大规模原油调度优化的双动态种群多目标进化算法

IF 6.6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Xianyu Hou , Renchu He , Wei Du
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引用次数: 0

摘要

随着炼油厂生产规模和设备复杂性的增加,炼油厂对原油调度提出了更严格的要求。因此,大规模多目标原油调度问题(LSMCOSPs)涉及大量的二元变量、非线性限制和许多多个优化目标,这使得传统算法难以有效地探索解空间,并且往往导致次优结果。本文通过构建海上炼油厂原油调度的离散时间混合整数非线性规划(MINLP)模型来解决这一挑战,该模型涵盖了卸载、运输、原油蒸馏装置(cdu)处理和中间产品库存管理等阶段。在此基础上,提出了一种双动态种群协同进化算法(DDPCEA)来解决该问题。实验包括三个调度案例,涉及多种原油类型、储罐和加工设备,共有数千个二元变量和数十个非线性约束。在算法执行过程中,初始固定的变异因子、交叉因子和非线性学习因子随着迭代次数的增加而动态演化。此外,引入修复策略进一步优化局部连续变量,使不可行解向可行区域移动。实验结果表明,与常用的LSMCOSPs多目标算法相比,本文提出的DDPCEA算法在切换次数和运行效率方面都有显著提高,同时在HV和IGD指标方面也取得了优异的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A dual-dynamic population multi-objective evolutionary algorithm for large-scale crude oil scheduling optimization
As refinery production scales and equipment complexity increase, refineries are setting stricter requirements for crude oil scheduling. Consequently, large-scale multi-objective crude oil scheduling problems (LSMCOSPs) involve a vast quantity of binary variables, nonlinear restrictions, and many multiple optimization objectives, making it challenging for conventional algorithms to efficiently explore the solution space and often resulting in suboptimal outcomes. This paper addresses this challenge by constructing a discrete-time mixed-integer nonlinear programming (MINLP) model for offshore refinery crude oil scheduling, covering stages such as unloading, transportation, processing in crude distillation units (CDUs), and intermediate product inventory management. Based on this model, we propose a dual-dynamic population co-evolutionary algorithm (denoted by DDPCEA) to solve the problem. The experiment consists of three scheduling cases, involving multiple crude oil types, storage tanks, and processing equipment, with a total of thousands of binary variables and dozens of nonlinear constraints. During the algorithm’s execution, the initially fixed mutation factor, crossover factor, and nonlinear learning factor dynamically evolve with the number of iterations. Additionally, a repair strategy is introduced to further optimize local continuous variables, moving infeasible solutions toward the feasible region. Experimental results demonstrate that, compared to commonly used multi-objective algorithms for LSMCOSPs, the proposed DDPCEA significantly improves both the number of changeovers and runtime efficiency, while also achieving superior performance in terms of HV and IGD metrics.
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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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