A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

doi:10.1109/TEVC.2019.2958075

Return

A Novel Evolutionary Algorithm for Dynamic Constrained Multiobjective Optimization Problems

2020-08-01

Qingda Chen

Jinliang Ding *

杨

杨圣祥 (Shengxiang Yang)

Tianyou Chai

DOI：10.1109/TEVC.2019.2958075

Original

Save

View PDF

Abstract

En 中文

To promote research on dynamic constrained multiobjective optimization, we first propose a group of generic test problems with challenging characteristics, including different modes of the true Pareto front (e.g., convexity-concavity and connectedness-disconnectedness) and the changing feasible region. Subsequently, motivated by the challenges presented by dynamism and constraints, we design a dynamic constrained multiobjective optimization algorithm with a nondominated solution selection operator, a mating selection strategy, a population selection operator, a change detection method, and a change response strategy. The designed nondominated solution selection operator can obtain a nondominated population with diversity when the environment changes. The mating selection strategy and population selection operator can adaptively handle infeasible solutions. If a change is detected, the proposed change response strategy reuses some portion of the old solutions in combination with randomly generated solutions to reinitialize the population, and a steady-state update method is designed to improve the retained previous solutions. The experimental results show that the proposed test problems can be used to clearly distinguish the performance of algorithms, and that the proposed algorithm is very competitive for solving dynamic constrained multiobjective optimization problems in comparison with state-of-the-art algorithms.

Keywords:

Optical fibers

Sociology

Statistics

Heuristic algorithms

Linear programming

Optimization

Convergence

Change response

dynamic constrained multiobjective optimization

population selection

test problems

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.