Add to ORKGThe paper presents a taxonomic analysis of existing hybrid multi-objective evolutionary algorithms aimed at solving multi-objective simulation optimisation problems. For that, the properties of evolutionary algorithms and the requirements made to solving the problem considered are determined. Finally, a combination of the properties, which allows one to increase the approximation accuracy of the Pareto-optimal front at relatively low computational costs, is revealed
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
In order to improve the performance of optimization, we apply a hybridization of adaptive biogeograp...
This study overcomes the three major difficulties experienced by the existing multi-objective evolut...
Multi-objective simulation optimisation, evolutionary algorithms, hybrid algorithms Abstract – The ...
Handling multi-objective optimization problems using evolutionary computations represents a promisin...
For tackling multiobjective optimisation (MOO) problem, many methods are available in the field of e...
Abstract- The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the...
Abstract: Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-ob...
Multi-objective optimization problems arise frequently in applications but can often only be solved ...
Multi-objective optimization problems arise frequently in applications but can often only be solved ...
Many optimization functions have complex landscapes with multiple global or local optima. In order t...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
In this work, a multi-objective hybrid optimizer is presented. The optimizer uses several multi-obje...
In this paper new multicriteria design optimization methods are discus-sed. These methods are evolut...
Multi-objective optimization problems deal with multiple conflicting objectives. In principle, they ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
In order to improve the performance of optimization, we apply a hybridization of adaptive biogeograp...
This study overcomes the three major difficulties experienced by the existing multi-objective evolut...
Multi-objective simulation optimisation, evolutionary algorithms, hybrid algorithms Abstract – The ...
Handling multi-objective optimization problems using evolutionary computations represents a promisin...
For tackling multiobjective optimisation (MOO) problem, many methods are available in the field of e...
Abstract- The rapid advances of evolutionary methods for multi-objective (MO) optimization poses the...
Abstract: Evolutionary multi-objective optimization (EMO), whose main task is to deal with multi-ob...
Multi-objective optimization problems arise frequently in applications but can often only be solved ...
Multi-objective optimization problems arise frequently in applications but can often only be solved ...
Many optimization functions have complex landscapes with multiple global or local optima. In order t...
In this thesis, the basic principles and concepts of single and multi-objective Genetic Algorithms (...
In this work, a multi-objective hybrid optimizer is presented. The optimizer uses several multi-obje...
In this paper new multicriteria design optimization methods are discus-sed. These methods are evolut...
Multi-objective optimization problems deal with multiple conflicting objectives. In principle, they ...
Over the past few years, the research on evolutionary algorithms has demonstrated their niche in sol...
In order to improve the performance of optimization, we apply a hybridization of adaptive biogeograp...
This study overcomes the three major difficulties experienced by the existing multi-objective evolut...