Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Tomoaki Takagi"'
Publikováno v:
IEEE Access, Vol 11, Pp 20619-20634 (2023)
This work introduces the following concepts of directional and estimated directional Pareto front to encourage multi-objective decision making, especially when the Pareto front exists in limited regions in the objective space. The general output of m
Externí odkaz:
https://doaj.org/article/568626166a4747d8ad5b3b5dcb49fb42
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031272493
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0773c2be304444187d79668f9a991f1f
https://doi.org/10.1007/978-3-031-27250-9_16
https://doi.org/10.1007/978-3-031-27250-9_16
Publikováno v:
CEC
This work proposes an arrangement method of weight vectors using virtual objective vectors supplementing the Pareto front estimation. In decomposition-based evolutionary multi-objective optimization, weight vectors decompose the Pareto front. Appropr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8790b12073b7950b782b1e55b65ab11e
https://doi.org/10.1109/cec45853.2021.9504954
https://doi.org/10.1109/cec45853.2021.9504954
Publikováno v:
Hybrid Intelligent Systems ISBN: 9783030730499
HIS
HIS
This paper proposes multi-factorial distance minimization problems for benchmarking of multi-factorial optimization. The multi-factorial optimization simultaneously searches for optimal solutions of multiple objective functions in the common variable
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::43d501afb69e1b67d3004d6844c3e2d9
https://doi.org/10.1007/978-3-030-73050-5_69
https://doi.org/10.1007/978-3-030-73050-5_69
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030720612
EMO
EMO
This work proposes a method to estimate the Pareto front even in areas without objective vectors in the objective space. For the Pareto front approximation, we use a set of non-dominated points, objective vectors, in the objective space. To finely ap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bfac91a644cdada175b7577095fd937a
https://doi.org/10.1007/978-3-030-72062-9_11
https://doi.org/10.1007/978-3-030-72062-9_11
Publikováno v:
GECCO Companion
This paper proposes an alternative method to generate the weight vector set for the decomposition-based evolutionary multi- and many-objective optimization. Since each weight vector specifies an approximation point on the Pareto front, the distributi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8d2ecbaa78cc4d139055064d34bec8a4
https://doi.org/10.1145/3377929.3398082
https://doi.org/10.1145/3377929.3398082
Publikováno v:
GECCO Companion
This work proposes a method to map optimization problems and evolutionary search algorithms into a two-dimensional space, respectively. In the research domain of evolutionary optimization, benchmark optimization problems and search algorithms have be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::100d0e91df78f57b6e8a31819be64e8e
https://doi.org/10.1145/3377929.3398104
https://doi.org/10.1145/3377929.3398104
Publikováno v:
GECCO Companion
This work proposes a method to adaptively determine the grid granularity for the grid decomposition-based evolutionary algorithm for many-objective optimization. The decomposition of the objective space is one of the promising approaches for evolutio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::89a498e0d5ff91fee4f746394df2b546
https://doi.org/10.1145/3377929.3398141
https://doi.org/10.1145/3377929.3398141
Publikováno v:
CEC
This work focuses on the environmental selection methods incorporated in several evolutionary multi-objective optimization (EMO) algorithms for sampling representative nondominated solutions from a large non-dominated solution set. Evolutionary multi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::01d0c714b6af7c042bd245619fe0bed1
https://doi.org/10.1109/cec48606.2020.9185529
https://doi.org/10.1109/cec48606.2020.9185529
Publikováno v:
Bio-inspired Information and Communication Technologies ISBN: 9783030242015
BICT
BICT
For solving multi-objective optimization problems with evolutionary algorithms, the decomposing the Pareto front by using a set of weight vectors is a promising approach. Although an appropriate distribution of weight vectors depends on the Pareto fr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::faf25ab78a890c848219f736491b1182
https://doi.org/10.1007/978-3-030-24202-2_6
https://doi.org/10.1007/978-3-030-24202-2_6