FWA for Noisy optimization Problems JunQi zhang(张军旗) Department of Computer Science and Technology, Tongji University, Shanghai, China zhangiungi@tongi.edu.cn
FWA for Noisy Optimization Problems JunQi Zhang （张军旗） Department of Computer Science and Technology, Tongji University, Shanghai, China zhangjunqi@tongji.edu.cn
Content Noisy Optimization problem a Resampling methods Fireworks algorithms From resampling to Non-resampling in FWa Novel Directions for noisy optimization
Content ◼ Noisy Optimization Problem ◼ Resampling Methods ◼ Fireworks Algorithms ◼ From Resampling to Non-resampling in FWA ◼ Novel Directions for Noisy Optimization
Classes of uncertainties Robust Design scenario (A)Environmental uncertainty: Changing environmental and uncertain operating conditions Uncertainties (via the a-variable) C f=f(x, a) (B)Input uncertainty Design parameter tolerances System 1">Min: and actuator imprecision to a certain degree of (gray or black box) accuracy Fa-> Max f=f(X+b, a F3 (C)Output uncertainty Uncertainties concerning the observed system performance Design parameters f=f[f(x+b, a)] Optimization Strategy L quality signals
Classes of Uncertainties (A)Environmental uncertainty:Changing environmental and uncertain operating conditions (via the a-variable) f=f(x,a) (B)Input uncertainty:Design parameter tolerances and actuator imprecision to a certain degree of accuracy f=f(x+b,a) (C) Output uncertainty:Uncertainties concerning the observed system performance f’=f’[f(x+b,a)] 1. Beyer, H. G., Sendhoff, B., "Robust optimization–a comprehensive survey", Computer Methods in Applied Mechanics and Engineering, vol.196,no.33-34, pp. 3190–3218, 2007
噪声优化问题的优化目标 mieF(o)=G(,,…M(x,) with x=(x1+81,,+8 =(C1+v,,ck+k) 1: M, c )=f: Mx, c)+e1: M subject to x∈ 2019-TEVC-Robust Multiobjective Optimization via Evolutionary algorithms
噪声优化问题的优化目标 2019-TEVC-Robust Multiobjective Optimization via Evolutionary Algorithms
Input Uncertainty and Multi-fidelity Input Uncertainty 2018-TAC-Simulation Budget Allocation for Selecting the Top-m Designs with Input Uncertainty 2019-TEVC New Sampling Strategies When Searching for Robust Solutions a Multi-fidelity 2018-TEVC-A Generic Test Suite for Evolutionary Multifidelity Optimization 2019-TAC-Efficient Simulation Budget Allocation for Subset Selection Using Regression metamodels
Input Uncertainty and Multi-fidelity ◼ Input Uncertainty ◼ 2018-TAC-Simulation Budget Allocation for Selecting the Top-m Designs with Input Uncertainty ◼ 2019-TEVC-New Sampling Strategies When Searching for Robust Solutions ◼ Multi-fidelity ◼ 2018-TEVC-A Generic Test Suite for Evolutionary Multifidelity Optimization ◼ 2019-TAC-Efficient Simulation Budget Allocation for Subset Selection Using Regression Metamodels