Titlebook: High-Performance Simulation-Based Optimization; Thomas Bartz-Beielstein,Bogdan Filipi?,El-Ghazali Book 2020 Springer Nature Switzerland A

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刀鋒

Many-Objective Optimization with Limited Computing Budgetg objectives. In recent years, a number of efficient algorithms have been proposed to deal with such problems, commonly referred to as many-objective optimization problems?(MaOP). However, most such algorithms require evaluation of numerous solutions prior to convergence which may not be affordable

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Multi-objective Bayesian Optimization for Engineering Simulationgate model (typically Kriging or a Gaussian Process) on evaluations of the objective function(s). To determine the next evaluation, an acquisition function is optimized (also referred to as infill criterion or sampling policy) which incorporates the model prediction and uncertainty and balances expl

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Automatic Configuration of Multi-objective Optimizers and Multi-objective Configurationxpertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it automatic. These research efforts go way beyond . only

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Optimization and Visualization in Many-Objective Space Trajectory Design Moon. The orbital raising phase is divided uniformly into sixteen sections, of which the first six are set to full propagation to escape early from the radiation belts, and the profiles of the other ten sections are subject to optimization together with the propagation start date and the spacecraft

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Surrogate-Assisted Evolutionary Optimization of Large Problemsmber of decision variables is large, or the number of objectives is large, or both. These problems pose challenges to evolutionary algorithms themselves, constructing surrogates and surrogate management. To address these challenges, we proposed two algorithms, one called kriging-assisted reference v

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