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Efficient Computation of Probabilistic Dominance in Robust Multi-Objective Optimization [article]

Faramarz Khosravi, Alexander Raß, Jürgen Teich
2019 arXiv   pre-print
Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions, which further increases the complexity of optimization  ...  Given two candidate solutions under comparison, this operator calculates the probability that one solution dominates the other in terms of each uncertain objective.  ...  This probabilistic dominance criterion can incorporate any of the previously introduced comparison operators in Section 3.1 to enable treating uncertainty in multi-objective optimization.  ... 
arXiv:1910.08413v1 fatcat:h24sdopgzzekdfseyk7foivdsm

Evolutionary multi-objective optimization algorithms with probabilistic representation based on pheromone trails

Hui Li, Dario Landa-silva, Xavier Gandibleux
2010 IEEE Congress on Evolutionary Computation  
In this paper, we investigate the performance of two state-of-the-art EMO algorithms -MOEA/D and NSGA-II, with probabilistic representation based on pheromone trails, on the multi-objective travelling  ...  So far, not much work has been done on evolutionary multi-objective (EMO) algorithms with probabilistic representation.  ...  However, little work has been done on designing multi-objective oriented representation of solutions. In [4] , a hybrid representation was studied for multi-objective optimization.  ... 
doi:10.1109/cec.2010.5585998 dblp:conf/cec/LiSG10 fatcat:3jscrmbkavatvnn4ute7qv3bme

Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic

Asmae Gannouni, Rachid Ellaia, El-Ghazali Talbi, B. Abou El Madj, M. Bouya
2017 MATEC Web of Conferences  
Finally, The behavior of the resulting Probabilistic Multi-objective Evolutionary Algorithms (PrMOEAs) is empirically investigated on the multi-objective stochastic VRP problem.  ...  From these considerations, the scope of this research should consist of: (i)Proposed novel methodology of stochastic optimality for ranking objective functions characterized by non-continuous and no closed  ...  The set of all efficient solutions is called efficient(or Pareto optimal) set, and its mapping in the objective space is called Pareto front.  ... 
doi:10.1051/matecconf/201710500001 fatcat:bgckzyxna5bgzpu2lvj73tlwq4

An Efficient Multi-objective Meta-heuristic Method for Probabilistic Transmission Network Planning

Kakuta Hiroki, Hiroyuki Mori
2014 Procedia Computer Science  
CNSGA-II is an efficient method for calculating a set of the Pareto optimal solutions efficiently in multi-objective optimization problems.  ...  The proposed method focused on a multi-objective optimization problem of construction cost and reliability to evaluate a set of the Pareto solutions efficiently, where probabilistic reliability index EENS  ... 
doi:10.1016/j.procs.2014.09.019 fatcat:pvdr33chdvgznailsjbfpheeza

A Combined Algorithm For Solving Reliability-based Robust Design Optimization Problems

Ameneh Forouzandeh Shahraki, Rassoul Noorossana
2013 Journal of Mathematics and Computer Science  
The main goal of all of them is optimizing the mean and minimizing the variance of performance function. In these problems, the accuracy and efficiency of  ...  Moreover, to increase the efficiency of the genetic algorithm, we use the design of experiment (DOE) to find the optimal levels of the parameters of this algorithm.  ...  Genetic algorithm (GA) is well suited to solve multi-objective optimization problems. A multi-objective GA can find a set of multiple non-dominated solutions in a single run.  ... 
doi:10.22436/jmcs.07.01.06 fatcat:pobtu5vht5hxxadanv4yyn2la4

Virtual interpolation of discrete multi-objective programming solutions with probabilistic operation

Ricardo C. Silva, Edilson F. Arruda, Fabrício O. Ourique
2011 SBA : Controle & Automação Sociedade Brasileira de Automatica  
This work presents a novel framework to address the long term operation of a class of multi-objective programming problems.  ...  To illustrate the approach, a two-phase method is proposed which solves a prescribed number of K mono-objective problems to identify a set of K points in the Pareto-optimal region.  ...  A solution x * ∈ Ω is said to be non-dominated (or Pareto-optimal, or efficient) if there exists no alternative solution in Ω that is best or equal in all objectives simultaneously and that strictly improves  ... 
doi:10.1590/s0103-17592011000400005 fatcat:5lmiwex24vbnldvk6ypu7poqsq

Lost in Optimization of Water Distribution Systems: Better Call Bayes

Antonio Candelieri, Andrea Ponti, Ilaria Giordani, Francesco Archetti
2022 Water  
BO can also be extended to multi-objective optimization. Two algorithms are proposed for multi-objective detection problems using two different acquisition functions.  ...  The related optimization problems fall into a simulation/optimization framework in which objectives and constraints are often black box.  ...  Acknowledgments: We greatly acknowledge the DEMS Data Science Lab for supporting this work by providing computational resources (DEMS-Department of Economics, Management and Statistics).  ... 
doi:10.3390/w14050800 fatcat:kvqawljdcfdbzi5us6ymtuztam

Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm

Jie Jia, Jian Chen, Guiran Chang, Zhenhua Tan
2009 Computers and Mathematics with Applications  
A novel coverage control scheme based on multi-objective genetic algorithm is proposed in this paper.  ...  Activating only the necessary number of sensor nodes at any particular moment is an efficient way to save the overall energy of the system.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their constructive suggestions, which improved the technical quality of the paper.  ... 
doi:10.1016/j.camwa.2008.10.036 fatcat:6ussrw3drfgtxlosiumvj76cwy

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larranaga
2014 IEEE Transactions on Evolutionary Computation  
This paper proposes a new multi-objective estimation of distribution algorithm (EDA) based on joint probabilistic modeling of objectives and variables.  ...  of the problems comparing with the search based on conventional genetic operators in the state-of-the-art multi-objective evolutionary algorithms.  ...  A Survey of Multi-objective Optimization with Probabilistic Modeling In several multi-objective EDAs proposed in the literature, a Bayesian network is estimated as the probabilistic model.  ... 
doi:10.1109/tevc.2013.2281524 fatcat:4s3ahirmsjavzclu4ranf6tyny

Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

Syed Kazmi, Muhammad Shahzad, Dong Shin
2017 Energies  
The multi-objective planning (MOP) problem had partially addressed in both works.  ...  Energies 2017, 10, 208 4 of 44 It is a well-established fact that real world planning problems are multi-objective (MO) in nature.  ...  review in the field of multi-objective planning focusing on four planning techniques.  ... 
doi:10.3390/en10020208 fatcat:7lletw53lvg2jlkwffwsmmscwu

Neurocomputing strategies for solving reliability‐robust design optimization problems

Nikos D. Lagaros, Vagelis Plevris, Manolis Papadrakakis
2010 Engineering computations  
The RRDO problem is to be formulated as a multi-objective optimization problem where the construction cost and the standard deviation of the structural response are the objectives to be minimized.  ...  Design/methodology/approach -The solution of the optimization problem is performed with the non-dominant cascade evolutionary algorithm with the weighted Tchebycheff metric, while the probabilistic analysis  ...  Solving the multi-objective optimization problem In this work the non-dominant CEA is further enhanced with the augmented Tchebycheff metric (Lagaros et al., 2005b) for solving the multi-objective optimization  ... 
doi:10.1108/02644401011073674 fatcat:dumzrg5nxvbjjkbt5n2measd5a

Multi-objective optimization with diversity preserving mixture-based iterated density estimation evolutionary algorithms

Peter A.N. Bosman, Dirk Thierens
2002 International Journal of Approximate Reasoning  
Moreover, they allow for elegant and parallel exploration of a multi-objective front. This parallel exploration aids the important preservation of diversity in multi-objective optimization.  ...  In this paper, we propose a new algorithm for evolutionary multi-objective optimization by learning and using probabilistic mixture distributions.  ...  In this paper, we improve this algorithm and show how learning and using probabilistic models in EAs can lead to efficient multi-objective optimization.  ... 
doi:10.1016/s0888-613x(02)00090-7 fatcat:b5h3ttolwzflhjmjxopc26h4hm

MOEA/D-GM: Using probabilistic graphical models in MOEA/D for solving combinatorial optimization problems [article]

Murilo Zangari de Souza, Roberto Santana, Aurora Trinidad Ramirez Pozo, Alexander Mendiburu
2015 arXiv   pre-print
MOEA/D is a decomposition based evolutionary algorithm that decomposes a multi-objective optimization problem (MOP) in a number of scalar single-objective subproblems and optimizes them in a collaborative  ...  To validate the introduced framework algorithm, an experimental study is conducted on a multi-objective version of the deceptive function Trap5.  ...  INTRODUCTION Several real-world problems can be stated as multi-objective optimization problems (MOPs) which have two or more objectives to be optimized.  ... 
arXiv:1511.05625v1 fatcat:sw42k3v6g5artkrwfxdnxyywwu

Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimization [article]

Antonio Candelieri, Andrea Ponti, Francesco Archetti
2022 arXiv   pre-print
FanG-HPO uses subsets of the large dataset (aka information sources) to obtain cheap approximations of both accuracy and fairness, and multi-objective Bayesian Optimization to efficiently identify Pareto-efficient  ...  learning algorithms and hyperparameter optimization via a multi-objective single-source optimization algorithm in BoTorch, a state-of-the-art platform for Bayesian Optimization.  ...  Acknowledgements We greatly acknowledge the DEMS Data Science Lab, Department of Economics Management and Statistics (DEMS), University of Milano-Bicocca, for supporting this work by providing computational  ... 
arXiv:2205.08835v1 fatcat:qkimsbxm3fd4ph5nzss7kcjgam

A Novel Probabilistic Pruning Approach to Speed Up Similarity Queries in Uncertain Databases [article]

Thomas Bernecker, Tobias Emrich, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz, Andreas Zuefle
2011 arXiv   pre-print
and a set D of uncertain database objects in a multi-dimensional space, the probabilistic domination count denotes the number of uncertain objects in D that are closer to R than B.  ...  In a nutshell, the problem to be solved is to compute the PDF of the random variable denoted by the probabilistic domination count: Given an uncertain database object B, an uncertain reference object R  ...  The problem solved in this paper is to efficiently compute the probability density distribution of DomCount(B, R)(B ∈ D) formally introduced by means of the probabilistic domination (cf.  ... 
arXiv:1101.2613v2 fatcat:zbycba6jhzgxvifkwmlh5dgrxu
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