Generalized decomposition and cross entropy methods for many-objective optimization
نویسندگان
چکیده
منابع مشابه
Generalized decomposition and cross entropy methods for many-objective optimization
Decomposition-based algorithms for multi-objective optimization problems have increased in popularity in the past decade. Although their convergence to the Pareto optimal front (PF) is in several instances superior to that of Pareto-based algorithms, the problem of selecting a way to distribute or guide these solutions in a high-dimensional space has not been explored. In this work, we introduc...
متن کاملGeneralized Cross-Entropy Methods
The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on φ-divergence distance measures. We show how the χ distance in particular yields a viable alternative to Kull...
متن کاملAlternative Fitness Assignment Methods for Many-Objective Optimization Problems
Pareto dominance (PD) has been the most commonly adopted relation to compare solutions in the multiobjective optimization context. Multiobjective evolutionary algorithms (MOEAs) based on PD have been successfully used in order to optimize bi-objective and three-objective problems. However, it has been shown that Pareto dominance loses its effectiveness as the number of objectives increases and ...
متن کاملEvolutionary Many-Objective Optimization Based on Adversarial Decomposition
The decomposition-based method has been recognized as a major approach for multiobjective optimization. It decomposes a multi-objective optimization problem into several singleobjective optimization subproblems, each of which is usually defined as a scalarizing function using a weight vector. Due to the characteristics of the contour line of a particular scalarizing function, the performance of...
متن کاملGeneralized Cross-entropy Methods with Applications to Rare-event Simulation and Optimization
The cross-entropy and minimum cross-entropy methods are well-known Monte Carlo simulation techniques for rare-event probability estimation and optimization. In this paper, we investigate how these methods can be extended to provide a general non-parametric cross-entropy framework based on 1-divergence distance measures. We show how the 2 2 distance, in particular, yields a viable alternative to...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Information Sciences
سال: 2014
ISSN: 0020-0255
DOI: 10.1016/j.ins.2014.05.045