the archive. In this paper, first we explain difficulties in the design of EMO algorithms in the existing two frameworks: non-elitist and elitist. Next, we propose the new framework of EMO algorithms. Then we demonstrate advantages of the proposed framework over the existing ones through computational experiments. Finally we suggest some interesting and promising future research topics.

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Section: Proposed Emo Frameworkmentioning

confidence: 99%

Section: Proposed Emo Frameworkmentioning

confidence: 99%

Section: Proposed Emo Frameworkmentioning

confidence: 99%

Section: Proposed Emo Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Ishibuchi¹,

Pang²,

Shang³

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the past decade, NSGA-III has been introduced as an improved version of NSGA-II in which the crowding distance-based method was replaced with the reference vectors-based niching method. On the other hand, CMOPSO and RVEA are considered recent development evolutionary algorithms which possess competitive advantages over the NSGA-II when tested on benchmark problems [28]. The coding was performed through PlatEMO in MATLAB [29].…”

Section: Multi-objective Optimization Formulationmentioning

confidence: 99%

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Tey

Shak

2019

SN Appl. Sci.

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Formula 1 is considered an important motorsport event that has driven the development of cutting-edge automotive technology. Similarly, virtual formula competition is introduced at the university level to harness technological advances for automotive development among universities. Performance and fuel efficiency have always been two conflicting objectives in determining the optimal powertrain design. In this paper, state-of-the-art multi-objective evolutionary algorithms (MOEAs) are employed to optimize the vehicle powertrain design based on competing event and the ontrack performance through software in-the-loop optimization approach. Unlike conventional powertrain optimization approach where the powertrain design is optimized again standard urban driving profiles to achieve optimum driving performance and fuel consumption which is not suitable to be employed to optimize race car powertrain design. The powertrain design is optimized and benchmarked in multiple dynamic test events i.e. Acceleration, Autocross and Endurance. Each event measures different performance parameters (i.e. lap time and fuel consumption) A total of nine design parameters are optimized in this study. Three design parameters (employed to formulate the progressive gear calculation of six gear ratios), five gear shifting rpm (revolutions per minute) and an aerodynamic downforce scale value (for front and rear downforce). Two stages of optimization solution are proposed to handle such complicated design requirement. NSGA-II was identified as the most efficient algorithms among the MOEAs employed in this study; especially in terms of computational time, Pareto distribution and Pareto design. Overall, the optimized powertrain design showed significant improvement in all events i.e. energy efficiency was increased by 13.7% for the endurance event while lap time was reduced by 0.7 and 1.2% in the acceleration and autocross events, respectively.

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Investigation of a Simple Distance Based Ranking Metric for Decomposition-Based Multi/Many-Objective Evolutionary Algorithms

Singh

Bhattacharjee

Ray

et al. 2018

AI 2018: Advances in Artificial Intelligence

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Benchmarking Multi- and Many-Objective Evolutionary Algorithms Under Two Optimization Scenarios

Cited by 87 publications

References 91 publications

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

A New Framework of Evolutionary Multi-Objective Algorithms with an Unbounded External Archive

Multi-objective optimization of virtual formula powertrain design for enhanced performance and fuel efficiency

Investigation of a Simple Distance Based Ranking Metric for Decomposition-Based Multi/Many-Objective Evolutionary Algorithms

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