dc.contributor.author | Yazidi, Anis | |
dc.contributor.author | Hammer, Hugo Lewi | |
dc.date.accessioned | 2019-04-29T11:44:46Z | |
dc.date.accessioned | 2019-04-30T06:56:52Z | |
dc.date.available | 2019-04-29T11:44:46Z | |
dc.date.available | 2019-04-30T06:56:52Z | |
dc.date.issued | 2018-06-23 | |
dc.identifier.citation | Yazidi A, Hammer HL. Solving stochastic nonlinear resource allocation problems using continuous learning automata. Applied intelligence (Boston) . 2018:1-20 | en |
dc.identifier.issn | 0924-669X | |
dc.identifier.issn | 0924-669X | |
dc.identifier.issn | 1573-7497 | |
dc.identifier.uri | https://hdl.handle.net/10642/6990 | |
dc.description.abstract | This paper deals with the Stochastic Non-linear Fractional Equality
Knapsack (NFEK) problem which is a fundamental resource allocation problem
based on incomplete and noisy information [7, 8]. The NFEK problem
arises in many applications such as in web polling under polling constraints,
and in constrained estimation. The primary contribution of this paper is a continuous
Learning Automata (LA)-based, optimal, efficient and yet simple solution
to the NFEK problem. Our solution is distinct from the first-reported
optimal solution to the problem due to Granmo and Oommen [7, 8] which
resorts to utilizing multiple two-action discretized LA, organized in a hierarchical
manner which comes with extra implementation and computational
complexity. In this work, we present an optimal solution to the problem using
a continuous LA which does not involve mapping the materials onto a binary
hierarchy. As opposed to the traditional family of Reward-Inaction (R-1)
LA, our scheme is modified in order to accommodate non-absorbing barriers,
thus guaranteeing convergence to the optimal allocation. The experimental
results that we have presented for numerous simulations demonstrate the efficiency
of our scheme and its superiority compared to the state-of-the-art in
terms of peak performance. | en |
dc.language.iso | en | en |
dc.publisher | Springer Verlag | en |
dc.relation.ispartofseries | Applied Intelligence;November 2018, Volume 48, Issue 11 | |
dc.rights | This is a post-peer-review, pre-copyedit version of an article published in Applied Intelligence.The final authenticated version is available online at: http://dx.doi.org/10.1007/s10489-018-1201-7 | en |
dc.subject | Continuous learning automata | en |
dc.subject | Stochastic non-linear fractional equality Knapsack problems | en |
dc.subject | Resource allocations | en |
dc.title | Solving stochastic nonlinear resource allocation problems using continuous learning automata | en |
dc.type | Journal article | en |
dc.type | Peer reviewed | en |
dc.date.updated | 2019-04-29T11:44:46Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | http://dx.doi.org/10.1007/s10489-018-1201-7 | |
dc.identifier.cristin | 1608266 | |
dc.source.journal | Applied intelligence (Boston) | |