An edge detector based on parallel quantum-inspired evolutionary algorithm

This work proposes a hybrid parallel quantum-inspired evolutionary algorithm (PQEA) based on cost minimization technique for edge detection. Quantum-inspired evolutionary algorithm (QEA) is based on the concepts and principles of quantum computing such as qubits and superposition of states. By adopting qubit chromosome as a representation, QEA can represent a linear superposition of solutions due to its probabilistic representation. QEA is more suitable for parallel structure than the conventional evolutionary algorithms because of rapid convergence and good global search capability. We combine PQEA and the local search technique to solve the problem of edge detection. Experimental results show that the algorithm perform very well in terms of the quality of the final edge image, rate of convergence and robustness to noise.