2019/10/02 by Maryam Hasani-Shoreh, Frank Neumann, Hasani-Shoreh, Maryam +1
Computer Science · #Advanced Multi-Objective Optimization Algorithms #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Metaheuristic Optimization Algorithms Research #Neural and Evolutionary Computing (cs.NE)
paper · pdf · doi:10.48550/arxiv.1910.06062
openalex publication_date 2019/10/02 · openalex created_date 2022/07/28 · openalex updated_date 2026/07/28
Population diversity plays a key role in evolutionary algorithms that enables\nglobal exploration and avoids premature convergence. This is especially more\ncrucial in dynamic optimization in which diversity can ensure that the\npopulation keeps track of the global optimum by adapting to the changing\nenvironment. Dynamic constrained optimization problems (DCOPs) have been the\ntarget for many researchers in recent years as they comprehend many of the\ncurrent real-world problems. Regardless of the importance of diversity in\ndynamic optimization, there is not an extensive study investigating the effects\nof diversity promotion techniques in DCOPs so far. To address this gap, this\npaper aims to investigate how the use of different diversity mechanisms may\ninfluence the behavior of algorithms in DCOPs. To achieve this goal, we apply\nand adapt the most common diversity promotion mechanisms for dynamic\nenvironments using differential evolution (DE) as our base algorithm. The\nresults show that applying diversity techniques to solve DCOPs in most test\ncases lead to significant enhancement in the baseline algorithm in terms of\nmodified offline error values.\n