Results 211 to 220 of about 633,451 (264)

Archived multi-objective simulated annealing transient electromagnetic inversion. [PDF]

Sci Rep
An Y, Fan T, Li P, Wang H, Liu Z, Zhao Z, Liu Q, Li G, Li B.   +8 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

A Lie group-based hybrid optimization framework for multi-objective UAV path planning using L-VGWO. [PDF]

Sci Rep
Wang Y, Guo C, Shao Y, Zheng L, Deng X.
europepmc   +1 more source

Fuzzy granulation-based wind speed prediction with multi-objective optimization. [PDF]

Sci Rep
Zhang C, Wang J, Li Z.
europepmc   +1 more source

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Multi-Objective Recommendations

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, 2021
The development of recommender systems usually deal with single-objective optimizations, such as minimizing prediction errors or maximizing the ranking quality. There is an emerging demand in multi-objective recommendations in which the recommendation list can be generated by optimizing multiple objectives.
Yong Zheng 0001, David (Xuejun) Wang
openaire   +1 more source

Multi-objective evolution strategy for multimodal multi-objective optimization

Applied Soft Computing, 2021
Abstract In the past decades, various effective and efficient multi-objective evolutionary algorithms (MOEAs) have been proposed for solving multi-objective optimization problems. However, existing MOEAs cannot satisfactorily address multimodal multi-objective optimization problems that demand to find multiple groups of optimal solutions ...
Kai Zhang 0002, Minshi Chen, Xin Xu 0007, Gary G. Yen   +3 more
openaire   +1 more source

Multi-Objective Clustering Ensemble

2006 Sixth International Conference on Hybrid Intelligent Systems (HIS'06), 2006
In this paper we present an algorithm for cluster analysis that integrates aspects from cluster ensemble and multi-objective clustering. The algorithm is constituted by a Pareto-based multi-objective genetic algorithm that uses clustering validation measures as the objective functions.
Katti Faceli, André Carlos Ponce de Leon Ferreira de Carvalho, Marcílio Carlos Pereira de Souto   +2 more
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Multi-Objective Scheduling

2009
no ...
Dutot, Pierre-François, Rzadca, Krzysztof, Saule, Érik, Trystram, Denis   +3 more
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Progressive Multi-Objective Optimization

International Journal of Information Technology & Decision Making, 2014
This paper introduces progressive multi-objective optimization (PMOO), a novel technique to include the decision maker's preferences into the multi-objective optimization process. PMOO integrates a well-known method for multi-criteria decision making (PROMETHEE) into a simple multi-objective metaheuristic by maintaining and updating a small reference ...
Kenneth Sörensen, Johan Springael
openaire   +3 more sources

Multi-Objective A* Algorithm for the Multimodal Multi-Objective Path Planning Optimization

2021 IEEE Congress on Evolutionary Computation (CEC), 2021
In this paper, we consider the multimodal multi-objective path planning (MMOPP) optimization, which is the main topic of a special session in IEEE CEC 2021. The MMOPP aims at finding all the Pareto optimal paths from a start area to a goal area on a grid map, while passing through several designated must-visit areas.
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