Influence of the archive size on the performance of the dynamic vector evaluated particle swarm optimisation algorithm solving dynamic multi-objective optimisation problems
作者： Marde Helbig , Andries Engelbrecht
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摘要: Many real-world problems consist of multiple objectives that are in conflict with one another and dynamic nature. These kinds do not have a single solution, but set optimal trade-off solutions. solutions stored fixed-size archive during the optimisation process. A decision maker then decides which solution to use for specific problem. Larger archives require more computations than smaller archives. However, no research has been conducted determine influence size on performance algorithms when solving these problems. Therefore, this paper investigates effect sizes vector evaluated particle swarm algorithm. In addition, study sampling true Pareto-optimal front (POF) measure values using small The results indicate does algorithm certain cases may be beneficial. Furthermore, larger POF worse value
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