Document Type : Research Article
Authors
Department of Mathematics and Computer Science, Lorestan University, Lorestan, 68151-44316, Iran.
10.30473/coam.2025.74960.1316
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 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.
Highlights
- Introduction of a novel Continuous Quantum Evolutionary Algorithm (CQEA) designed to determine the Markowitz efficient frontier.
- Standard quantum evolutionary algorithms struggle with continuous optimization.
- CQEA enhances the observer operation to solve a broader class of continuous optimization problems.
- CQEA’s technical characteristics and performance are evaluated using established test functions.
- Assessment covers both unimodal and multimodal functions to gauge convergence and robustness.
- Two performance indices are computed to demonstrate the convergence behavior of CQEA.
- Demonstrates the algorithm on three representative problems: convex, non-convex, and high-dimensional efficient-evaluation optimization tasks.
- Two high-dimensional test cases illustrate the practical applicability of CQEA.
Keywords
- Convex optimization
- Non-Convex optimization
- Evolution algorithm
- Quantum evolution algorithm
- Efficient frontier
- Markowitz efficient frontier
Main Subjects
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