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

Adamuthe, Amol C.; Sale, Vaishnavi N.; Mane, Sandeep U.

doi:10.37398/jsr.2020.640136

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…The obtained solutions are compared with the harmony search algorithm (Adamuthe et al, 2020) and the optimal solution. Table II shows the optimal profit, profit obtained by the harmony search algorithm, profit obtained by the flower pollination algorithm and its variations which include FPA with crossover and mutation (Version 1), FPA with crossover (Version 2) & FPA with mutation (Version 3).…”

Section: Dataset Results and Discussionmentioning

confidence: 99%

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

Sundarkar¹,

Adamuthe²

2021

JSR

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The 01 knapsack problem is a combinatorial optimization problem where objective is to maximum profit. It can be considered as a resource allocation problem where the decision is to pick the most important items. Knapsack problem has a large range of applications in many domains. This paper presents three variations of FPA influenced by concept of crossover and mutation operator in genetic algorithms. The three variations, named FPA with crossover and mutation (Version 1), FPA with crossover (Version 2), FPA with mutation (Version 3) for solving 01 knapsack problem are presented. Comparison of variations is done with basic FPA algorithms and other algorithms for three datasets of a single dimension of 01 knapsack problem. The results show that improved FPA has better results than basic FPA. The results are optimal for smaller instances and with increase in number of objects results are closer to optimal but not optimal.

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Section: Dataset Results and Discussionmentioning

confidence: 99%

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

Sundarkar¹,

Adamuthe²

2021

JSR

Self Cite

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“…In the single Knapsack problem [35], items with different weights and values are given, in addition to the Knapsack capacity. The main objective is to pick items that maximise the profit formula while taking into consideration weight limits.…”

Section: A Problem Definitionmentioning

confidence: 99%

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

Chahoud¹,

Sami²,

Mourad³

et al. 2022

Preprint

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“…In the single knapsack problem [18], items with different weights and values are given, and a knapsack capacity is given. The main objective is to pick items that maximize the profit formula while taking into consideration weight limits.…”

Section: Problem Definitionmentioning

confidence: 99%

On-Demand Client Deployment And Selection In Federated Learning. (c2022)

Chahoud¹

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Traditional machine learning models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated learning plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients on the fly wherever and whenever needed. In fact, some devices are not available to serve as clients in the federated learning due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in federated learning offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed.

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

Cited by 9 publications

References 18 publications

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

Solving 01 Knapsack Problem with variations of Flower Pollination Algorithm

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

On-Demand Client Deployment And Selection In Federated Learning. (c2022)

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