Abstract
Original scientific paper This paper introduces a proposal of design of Ant Colony Optimization algorithm paradigm using Hyper-Cube framework to solve the Software Project Scheduling Problem. This NP-hard problem consists in assigning tasks to employees in order to minimize the project duration and its overall cost. This assignment must satisfy the problem constraints and precedence between tasks. The approach presented here employs the Hyper-Cube framework in order to establish an explicitly multidimensional space to control the ant behaviour. This allows us to autonomously handle the exploration of the search space with the aim of reaching encouraging solutions.
Highlights
Every software project has its manager and this project manager needs to apply his knowledge to solve multiple problems
In this paper we present a proposal of a design of an Ant Colony Optimization (ACO) algorithm
We have presented a model to the resolution of the Software Project Scheduling Problem (SPSP) using an Max-Min Ant System (MMAS) algorithm and HC framework, which provides a well-defined structure with ndimensional space for the memoristic information determined by the pheromone
Summary
Every software project has its manager and this project manager needs to apply his knowledge to solve multiple problems. In addition the duration and cost of the project should be minimized [1] This is a NP-hard problem, with multiple combinatorial optimization issues and needs much time to be completed. A group of employees should be assigned to each task according to their personal skills This should be done in order to minimize the project duration and its overall cost. In ACO, a group of software components called artificial ants travel in a graph to find good solutions to a given optimization problem. This bio-inspired metaheuristic is able to approximately solve several NPhard combinatorial problems effectively [5 ÷ 8].
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