Optimization & Operations Research
Tahereh Azizpour; Majid Yarahmadi
Abstract
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 ...
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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.
