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A multi-agent genetic algorithm with variable neighborhood search for resource investment project scheduling problems | IEEE Conference Publication | IEEE Xplore

A multi-agent genetic algorithm with variable neighborhood search for resource investment project scheduling problems


Abstract:

In this paper, the multi-agent genetic algorithm (MAGA) is combined with the variable neighborhood search (VNS) to solve resource investment project scheduling problems (...Show More

Abstract:

In this paper, the multi-agent genetic algorithm (MAGA) is combined with the variable neighborhood search (VNS) to solve resource investment project scheduling problems (RIPSPs). An agent, coded by a valid activity list and a capacity list, represents a candidate solution to the RIPSPs. All agents live in a lattice-like environment, with each agent fixed on a lattice point. To increase energies, a series of operators, namely crossover, mutation, competition, self-learning and a VNS, are designed. The effectiveness of the algorithm is demonstrated through experiments on Möhring instances, synthetic instances and generated instances of J10, J14 and J20. The tests results are satisfactory.
Date of Conference: 25-28 May 2015
Date Added to IEEE Xplore: 14 September 2015
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Conference Location: Sendai, Japan

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