计算机工程与应用2026，Vol.62Issue(1)：172-191,20.DOI:10.3778/j.issn.1002-8331.2505-0294
部分强化效应驱动的大规模多目标优化问题求解算法
Algorithm for Solving Large-Scale Multi-Objective Optimization Problems Driven by Partial Reinforcement Effect
摘要
Abstract
To address the challenges of high-dimensional decision space,convergence difficulties,and inefficient resource allocation in large-scale multi-objective optimization problems,the DVA-PRO algorithm driven by partial reinforcement effect for large-scale multi-objective optimization problems is proposed.This algorithm reconstructs the original objective problem through binary decision variables to reduce dimensionality and designs an evaluation and positive reinforcement mechanism based on partial reinforcement effect theory.It dynamically allocates computational resources,with a high reinforcement rate in the early stages of optimization to promote convergence and an expanded reinforcement scope in the later stages to maintain diversity.Comparison experiments of DVA-PRO with six algorithms are conducted on 100 large-scale multi-objective optimization benchmark test problems,and simulations are performed on four types of real-world engineering application problems.The experimental results indicate that DVA-PRO ranks first in terms of performance metrics on 79 benchmark test problems and all real-world engineering application problems.Under the same computational resource constraints,DVA-PRO can effectively search for and converge to the Pareto front,demonstrating superior com-prehensive performance to other algorithms and showing both efficiency and versatility in different types of large-scale multi-objective optimization problems.关键词
进化算法/大规模优化/多目标优化/部分强化效应/问题重构Key words
evolutionary algorithm/large-scale optimization/multi-objective optimization/partial reinforcement effect/problem restruring分类
信息技术与安全科学引用本文复制引用
顾清华,王晗睿,王倩,骆家乐..部分强化效应驱动的大规模多目标优化问题求解算法[J].计算机工程与应用,2026,62(1):172-191,20.基金项目
国家自然科学基金(52374135,52074205) （52374135,52074205）
陕西省金属矿智能开采理论及技术创新团队(2023-CX-TD-12) （2023-CX-TD-12）
陕西省矿产资源低碳智能高效开采技术创新引智基地 （）
陕西省智能开采理论与技术创新团队高校青年创新团队. （）
