Metaheuristics for (Variable-Size) Mixed Optimization Problems: A Unified Taxonomy and Survey
This work addresses a gap in the optimization research community by organizing existing methodologies for a challenging class of problems, but it is incremental as it surveys and classifies rather than introduces new methods.
The paper tackles the lack of a unified taxonomy and comprehensive survey for metaheuristics solving mixed-variable optimization problems (MVOPs) and variable-size MVOPs (VMVOPs), presenting a taxonomy that provides a common terminology, classification mechanisms, and identifies open research issues.
Many real world optimization problems are formulated as mixed-variable optimization problems (MVOPs) which involve both continuous and discrete variables. MVOPs including dimensional variables are characterized by a variable-size search space. Depending on the values of dimensional variables, the number and type of the variables of the problem can vary dynamically. MVOPs and variable-size MVOPs (VMVOPs) are difficult to solve and raise a number of scientific challenges in the design of metaheuristics. Standard metaheuristics have been first designed to address continuous or discrete optimization problems, and are not able to tackle (V)MVOPs in an efficient way. The development of metaheuristics for solving such problems has attracted the attention of many researchers and is increasingly popular. However, to our knowledge there is no well established taxonomy and comprehensive survey for handling this important family of optimization problems. This paper presents a unified taxonomy for metaheuristic solutions for solving (V)MVOPs in an attempt to provide a common terminology and classification mechanisms. It provides a general mathematical formulation and concepts of (V)MVOPs, and identifies the various solving methodologies than can be applied in metaheuristics. The advantages, the weaknesses and the limitations of the presented methodologies are discussed. The proposed taxonomy also allows to identify some open research issues which needs further in-depth investigations.