On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization

TL;DR

This paper investigates how various diversity promotion mechanisms affect the performance of differential evolution algorithms in dynamic constrained optimization problems, showing significant improvements in solution quality.

Contribution

It adapts and applies common diversity mechanisms to DCOPs and evaluates their impact, filling a research gap in understanding diversity effects in these problems.

Findings

Diversity techniques improve baseline algorithm performance in most test cases.

Applying diversity mechanisms reduces offline error values.

Enhanced exploration leads to better tracking of global optima.

Abstract

Population diversity plays a key role in evolutionary algorithms that enables global exploration and avoids premature convergence. This is especially more crucial in dynamic optimization in which diversity can ensure that the population keeps track of the global optimum by adapting to the changing environment. Dynamic constrained optimization problems (DCOPs) have been the target for many researchers in recent years as they comprehend many of the current real-world problems. Regardless of the importance of diversity in dynamic optimization, there is not an extensive study investigating the effects of diversity promotion techniques in DCOPs so far. To address this gap, this paper aims to investigate how the use of different diversity mechanisms may influence the behavior of algorithms in DCOPs. To achieve this goal, we apply and adapt the most common diversity promotion mechanisms for dynamic environments using differential evolution (DE) as our base algorithm. The results show that applying diversity techniques to solve DCOPs in most test cases lead to significant enhancement in the baseline algorithm in terms of modified offline error values.