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Coordinated Tuning of a Group of Static Var Compensators Using Multi-Objective Genetic Algorithm
(Centro Latinoamericano de Estudios en Informática, 2011)
PARETO EFFICIENT SOLUTIONS IN MULTI-OBJECTIVE OPTIMIZATION INVOLVING FORBIDDEN REGIONSPARETO EFFICIENT SOLUTIONS IN MULTI-OBJECTIVE OPTIMIZATION INVOLVING FORBIDDEN REGIONS
(Departamento de Matemática Aplicada. Facultad de Matemática y Computación. Universidad de La Habana, 2023)
Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems
(2014)
This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a ...
Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach
(Walter de Gruyter GmbHGermany, 2022)
A particle swarm optimizer for multi-objective optimization
(Universidad Nacional de La Plata. Facultad de Informática, 2005-12)
This paper proposes a hybrid particle swarm approach called Simple Multi-Objective Particle Swarm Optimizer (SMOPSO) which incorporates Pareto dominance, an elitist policy, and two techniques to maintain diversity: a ...
Space debris tracking with the poisson labeled multi-bernoulli filter
(MDPI, 2021)
This paper presents a Bayesian filter based solution to the Space Object (SO) tracking problem using simulated optical telescopic observations. The presented solution utilizes the Probabilistic Admissible Region (PAR) ...
Multi-objective optimization of a solar-assisted heat pump for swimming pool heating using genetic algorithm
(Elsevier Ltd, 2018)
A proper assessment of heating systems for swimming pools should evaluate the compromise between comfortlevel and the willingness to pay for it. The present work presents a multi-objective optimization of indirect solarassisted ...
Heuristically accelerated reinforcement learning modularization for multi-agent multi-objective problems
(2014)
This article presents two new algorithms for finding the optimal solution of a Multi-agent Multi-objective Reinforcement Learning problem. Both algorithms make use of the concepts of modularization and acceleration by a ...