Solving the 0-1 Knapsack Problem by Using Tissue P System With Cell Division

Lian, Yong; Zheng, Jinhang; Guo, Ping; Pérez–Jiménez, Mario J.

doi:10.1109/access.2019.2917889

Cited by 14 publications

(4 citation statements)

References 30 publications

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“…𝐵𝑀𝐻 = (𝑋(𝑡) − 𝐷) + (𝑟𝑖 5 × 𝑚𝑎𝑙𝑒 𝑔𝑎𝑧𝑒𝑙𝑙𝑒 − 𝑟𝑖 6 × 𝐵𝐻 ) × 𝐶𝑜𝑓 𝑟 (7) where 𝑋(𝑡) represents the current position of the solution, and 𝐷 is a vector that represents the direction of the movement. The next component, (𝑟𝑖 5 × 𝑚𝑎𝑙𝑒 𝑔𝑎𝑧𝑒𝑙𝑙𝑒 − 𝑟𝑖 6 × 𝐵𝐻 ), is a combination of two vectors that determine the direction of the movement based on the best solution found so far (𝐵𝐻) and the location of a reference point (𝑚𝑎𝑙𝑒 𝑔𝑎𝑧𝑒𝑙𝑙𝑒 ).…”

Section: The Mountain Gazelle Optimizermentioning

confidence: 99%

The Enhanced Binary Mountain Gazelle Optimization Algorithm for 0-1 Knapsack Problems

BAS,

IHSAN

2024

Preprint

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Algorithms serve as the backbone of computer science, permeating diverse fields with their indispensable applications. The Knapsack Problems (KP), an optimization puzzle, revolves around the judicious selection of items characterized by their values and weights to maximize utility within the constraints of a limited-capacity container. This study introduces a pioneering mathematical optimization approach inspired by the nuanced behaviors of natural gazelles. Delving deep into the intricate hierarchical and social dynamics inherent in gazelle behavior, the Binary Mountain Gazelle Optimizer (BinMGO) emerges as a standout. Empowered by six diverse transfer functions, spanning from S-shaped to X-shaped varieties, BinMGO is finely tuned to address 0–1 KP. After evaluating six BinMGO variants, the most effective one is identified. Acknowledging the limitations posed by transfer functions, BinMGO undergoes additional refinement, resulting in the developing of the Enhanced Binary Mountain Gazelle Optimizer (EBinMGO), employing multiple mutation techniques tailored specifically for addressing 0–1 KP. Thorough experimentation conducted on 0–1 KP datasets highlights EBinMGO's superiority over renowned swarm intelligence algorithms such as Ali Baba and the Forty Thieves (AFT), Prairie Dog Optimization Algorithm (PDO), Pelican Optimization Algorithm (POA), and Snake Optimizer (SO). The consistent proficiency demonstrated by EBinMGO in delivering superior outcomes across all experimental results positions EBinMGO as a promising solution for binary optimization challenges. Furthermore, this study provides valuable insights into mutation-based optimization algorithms, offering potential avenues for addressing complex problems inspired by nature's intricacies.

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Section: The Mountain Gazelle Optimizermentioning

confidence: 99%

The Enhanced Binary Mountain Gazelle Optimization Algorithm for 0-1 Knapsack Problems

BAS,

IHSAN

2024

Preprint

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“…Tissue-like P system is proposed on the basis of the cell-like P system. There is only one cell in cell-like P system, while tissue-like P system is composed of multiple cells [ 34 ]. Tissue-like P system also has three elements of membrane system, among which the connection between membranes plays a prominent role [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

An improved multi-view attention network inspired by coupled P system for node classification

Liu

2022

PLoS ONE

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Most of the existing graph embedding methods are used to describe the single view network and solve the single relation in the network. However, the real world is made up of networks with multiple views of complex relationships, and the existing methods can no longer meet the needs of people. To solve this problem, we propose a novel multi-view attention network inspired by coupled P system(MVAN-CP) to deal with node classification. More specifically, we design a multi-view attention network to extract abundant information from multiple views in the network and obtain a learning representation for each view. To enable the views to collaborate, we further apply attention mechanism to facilitate the view fusion process. Taking advantage of the maximum parallelism of P system, the process of learning and fusion will be realized in the coupled P system, which greatly improves the computational efficiency. Experiments on real network data sets indicate that our model is effective.

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“…Mengistu et al [ 38 ] modeled the virtual machine placement problem in volunteer cloud computing as a bounded 0-1 multidimensional knapsack problem and developed three heuristic-based algorithms to meet the objectives and constraints specific to volunteer cloud computing. Some methods applied to solve the knapsack problem are a tissue P system with cell division [ 39 ] and a quantum-inspired binary wolf pack algorithm [ 40 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

Olivas

Amaya

Ortíz-Bayliss

et al. 2021

Computational Intelligence and Neuroscience

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Hyperheuristics rise as powerful techniques that get good results in less computational time than exact methods like dynamic programming or branch and bound. These exact methods promise the global best solution, but with a high computational time. In this matter, hyperheuristics do not promise the global best solution, but they promise a good solution in a lot less computational time. On the contrary, fuzzy logic provides the tools to model complex problems in a more natural way. With this in mind, this paper proposes a fuzzy hyperheuristic approach, which is a combination of a fuzzy inference system with a selection hyperheuristic. The fuzzy system needs the optimization of its fuzzy rules due to the lack of expert knowledge; indeed, traditional hyperheuristics also need an optimization of their rules. The fuzzy rules are optimized by genetic algorithms, and for the rules of the traditional methods, we use particle swarm optimization. The genetic algorithm will also reduce the number of fuzzy rules, in order to find the best minimal fuzzy rules, whereas traditional methods already use very few rules. Experimental results show the advantage of using our approach instead of a traditional selection hyperheuristic in 3200 instances of the 0/1 knapsack problem.

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Solving the 0-1 Knapsack Problem by Using Tissue P System With Cell Division

Cited by 14 publications

References 30 publications

The Enhanced Binary Mountain Gazelle Optimization Algorithm for 0-1 Knapsack Problems

The Enhanced Binary Mountain Gazelle Optimization Algorithm for 0-1 Knapsack Problems

An improved multi-view attention network inspired by coupled P system for node classification

Enhancing Hyperheuristics for the Knapsack Problem through Fuzzy Logic

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