# On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints

## TL;DR

This paper introduces a flexible benchmark framework for testing evolutionary algorithms on dynamic constrained optimization problems with linear constraint changes, addressing limitations of existing benchmarks in scalability and environmental change types.

## Contribution

The paper proposes a novel framework for creating scalable, flexible benchmarks with linear constraint changes, applicable to any objective function in dynamic environments.

## Key findings

- Constraint handling techniques' performance is affected by linear constraint changes.
- The framework allows testing algorithms under various problem dimensions and change frequencies.
- It provides a more realistic and adaptable benchmark for dynamic constrained optimization.

## Abstract

Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.