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基于动态资源分配策略的多目标多因子进化算法

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为了充分利用多任务之间的迁移学习,并且降低计算代价,提出一种基于动态资源分配策略的多目标多因子进化算法.将多目标多因子优化问题分解为多个单目标优化子问题,利用统一空间中多因素分布的单种群同时优化所有单目标子问题;提出一种多因子环境下的动态资源分配策略,将计算资源根据这些单目标子问题在每一代中的演化速度进行分配;将该算法应用于9 个多任务优化问题,实验结果表明提出的算法能够提升优化效果,降低计算代价.
MULTIPLE OBJECTIVE AND MULTIPLE FACTOR EVOLUTIONARY ALGORITHM BASED ON DYNAMIC RESOURCE ALLOCATION STRATEGY
In order to make full use of the transfer knowledge between multiple tasks and reduce the computational cost,a multiple objective and multiple factor evolutionary algorithm based on dynamic resource allocation strategy is proposed.The multiple objective and multiple task optimization problem was decomposed into several single objective optimization sub-problems,and all single objective sub-problems were optimized simultaneously by using a single population with multiple factor distribution in a unified space.A dynamic resource allocation strategy in multiple factor environment was proposed,which allocated computing resources according to the evolution speed of these single objective sub-problems in each generation.The algorithm was applied to nine multiple task optimization problems.The experimental results show that the proposed algorithm can improve the optimization effect and reduce the computational cost.

Dynamic resource allocationTransfer learningMultiple objectiveMultiple factor

郭素芹、林瑞安、刘伟、吴高杰、李莹

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福建亿力电力科技有限责任公司 福建 福州 350001

国网上海市电力公司电力科学研究院 上海 200000

动态资源分配 迁移学习 多目标 多因子

2024

计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
年,卷(期):2024.41(12)