A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault Diagnosis

Authors:
P. Arpaia;D. Maisto;C. Manna
Affiliations:
Dipartimento di Ingegneria, Universití del Sannio, CERN - European Laboratory for Nuclear Research, Department of Technologies, Group of Magnets, Superconductors and Cryostats, M26220, (30-03 ...;Institute of High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Via P. Castellino 11, 80131 Naples, Italy;Dipartimento di Ingegneria Elettrica, Universití degli Studi di Napoli Federico II, Via Claudio 21, 80125 Naples, Italy
Venue:
Applied Soft Computing
Year:
2011

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Abstract

A heuristic search algorithm, the Quantum-inspired Competitive Evolutionary Algorithm (QuCEA), based on both quantum and evolutionary computing, is proposed. The individuals of a population, coded as qubit strings, evolve by means of an original variation operator inspired by competitive learning. The proposed operator is application independent and intuitively controllable by a single real parameter. QuCEA has been applied to Multiple-Fault Diagnosis, a typical NP-hard problem for industrial diagnosis. In particular, the proposed algorithm gives remarkable results both in simulation and in on-field tests for a lift monitoring system, also in comparison with a standard genetic algorithm and a state-of-the-art Quantum-inspired Evolutionary Algorithm.