MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem

Liu, Rui Tao; Lv, Xiu Jian

doi:10.4028/www.scientific.net/amm.380-384.1877

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, ACO algorithms have been considered as one of the most efficient algorithms in finding out solutions to NP-hard problems [11], [18]. However, ACO algorithms have problems of premature convergence and stagnation in general, and the convergence rates of ACO algorithms are low for solving these NP-hard problems, especially with the increment of problem scale [19], [20], [21], [22], [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

Liu

Gao

Zhang

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

NP-hard problems exist in many real world applications. Ant colony optimization (ACO) algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system (ACS) for solving NP-hard problems such as traveling salesman problem (TSP) and 0/1 knapsack problem (0/1 KP). In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.

show abstract

Section: Introductionmentioning

confidence: 99%

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

Liu

Gao

Zhang

et al. 2017

IEEE/ACM Trans. Comput. Biol. and Bioinf.

View full text Add to dashboard Cite

show abstract

A parallel implementation to the multidimensional knapsack problem using augmented neural networks

Dantas

Cáceres

2014

2014 XL Latin American Computing Conference (CLEI)

View full text Add to dashboard Cite

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Laabadi¹,

Naimi²,

Amri³

et al. 2018

AJOR

View full text Add to dashboard Cite

The 0/1 Multidimensional Knapsack Problem (0/1 MKP) is an interesting NP-hard combinatorial optimization problem that can model a number of challenging applications in logistics, finance, telecommunications and other fields. In the 0/1 MKP, a set of items is given, each with a size and value, which has to be placed into a knapsack that has a certain number of dimensions having each a limited capacity. The goal is to find a subset of items leading to the maximum total profit while respecting the capacity constraints. Even though the 0/1 MKP is well studied in the literature, we can just find a little number of recent review papers on this problem. Furthermore, the existing reviews focus particularly on some specific issues. This paper aims to give a general and comprehensive survey of the considered problem so that it can be useful for both researchers and practitioners. Indeed, we first describe the 0/1 MKP and its relevant variants. Then, we present the detailed models of some important real-world applications of this problem. Moreover, an important collection of recently published heuristics and metaheuristics is categorized and briefly reviewed. These approaches are then quantitatively compared through some indicative statistics. Finally, some synthetic remarks and research directions are highlighted in the conclusion.

show abstract

MapReduce-Based Ant Colony Optimization Algorithm for Multi-Dimensional Knapsack Problem

Cited by 3 publications

References 5 publications

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

Solving NP-Hard Problems with <italic>Physarum</italic>-Based Ant Colony System

A parallel implementation to the multidimensional knapsack problem using augmented neural networks

The 0/1 Multidimensional Knapsack Problem and Its Variants: A Survey of Practical Models and Heuristic Approaches

Contact Info

Product

Resources

About