.png){kind=icon}
返回
An adaptive binary quantum-behaved particle swarm optimization algorithm for the multidimensional knapsack problem
.png){kind=icon}
PRE
AI
DOI：10.1016/j.swevo.2024.101494.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The multidimensional knapsack problem (MKP) is a classical combinatorial optimization problem with wide real -life applications. Binary quantum -behaved particle swarm optimization (BQPSO) algorithm is a popular heuristic algorithm used in binary optimization. While BQPSO exhibits strong global search capabilities, it is still prone to local optima due to particle aggregation. To address this issue, an adaptive BQPSO (ABQPSO) algorithm is proposed to solve the MKP efficiently. A hybrid encoding population initialization scheme is employed, leveraging specific knowledge of MKP to increase population diversity and improve search efficiency. Furthermore, ABQPSO uses a mapping strategy that converts continuous values into discrete values based on the average position of particles. An adaptive repair operator considering two pseudo -utility ratios introduced to enable particles to explore different feasible regions, which dynamically adjusts current pseudo utility ratios based on changes in the global best solution. A local search method is applied to guide particles towards convergence to the optimum. A local sparseness degree measurement and a diversity mechanism are utilized to avoid local optima. To evaluate the effectiveness of ABQPSO, it is compared against ten stateof-the-art algorithms using 168 MKP benchmark instances of varying scales. Experimental results reveal that ABQPSO outperforms the comparison algorithms, especially for large-scale problems, demonstrating better solution accuracy.
Keyword:
Multidimensional knapsack problem
Combinatorial optimization problem
Quantum-behaved particle swarm optimization
Adaptive repair operator
Local search
期刊
IF:
8.5
论文数:
2.2K
被引数:
1.0W
机构
引用论文
Physical properties of the cell wall of photoautotrophic suspension cells fromChenopodium rubrum L..png)
Planta
PlantaIF0
PREAI
分享收藏分享收藏分享收藏Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
PREAI
分享收藏A Cluster-Based Competitive Particle Swarm Optimizer with a Sparse Truncation Operator for Multi-Objective Optimization基于聚类的稀疏截断竞争粒子群算法的多目标优化
PREAI
分享收藏A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms关于使用非参数统计检验作为比较进化和群体智能算法的方法的实用教程
PREAI
分享收藏
查看更多内容