Now showing items 1-6 of 6
A proportion-based selection scheme for multi-objective optimization
(IEEE Press, 2018-02-08)
Classical multi-objective evolutionary algorithms (MOEAs) have been proven to be inefficient for solving multiobjective optimizations problems when the number of objectives increases due to the lack of sufficient selection ...
The effect of diversity maintenance on prediction in dynamic multi-objective optimization
There are many dynamic multi-objective optimization problems (DMOPs) in real-life engineering applications whose objectives change over time. After an environmental change occurs, prediction strategies are commonly used ...
An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance
Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, ...
A performance indicator for reference-point-based multiobjective evolutionary optimization
Aiming at the difficulty in evaluating preference-based evolutionary multiobjective optimization, this paper proposes a new performance indicator. The main idea is to project the preferred solutions onto a constructed ...
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 ...