Composite predictive evolutionary algorithm for dynamic constrained multi-objective problems
GUO Zhi-ye
WEI Jing-xuan
Abstract：Dynamic constrained multi-objective problems are used in real-world scenarios,such as intersection traffic management and energy-efficient power scheduling.Both the objective functions and constraints undergo continuous slow changes over time(in the environment).The key to solving these dynamic problems is effectively tracking a set of optimal solutions that evolve with environment.To address such problems,firstly,constraint changes are categorized into two types,and two constraint predictors are proposed for tracking feasible regions.Secondly,the constraint predictors are combined with a nonlinear predictor to form a composite predictive strategy.Depending on the specific changes in the problem,the strategy uses the corresponding predictor to obtain predictive solutions with less resource consumption,thus accelerating the optimization process.Finally,a decomposition-based multi-objective optimization algorithm is applied to optimize the predictive solutions,obtaining the ultimate optimal solution.The composite predictive evolutionary algorithm is compared with six typical evolutionary algorithms on eight dynamic problems.The experimental results demonstrate that the proposed algorithm has a significant advantage in terms of convergence and diversity in the solution set,with superior predictive performance of the composite strategy.
Keywords：dynamic multi-objective optimizationevolutionary algorithmtime-varying constraints
Publication Date：2025-02-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 335-343 )