Evolutionary robustness analysis for multi-objective optimization: benchmark problems
نویسندگان
چکیده
منابع مشابه
Robustness Analysis in Evolutionary Multi-Objective Optimization
This paper presents two approaches to robustness analysis in multi-objective optimization problems, in which the model data (coefficients of objective functions, coefficients of constraints, bounds of decision variables, etc.) are subject to small perturbations, with respect to a ”nominal” set of coefficients of the model data. In these approaches, the concept of degree of robustness is incorpo...
متن کاملMulti-Objective Benchmark Studies for Evolutionary Computation
During the past few decades, many global optimisation and multi-objective evolutionary algorithms (MOEAs) have been developed. Those algorithms have shown very useful in enabling system design automation and globally accurate modelling. However, there is a lack of systematic benchmark measures that may be used to assess the merit and performance of these algorithms [1],[2],[3],[7],[8]. Such ben...
متن کاملScalable Test Problems for Evolutionary Multi-Objective Optimization
After adequately demonstrating the ability to solve different two-objective optimization problems, multi-objective evolutionary algorithms (MOEAs) must now show their efficacy in handling problems having more than two objectives. In this paper, we have suggested three different approaches for systematically designing test problems for this purpose. The simplicity of construction, scalability to...
متن کاملConstrained Test Problems for Multi-objective Evolutionary Optimization
Over the past few years, researchers have developed a number of multi-objective evolutionary algorithms (MOEAs). Although most studies concentrated on solving unconstrained optimization problems, there exists a few studies where MOEAs have been extended to solve constrained optimization problems. As the constraint handling MOEAs gets popular, there is a need for developing test problems which c...
متن کاملA New Evolutionary Algorithm for Multi-objective Optimization Problems
Among the currently successful Evolutionary Multi-Objective Algorithms (MOEAs), elitism and no sharing factor are two common characteristics and have been demonstrated to improve performance significantly. Based on these two principles, two heuristics, with which impressive improvements were showed in single objective optimization, are introduced in a newly designed EMOA in this paper: multi-pa...
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: Structural and Multidisciplinary Optimization
سال: 2013
ISSN: 1615-147X,1615-1488
DOI: 10.1007/s00158-013-1010-x