Benchmarking for Metaheuristic Black-Box Optimization: Perspectives and Open Challenges

Research on new optimization algorithms is often funded based on the motivation that such algorithms might improve the capabilities to deal with real-world and industrially relevant optimization challenges. Besides a huge variety of different evolutionary and metaheuristic optimization algorithms, also a large number of test problems and benchmark suites have been developed and used for comparative assessments of algorithms, in the context of global, continuous, and black-box optimization. For many of the commonly used synthetic benchmark problems or artificial fitness landscapes, there are however, no methods available, to relate the resulting algorithm performance assessments to technologically relevant real-world optimization problems, or vice versa. Also, from a theoretical perspective, many of the commonly used benchmark problems and approaches have little to no generalization value. Based on a mini-review of publications with critical comments, advice, and new approaches, this communication aims to give a constructive perspective on several open challenges and prospective research directions related to systematic and generalizable benchmarking for black-box optimization

Sensitivity Analysis in Systematic and Representative Benchmarking of Optimization Algorithm Performance

International audienceBesides the conventional and established applications of sensitivity analysis in the field of optimization, sensitivity analysis-based approaches can also be used in research on systematic performance analysis of meta-heuristic optimization algorithms, and the construction of synthetic industrially relevant benchmark problems. By means of academic examples as well as industrial case studies, we demonstrate innovative approaches which set steps towards systematic and representative benchmarking and empirical optimization algorithm performance analysis

Topology optimization combined with element-by-element solution techniques

Topology optimization approaches are commonly used for design problems involving physical phenomena related to solid mechanics, acoustics, electromagnetism, fluid mechanics, and combinations thereof. In computational models of these physical phenomena, the field variables are commonly approximated using spatial discretizations within the domain using the Finite Element Method. Even for topology design problems in which the field variables must only satisfy linear state equations, the storage and solution of the resulting global system of equations can become a computational bottleneck. In this contribution, we investigate a topology optimization approach in which the Solid Isotropic Material with Penalization (SIMP) method is combined with element-wise solution approaches, in order to reduce computational memory requirements