Search Results for author: Ryoji Tanabe

Found 18 papers, 8 papers with code

On the Unbounded External Archive and Population Size in Preference-based Evolutionary Multi-objective Optimization Using a Reference Point

1 code implementation7 Apr 2023 Ryoji Tanabe

Then, we investigate the influence of the UA and population size on the performance of PBEMO algorithms.

Quality Indicators for Preference-based Evolutionary Multi-objective Optimization Using a Reference Point: A Review and Analysis

1 code implementation28 Jan 2023 Ryoji Tanabe, Ke Li

Some quality indicators have been proposed for benchmarking preference-based evolutionary multi-objective optimization algorithms using a reference point.

Benchmarking Decision Making

Benchmarking the Hooke-Jeeves Method, MTS-LS1, and BSrr on the Large-scale BBOB Function Set

1 code implementation28 Apr 2022 Ryoji Tanabe

This paper investigates the performance of three black-box optimizers exploiting separability on the 24 large-scale BBOB functions, including the Hooke-Jeeves method, MTS-LS1, and BSrr.


A Two-phase Framework with a Bézier Simplex-based Interpolation Method for Computationally Expensive Multi-objective Optimization

1 code implementation29 Mar 2022 Ryoji Tanabe, Youhei Akimoto, Ken Kobayashi, Hiroshi Umeki, Shinichi Shirakawa, Naoki Hamada

The first phase in TPB aims to approximate a few Pareto optimal solutions by optimizing a sequence of single-objective scalar problems.

Benchmarking Feature-based Algorithm Selection Systems for Black-box Numerical Optimization

1 code implementation17 Sep 2021 Ryoji Tanabe

However, there is still room for analysis of algorithm selection for black-box optimization.


Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction Framework

1 code implementation21 Apr 2021 Ryoji Tanabe

To improve the scalability of the ELA approach, this paper proposes a dimensionality reduction framework that computes features in a reduced lower-dimensional space than the original solution space.

Dimensionality Reduction

TPAM: A Simulation-Based Model for Quantitatively Analyzing Parameter Adaptation Methods

no code implementations5 Oct 2020 Ryoji Tanabe, Alex Fukunaga

We propose a Target function-based PAM simulation (TPAM) framework for evaluating the tracking performance of PAMs.

Reviewing and Benchmarking Parameter Control Methods in Differential Evolution

no code implementations2 Oct 2020 Ryoji Tanabe, Alex Fukunaga

We also investigate how much room there is for further improvement of PCMs by comparing the 24 methods with an oracle-based model, which can be considered to be a conservative lower bound on the performance of an optimal method.


An Analysis of Control Parameters of MOEA/D Under Two Different Optimization Scenarios

no code implementations2 Oct 2020 Ryoji Tanabe, Hisao Ishibuchi

An unbounded external archive (UEA), which stores all nondominated solutions found during the search process, is frequently used to evaluate the performance of multi-objective evolutionary algorithms (MOEAs) in recent studies.


How Far Are We From an Optimal, Adaptive DE?

no code implementations2 Oct 2020 Ryoji Tanabe, Alex Fukunaga

We consider how an (almost) optimal parameter adaptation process for an adaptive DE might behave, and compare the behavior and performance of this approximately optimal process to that of existing, adaptive mechanisms for DE.

Review and Analysis of Three Components of Differential Evolution Mutation Operator in MOEA/D-DE

no code implementations1 Oct 2020 Ryoji Tanabe, Hisao Ishibuchi

To address this issue, we present a review of the existing configurations of the DE mutation operator in MOEA/D-DE and systematically examine the influence of each component on the performance of MOEA/D-DE.

A Niching Indicator-Based Multi-modal Many-objective Optimizer

no code implementations1 Oct 2020 Ryoji Tanabe, Hisao Ishibuchi

Results show that the proposed method can handle a large number of objectives and find a good approximation of multiple equivalent Pareto optimal solutions.

A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms

no code implementations30 Sep 2020 Ryoji Tanabe, Hisao Ishibuchi

The performance of improved versions of six decomposition-based evolutionary algorithms by our framework is evaluated on various test problems regarding the number of objectives, decision variables, and equivalent Pareto optimal solution sets.

Non-elitist Evolutionary Multi-objective Optimizers Revisited

no code implementations30 Sep 2020 Ryoji Tanabe, Hisao Ishibuchi

Since around 2000, it has been considered that elitist evolutionary multi-objective optimization algorithms (EMOAs) always outperform non-elitist EMOAs.

A Review of Evolutionary Multi-modal Multi-objective Optimization

no code implementations28 Sep 2020 Ryoji Tanabe, Hisao Ishibuchi

Multi-modal multi-objective optimization aims to find all Pareto optimal solutions including overlapping solutions in the objective space.

An Analysis of Quality Indicators Using Approximated Optimal Distributions in a Three-dimensional Objective Space

no code implementations27 Sep 2020 Ryoji Tanabe, Hisao Ishibuchi

One promising approach for understanding quality indicators is the use of the optimal distribution of objective vectors that optimizes each quality indicator.


An Easy-to-use Real-world Multi-objective Optimization Problem Suite

1 code implementation27 Sep 2020 Ryoji Tanabe, Hisao Ishibuchi

Although synthetic test problems are widely used for the performance assessment of evolutionary multi-objective optimization algorithms, they are likely to include unrealistic properties which may lead to overestimation/underestimation.

Analyzing Adaptive Parameter Landscapes in Parameter Adaptation Methods for Differential Evolution

1 code implementation26 Sep 2020 Ryoji Tanabe

Second, we propose a method of analyzing adaptive parameter landscapes using a 1-step-lookahead greedy improvement metric.

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