基于遗传算法的混合技术用于解决多目标优化问题  

Hybrid technology based on genetic algorithm is used to solve multi-objective optimization problem

作　　者：胡珍妮 张艳维[1] HU Zhenni;ZHANG Yanwei(Xi’an Transportation Engineering Institute,Xi’an 710300,China)

机构地区：[1]西安交通工程学院,陕西西安710300

出　　处：《电子设计工程》2023年第13期41-45,50,共6页Electronic Design Engineering

摘　　要：随着新兴的通信技术,如移动边缘计算、物联网(IoT)和第五代(5G)宽带蜂窝网络等的发展,出现了许多与上述技术相关的多目标优化问题,例如能源消耗、具有成本效益的边缘用户分配和高效调度等。基于此,文中提出了一种基于遗传算法的改进技术,考虑通过模糊关系方程将这些问题公式化,作为实现优化解决方案的有效方法,用于求解由s范数模糊关系约束构成的多目标优化问题。在提出的方法中,缩小问题的规模,从而使简化后的问题易于求解。将所提出的基于遗传算法的技术应用于求解简化问题,这种方法最重要的优点是可以解决物联网、电子商务和5G领域的各种多目标优化问题。此外,通过数值实验证明了该方法的有效性,该方法不仅克服了传统方法在非凸可行域的局限性,而且对复杂系统的建模也有一定的应用价值。With the new communication technologies,such as edge mobile computing,the Internet of Things(IoT)and the fifth Generation(5G)broadband cellular networks such as the development of many multiobjective optimization problems associated with the above technology,for example,energy consumption,cost-effective edge user distribution and efficient scheduling,etc.Based on this,an improved technique based on genetic algorithm is proposed in this paper,which considers the formalization of these problems by fuzzy relation equations as an effective method to realize optimization solutions for multi-objective optimization problems composed of s-norm fuzzy relation constraints.In the proposed method,the scale of the problem is reduced to make the simplified problem easy to solve.The proposed technique based on genetic algorithm is applied to locally solve the simplified problem.The most important advantage of this approach is that it can solve various multi-objective optimization problems in the areas of Internet of Things,e-commerce and 5G.In addition,numerical experiments show the effectiveness of the proposed method,which not only overcomes the limitations of traditional methods in non-convex feasible region,but also has certain application value for complex system modeling.

关 键 词：遗传算法 多目标优化问题 模糊关系方程 通信技术 

分 类 号：TN0[电子电信—物理电子学]