A Quantum-inspired Evolutionary Algorithm with a competitive variation operator for Multiple-Fault 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 …
(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 …
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.
Elsevier