Paper Info
Title
A differential prediction model for evolutionary dynamic multiobjective optimization.
Abstract
This paper introduces a differential prediction model to predict the varying Pareto-Optimal Solutions (POS) when solving dynamic multiobjective optimization problems (DMOPs). In dynamic multiobjective optimization problems, several competing objective functions and/or constraints change over time. As a consequence, the Pareto-Optimal Solutions and/or Pareto-Optimal Front may vary over time. The differential prediction model is used to forecast the shift vector in the decision space of the centroid in the population through the centroid's historical locations in three previous environments. This differential prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition to solve DMOPs. After detecting the environmental change, half of individuals in the population are forecasted their new positions in the decision space by using the differential prediction model and the others' positions are retained. The proposed model is tested on a number of typical benchmark problems with several dynamic characteristics. Experimental results show that the proposed model is competitively in comparisons with the other state-of-the-art models or approaches that were proposed for solving DMOPs.
Year
DOI
Venue
2018
10.1145/3205455.3205494
GECCO
Keywords
Field
DocType
Differential prediction model, dynamic environments, multiobjective optimization, evolutionary algorithm
Population,Mathematical optimization,Evolutionary algorithm,Computer science,Multi-objective optimization,Multiobjective optimization problem,Centroid
Conference
ISBN
Citations 
PageRank 
978-1-4503-5618-3
3
0.38
References 
Authors
17
5
Name
Order
Citations
PageRank
Leilei Cao1312.78
Lihong Xu234436.70
Erik Goodman314515.19
Shuwei Zhu4252.94
Hui Li5782.02