Designing a New Continuous Quantum Evolutionary Algorithm for Nonlinear Optimization and Efficiency Frontier Evaluation

In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. ‎ Traditional quantum evolutionary algorithms have primarily been implemented for discrete and binary decision variables‎. ‎The proposed method has been designed as a novel continuous quantum evolutionary optimization algorithm tailored to problems with continuous decision variables‎. ‎ To assess the algorithm’s performance, several numerical experiments are conducted‎, ‎and the simulated results are compared with the Grey Wolf Optimizer and Magnet Fish Optimization search algorithm‎. ‎The simulation results indicate that the proposed algorithm can approximate the optimal solution more accurately than the two compared algorithms.

کلیدواژه ها:

Convex optimization‎ ، ‎Non-Convex optimization‎ ، ‎Evolution algorithm‎ ، ‎Quantum evolution algorithm‎ ، ‎Efficient frontier‎ ، ‎Markowitz efficient frontier