Now showing items 1-3 of 3
Experimental study on population-based incremental learning algorithms for dynamic optimization problems
Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this ...
Metaheuristics for dynamic combinatorial optimization problems.
(The Institute of Mathematics and its Applications., 2012)
Many real-world optimization problems are combinatorial optimization problems subject to dynamic environments. In such dynamic combinatorial optimization problems (DCOPs), the objective, decision variables and/or constraints ...
A general framework of multi-population methods with clustering in undetectable dynamic environments.
To solve dynamic optimization problems, multiple population methods are used to enhance the population diversity for an algorithm with the aim of maintaining multiple populations in different subareas in the fitness ...