Название: Decomposition-based Evolutionary Optimization In Complex EnvironmentsАвтор: Juan Li, Bin Xin, Jie Chen
Издательство: World Scientific Publishing
Год: 2021
Страниц: 248
Язык: английский
Формат: pdf (true)
Размер: 15.2 MB
Multi-objective optimization problems (MOPs) and uncertain optimization problems (UOPs) which widely exist in real life are challengeable problems in the fields of decision making, system designing, and scheduling, amongst others. Decomposition exploits the ideas of ‘making things simple’ and ‘divide and conquer’ to transform a complex problem into a series of simple ones with the aim of reducing the computational complexity. In order to tackle the abovementioned two types of complicated optimization problems, this book introduces the decomposition strategy and conducts a systematic study to perfect the usage of decomposition in the field of multi-objective optimization, and extend the usage of decomposition in the field of uncertain optimization.
The evolutionary algorithm (EA) is a type of classical stochastic optimization method and has been proved to be capable of dealing with complicated problems. Multi-objective evolutionary algorithms (MOEAs) focus on the posterior cases, which means designing efficient methods to obtain a set of non-dominated solutions that can be presented to decision makers to select from based on their preferences. In the literature, a category of MOPs that involves more than three objectives is termed as the many-objective optimization problem (MaOP).
Decomposition is an efficient and prevailing strategy in the field of traditional mathematical programming. Several methods for constructing aggregation functions can be found in the literature. The most popular ones among them include the weighted sum approach and Tchebycheff approach. Recently, the boundary intersection method and the ε-constraint method have also attracted a lot of attention. The success of decomposition has been witnessed by the multi-objective evolutionary algorithm MOEA/D and its variants. In decomposition-based methods, an MOP is decomposed into a number of scalar subproblems by using various scalarizing functions.
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