Now showing items 11-14 of 14
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 predictive strategy based on special points for evolutionary dynamic multi-objective optimization
There are some real-world problems in which multiple objectives conflict with each other and the objectives change with time. These problems require an optimization algorithm to track the moving Pareto front or Pareto set ...
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 ...
Solving dynamic multi-objective problems with an evolutionary multi-directional search approach
The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction ...