An adaptive gradient multiobjective particle swarm optimization (AGMOPSO) algorithm, based on a multiobjective gradient (stocktickerMOG) method and a self-adaptive flight parameters mechanism, is developed to improve the computation performance in this paper. In this AGMOPSO algorithm, the stocktickerMOG method is devised to update the archive to improve the convergence speed and the local exploitation in the evolutionary process. Meanwhile, the self-adaptive flight parameters mechanism, according to the diversity information of the particles, is then established to balance the convergence and diversity of AGMOPSO. Attributed to the stocktickerMOG method and the self-adaptive flight parameters mechanism, this AGMOPSO algorithm not only has faster convergence speed and higher accuracy, but also its solutions have better diversity. Additionally, the convergence is discussed to confirm the prerequisite of any successful application of AGMOPSO. Finally, with regard to the computation performance, the proposed AGMOPSO algorithm is compared with some other multiobjective particle swarm optimization algorithms and two state-of-the-art multiobjective algorithms. The results demonstrate that the proposed AGMOPSO algorithm can find better spread of solutions and have faster convergence to the true Pareto-optimal front.

中文翻译：

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自适应梯度多目标粒子群优化


为了提高计算性能，本文提出了一种基于多目标梯度（stocktickerMOG）方法和自适应飞行参数机制的自适应梯度多目标粒子群优化（AGMOPSO）算法。在该AGMOPSO算法中，设计了stocktickerMOG方法来更新档案，以提高收敛速度和进化过程中的局部利用。同时，根据粒子的多样性信息，建立自适应飞行参数机制，平衡AGMOPSO的收敛性和多样性。得益于stocktickerMOG方法和自适应飞行参数机制，该AGMOPSO算法不仅具有更快的收敛速度和更高的精度，而且解具有更好的多样性。此外，还讨论了收敛性，以确认 AGMOPSO 成功应用的先决条件。最后，在计算性能方面，将所提出的 AGMOPSO 算法与其他一些多目标粒子群优化算法和两种最先进的多目标算法进行了比较。结果表明，所提出的 AGMOPSO 算法可以找到更好的解扩展，并更快地收敛到真正的 Pareto 最优前沿。