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

作者：Arpaia P; Maisto D; Manna C^*

来源：Applied Soft Computing, 2011, 11(8): 4655-4666.

DOI：10.1016/j.asoc.2011.07.017

• Quantum computing
• Evolutionary algorithms
• Competitive learning
• MultipleFault Diagnosis

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.

• 出版日期2011-12