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Learning automata-based solutions to the nonlinear fractional knapsack problem with applications to optimal resource
Ole-Christoffer Granmo1, B John Oommen, Svein Arild Myrer
1Department of Information and Communication Technology, Agder University College, 4876 Grimstad, Norway. ole.granmo@hia.no
Summary
This study introduces a novel team of learning automata (LA) to solve the nonlinear fractional knapsack problem, optimizing web resource allocation for information discovery and real-time sampling. The approach enhances current solutions by using informed guesses for near-optimal results.
Area of Science:
- Computer Science
- Operations Research
- Web Science
Background:
- Resource allocation is critical for web monitoring and real-time sampling.
- Existing solutions for these problems have limitations.
- The nonlinear fractional knapsack problem offers a framework for optimization.
Purpose of the Study:
- To apply a novel solution for the nonlinear fractional knapsack problem to two real-world web-based resource allocation challenges.
- To improve upon existing methods for web monitoring and real-time proportion estimation.
- To introduce a new scheme using learning automata (LA) for efficient problem-solving.
Main Methods:
- A team of deterministic learning automata (LA) was employed.
- The LA performed a controlled random walk on a discretized solution space.
- The approach was tested on web monitoring and real-time binomial proportion estimation scenarios.
Main Results:
- The proposed LA scheme effectively optimizes information discovery under constrained polling capacity.
- It enables efficient real-time allocation of sampling resources for estimating multiple binomial proportions.
- Experimental results show consistent improvement towards near-optimal solutions, adaptable to changing environments.
Conclusions:
- The novel learning automata scheme provides an efficient solution for previously unaddressed resource allocation problems.
- Discretization resolution is key to the precision of the proposed method.
- This approach offers a significant advancement in applying LA to practical web-based challenges.
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