Now showing items 1-4 of 4
Hybrid of memory andprediction strategies for dynamic multiobjective optimization
Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the ...
An adaptation reference-point-based multiobjective evolutionary algorithm
It is well known that maintaining a good balance between convergence and diversity is crucial to the performance of multiobjective optimization algorithms (MOEAs). However, the Pareto front (PF) of multiobjective optimization ...
A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective optimization
Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added ...
Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons
Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although ...