Abstract
This article presents the application of Virtual Savant to solve resource allocation problems, a widely-studied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given problem from the solutions computed by a reference algorithm, and its design allows taking advantage of modern parallel computing infrastructures. The proposed approach is evaluated to solve the Knapsack Problem, which models different variant of resource allocation problems, considering a set of instances with varying size and difficulty. The experimental analysis is performed on an Intel Xeon Phi many-core server. Results indicate that Virtual Savant is able to compute accurate solutions while showing good scalability properties when increasing the number of computing resources used.
Highlights
Resource allocation refers to the assignment of a number of available resources or assets to different issues or items
This article describes a generic paradigm that proposes applying a computational intelligence approach to find accurate solutions to resource allocation problems modeled by the 0/1 Knapsack Problem in short computation times. 0/1 Knapsack Problem is a binary version of the Knapsack Problem where each item is considered as an atomic unit, i.e., each item can be included in the knapsack as a unit or discarded
A many-core computing infrastructure was used in the experimental analysis, in order to evaluate the capabilities of Virtual Savant to compute accurate results over a massively parallel platform
Summary
Resource allocation refers to the assignment of a number of available resources or assets to different issues or items. This article describes a generic paradigm that proposes applying a computational intelligence approach to find accurate solutions to resource allocation problems modeled by the 0/1 Knapsack Problem in short computation times. The Virtual Savant paradigm proposes applying a learning approach using computational intelligence to predict the results computed by a reference algorithm that solves a given problem [7,3]. Once the training phase is completed, Virtual Savant can be applied to solve new, unknown, and even larger problem instances In this way, the Virtual Savant paradigm aims at learning the behavior of a given resolution algorithm in order to generate a completely different program that reproduces an analogous but unknown process to compute accurate results for the same problem.
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