A quantum evolutionary algorithm inspired by manta ray foraging optimisation
Shikha Gupta, Naveen Kumar V · International Journal of Computational Science and Engineering · 2024
Manta ray foraging optimisation (MRFO) algorithm, a recent bio-inspired technique, and quantum-motivated computing have proven effective in solving complex combinatorial optimisation problems. Leveraging their qualities, we propose a continuous space optimisation approach that offers a novel combination of encoding and evolution of the chromosomes. The qubits in the quantum individual are encoded with the phase parameters and are based on Bloch representation. The phase angle-encoded qubit simplifies the expression and evolution of an individual. The proposed algorithm can search the optimised solution simultaneously on three coordinate axes of the Bloch sphere, to possibly achieve better convergence. The performance of the proposed algorithm is examined vis-a-vis the standard MRFO algorithm for optimising the value of 20 benchmark functions. While both algorithms compete well in finding the best fitness values, the proposed approach shows better convergence for 16 out of 20 functions.