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

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Open Issues in Surrogate-Assisted Optimizationiques, propose suggestions for improvements and give an outlook on promising research directions. This is valuable for practitioners and researchers alike, since the increased availability of computational resources on the one hand and the continuous development of new approaches on the other hand raise many intricate new problems in this field.

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1860-949X lex problems.Provides theoretical treatments and real-world This book presents the state of the art in designing high-performance algorithms that combine simulation and optimization in order to solve complex optimization problems in science and industry, problems that involve time-consuming simulati

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Infill Criteria for Multiobjective Bayesian Optimizationthe progress in the computation of such integrals, we will present new, efficient, procedures for the high dimensional expected improvement and probability of improvement. Moreover, the chapter will summarize main properties of these infill criteria, including continuity and differentiability as wel

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Many-Objective Optimization with Limited Computing Budgetonverged and well distributed set of solutions within a limited computing budget. The proposed algorithm successfully combines features of state-of-the-art MaOPs and surrogate-assisted optimization strategies. The algorithm relies on principles of decomposition and adaption of reference vectors for

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Automatic Configuration of Multi-objective Optimizers and Multi-objective Configurationproaches can be used to design new, high-performing multi-objective evolutionary algorithms. The second aspect is the research on multi-objective configuration, that is, the possibility of using multiple performance metrics for the evaluation of algorithm configurations. We highlight some few exampl

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Optimization and Visualization in Many-Objective Space Trajectory Designtions, analyze the trade-offs between variables and objectives, and use a method called visualization with prosections to gain insights into the problem and to analyze the dynamics of the optimization algorithm.

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Towards Better Integration of Surrogate Models and Optimizerstions between these parameters, and how the problem characteristics impact optimization results. In the experimental study, we use the popular Black-Box Optimization Benchmarking (BBOB) testbed. Interestingly, the analysis finds no evidence for significant interactions between model and optimizer pa

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