On the Use of Diversity Mechanisms in Dynamic Constrained Continuous Optimization
This work addresses a gap in dynamic constrained optimization, which is important for researchers in evolutionary algorithms, but it is incremental as it applies existing diversity mechanisms to a specific problem type.
The paper tackled the lack of extensive study on diversity promotion techniques in dynamic constrained optimization problems (DCOPs) by investigating how different diversity mechanisms influence algorithm behavior, finding that applying these techniques in most test cases led to significant enhancement in the baseline algorithm in terms of modified offline error values.
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.