A parallel solution for the 0–1 knapsack problem using firefly algorithm

Hajarian, Mohammad; Shahbahrami, Asadollah; Hoseini, Farnaz

doi:10.1109/csiec.2016.7482134

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…The objective is to acquire the required resources within the budget for purchasing computing resources. The classical 0-1 knapsack problem involves selecting items and a knapsack with known weights and values for each item [28]. Therefore, the task offloading problem can be modeled as a variant of the 0-1 knapsack problem.…”

Section: A Polynomial-time Optimal Pre-emptive Task Offloading Mechanismmentioning

confidence: 99%

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Lv,

Wang,

Wang

et al. 2024

Electronics

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With the exploration of next-generation network technology, visual internet of things (VIoT) systems impose significant computational and transmission demands on mobile edge computing systems that handle large amounts of offloaded video data. Visual users offload specific tasks to cloud or edge computing platforms to meet strict real-time requirements. However, the available scheduling and computational resources for offloading tasks constantly destroy the system’s reliability and efficiency. This paper proposes a mechanism for task offloading and resource optimization based on predictive perception. Firstly, we proposed two LSTM-based decision-making prediction methods. In resource-constrained scenarios, we improve resource utilization by encouraging edge devices to participate in task offloading, ensuring the completion of more latency-sensitive request tasks, and enabling predictive decision-making for task offloading. We propose a polynomial time optimal mechanism for pre-emptive decision task offloading in resource-abundant scenarios. We solve the 0–1 knapsack problem of offloading tasks to better meet the demands of low-latency tasks where the system’s available resources are not constrained. Finally, we provide numerical results to demonstrate the effectiveness of our scheme.

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Section: A Polynomial-time Optimal Pre-emptive Task Offloading Mechanismmentioning

confidence: 99%

Task Offloading and Resource Optimization Based on Predictive Decision Making in a VIoT System

Lv,

Wang,

Wang

et al. 2024

Electronics

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“… Evolutionary algorithm Solving single and multi-objective 01 Knapsack Problem using Harmony Search Algorithm Amol C. Adamuthe 1* , Vaishnavi N. Sale 1 , and Sandeep U. Mane 2  Genetic algorithm (Zhao et al, 2009;Pradhan et al, 2014)  Particle swarm optimization (Bansal & Deep, 2012;Li & Li, 2009; Ouyang &Wang, 2012)  Wolf Pack Algorithm (Gao et al, 2018)  firefly algorithm (Hajarian et al, 2016;Bhattacharjee & Sarmah, 2015)  shuffled frog leaping algorithm (Bhattacharjee & Sarmah, 2014) Genetic algorithms, particle swarm optimization, differential evolution are popular heuristic/meta-heuristic algorithms. Harmony search algorithm is a population-based algorithm that imitates the music improvisation process used by the musicians.…”

Section: Introductionmentioning

confidence: 99%

Solving single and multi-objective 01 Knapsack Problem using Harmony Search Algorithm

Adamuthe¹,

Sale²,

Mane³

2020

JSR

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Knapsack problem is finding the optimal selection of objects to get maximum profit. Knapsack problem has a wide range of application in different domain such as production, transportation, resource management etc. Knapsack problem varies with change in number of items and number of objectives. 01 knapsack problem is reported as a classical optimization problem under NP category. Harmony search (HS) algorithm is a popular heuristic algorithm investigated to solve different optimization problems. This paper presents harmony search for solving single objective and multi-objective knapsack problem. Performance of HS is tested with 43 instances of single objective knapsack problem taken from three datasets. HS provides optimal results except for three instances. 46 instances of 01 knapsack problem with three, four and five objectives are tested. Experiments show that better results are obtained with an increase in harmony memory with better exploration in objectives.

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