Heterogeneous dynamic vector evaluated particle swarm optimisation for dynamic multi-objective optimisation
作者： Marde Helbig , Andries P. Engelbrecht
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摘要: Optimisation problems with more than one objective, where at least objective changes over time, are called dynamic multi-objective optimisation (DMOOPs). Since two objectives in conflict another, a single solution does not exist, and therefore the goal of algorithm (DMOA) is to track set optimal trade-off solutions time. One major issues when solving problems, balancing exploration exploitation during search process. This paper investigates performance vector evaluated particle swarm (DVEPSO) using heterogeneous PSOs (HPSOs), each has different behaviour. The study determine whether use (HPSO) algorithms will improve DVEPSO by incorporating particles behaviour (PSO) algorithm. results indicate that HPSOs improves DVEPSO, especially for type I III DMOOPs.
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