A Hybrid Scalarization and Adaptive ε-Ranking Strategy for Many-Objective Optimization

Aguirre, Hernán; Tanaka, Kiyoshi

doi:10.1007/978-3-642-15871-1_2

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…This can be also suggested that the latest research refers the Miettinen’s work for the introduction of the scalarization methods (Schlünz et al , 2016). The resent works for the scalarization method include: the modification of the conventional method to meet to an application (Schlünz et al , 2016); the combination of the scalarization methods (Luque et al , 2012); the application to the evolutionary method to improve the ranking strategy which evaluates pareto optimal solutions (Aguirre and Tanaka, 2010); and the consideration of a comprehensive formulation of the methods Eichfelder, 2009). …”

Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

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Selection guide of multi-objective optimization for ergonomic design

Muramatsu

Katô

2019

JEDT

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Purpose The purpose of this paper is to propose the selection guide of the multi-objective optimization methods for the ergonomic design. The proposed guide enables designers to select an appropriate method for optimizing the human characteristics composed of the engineering characteristics (e.g. users’ height, weight and muscular strength) and the physiological characteristics (e.g. brain wave, pulse-beat and myoelectric signal) in the trade-off relationships. Design/methodology/approach This paper focuses on the types of the relationships between engineering or physiological characteristics and their psychological characteristics (e.g. comfort and usability). Using these relationships and the characteristics of the multi-objective optimization methods, this paper classified them and constructed a flow chart for selecting them. Findings This paper applied the proposed selection guide to a geometric design of a comfortable seat and confirmed its applicability. The selected multi-objective optimization method optimized the contact area of seat back (engineering characteristic associated with the comfortable fit of the seat backrest) and the blood flow volume (physiological characteristic associated with the numbness in the lower limb) on the basis of each design intent such as a deep-vein thrombosis after long flight. Originality/value Because of the lack of the selection guide of the multi-objective optimization methods, an inappropriate method is often applied in industry. This paper proposed the selection guide applied in the ergonomic design having a lot of the multi-objective optimization problem.

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Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

“…the application to the evolutionary method to improve the ranking strategy which evaluates pareto optimal solutions (Aguirre and Tanaka, 2010); and…”

Section: Multi-objective Optimization Methodsmentioning

confidence: 99%

Selection guide of multi-objective optimization for ergonomic design

Muramatsu

Katô

2019

JEDT

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“…In (Hernan & Kiyoshi, 2010); (Aguirre & Tanaka, 2011), a hybrid strategy based on twostage search process is developed for solving many-objective optimization. The first stage of the search is directed by a scalarization function and the second stage by Pareto selection enhanced with adaptive -Ranking.…”

Section: Hybrid Moeas Based On Pareto Dominancementioning

confidence: 99%

Comprehensive Survey of the Hybrid Evolutionary Algorithms

Khan

2013

International Journal of Applied Evolutionary Computation

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Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) are two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization problems (MOPs. The mathematical formulation of a MOP and some basic definitions for tackling MOPs, including Pareto optimality, Pareto optimal set (PS), Pareto front (PF) are provided in Section 1. Section 2 presents a brief introduction to hybrid MOEAs. The authors present literature review in subsections. Subsection 2.1 provides memetic multiobjective evolutionary algorithms. Subsection 2.2 presents the hybrid versions of well-known Pareto dominance based MOEAs. Subsection 2.4 summarizes some enhanced Versions of MOEA/D paradigm. Subsection 2.5 reviews some multimethod search approaches dealing optimization problems.

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“…-dominance [26], [27], grid-dominance [28], volumedominance [29], and subspace-dominance [30], [31] belong to this type of approaches. 2) Diversity-based approaches further enhance the selection pressure of MOEAs by maintaining better diversity.…”

Section: Introductionmentioning

confidence: 99%

Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search

Cai,

Sun,

Zhu

et al. 2018

Preprint

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In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEA-CS) was proposed. MaOEA-CS implicitly contains two phases: the exploitative search for the most important boundary optimal solutions -corner solutions, at the first phase, and the use of angle-based selection [1] with the explorative search for the extension of PF approximation at the second phase. Due to its high efficiency and robustness to the shapes of PFs, it has won the CEC 2017 Competition on Evolutionary Many-Objective Optimization. In addition, MaOEA-CS has also been applied on two real-world engineering optimization problems with very irregular PFs. The experimental results show that MaOEA-CS outperforms other six state-of-the-art compared algorithms, which indicates it has the ability to handle real-world complex optimization problems with irregular PFs.

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A Hybrid Scalarization and Adaptive ε-Ranking Strategy for Many-Objective Optimization

Cited by 4 publications

References 7 publications

Selection guide of multi-objective optimization for ergonomic design

Selection guide of multi-objective optimization for ergonomic design

Comprehensive Survey of the Hybrid Evolutionary Algorithms

Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search

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