2000 character limit reached
Halfway Escape Optimization: A Quantum-Inspired Solution for General Optimization Problems (2405.02850v7)
Published 5 May 2024 in cs.NE, cs.AI, and math.OC
Abstract: This paper first proposes the Halfway Escape Optimization (HEO) algorithm, a quantum-inspired metaheuristic designed to address general optimization problems. The HEO mimics the effects between quantum such as tunneling, entanglement. After the introduction to the HEO mechansims, the study presents a comprehensive evaluation of HEO's performance against extensively-used optimization algorithms, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Fish Swarm Algorithm (AFSA), Grey Wolf Optimizer (GWO), and Quantum behaved Particle Swarm Optimization (QPSO). The primary analysis encompasses 14 benchmark functions with dimension 30, demonstrating HEO's effectiveness and adaptability in navigating general optimization problems. The test of HEO in Pressure Vessel Design and Tubular Column Design also infers its feasibility and potential in real-time applications. Further validation of HEO in Osmancik-97 and Cammeo Rice Classification achieves a higher accuracy record.
- (2022). Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm. Advanced Engineering Informatics, 51, 101536. doi:https://doi.org/10.1016/j.aei.2022.101536.
- Solution of ackley function based on particle swarm optimization algorithm, . URL: https://ieeexplore.ieee.org/document/9213634.
- The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6, 58–73. doi:10.1109/4235.985692.
- Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 26, 29–41. doi:10.1109/3477.484436.
- Abstract Algebra. Englewood Cliffs, NJ: Prentice Hall.
- A new optimizer using particle swarm theory, . doi:10.1109/mhs.1995.494215.
- Elegbede, C. (2005). Structural reliability assessment based on particles swarm optimization. Structural Safety, 27, 171–186. URL: https://www.sciencedirect.com/science/article/pii/S0167473004000499. doi:https://doi.org/10.1016/j.strusafe.2004.10.003.
- ELGebaly, A. E. (2019). Optimized design of single tum transformer of distributed static series compensators using fem based on ga. In 2019 21st International Middle East Power Systems Conference (MEPCON) (pp. 1133–1138). doi:10.1109/MEPCON47431.2019.9008227.
- Glover, F. (1986). Future paths for integer programming and links to artificial intelligence. Computers & Operations Research, 13, 533–549. doi:10.1016/0305-0548(86)90048-1.
- Grefenstette, J. J. (1986). Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man, and Cybernetics, 16, 122–128. doi:10.1109/TSMC.1986.289288.
- Adaptive gradient multiobjective particle swarm optimization. IEEE Transactions on Cybernetics, 48, 3067–3079. doi:10.1109/TCYB.2017.2756874.
- Holland, J. H. (1975). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Complex Adaptive Systems.
- Karp, R. M. (2010). Reducibility among combinatorial problems. In M. Jünger, T. M. Liebling, D. Naddef, G. L. Nemhauser, W. R. Pulleyblank, G. Reinelt, G. Rinaldi, & L. A. Wolsey (Eds.), 50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art (pp. 219–241). Berlin, Heidelberg: Springer Berlin Heidelberg. URL: https://doi.org/10.1007/978-3-540-68279-0_8. doi:10.1007/978-3-540-68279-0_8.
- Optimization by simulated annealing. Science, 220, 671–680. URL: https://www.science.org/doi/abs/10.1126/science.220.4598.671. doi:10.1126/science.220.4598.671. arXiv:https://www.science.org/doi/pdf/10.1126/science.220.4598.671.
- An optimizing method based on autonomous animats: Fish-swarm algorithm. Systems Engineering - Theory and Practice, 22, 32--38. doi:10.12011/1000-6788(2002)11-32.
- Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. doi:10.1016/j.advengsoft.2017.07.002.
- Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. doi:10.1016/j.advengsoft.2013.12.007.
- Quantum behaved particle swarm optimization (qpso) for multi-objective design optimization of composite structures. Expert Systems with Applications, 36, 11312--11322. URL: https://www.sciencedirect.com/science/article/pii/S0957417409002978. doi:https://doi.org/10.1016/j.eswa.2009.03.006.
- Solar-dg and dstatcom concurrent planning in reconfigured distribution system using apso and gwo-pso based on novel objective function. Energies, 16. URL: https://www.mdpi.com/1996-1073/16/1/263. doi:10.3390/en16010263.
- Sommerfeld, A. (1916). Zur quantentheorie der spektrallinien. Annalen der Physik, 356, 1–94. doi:10.1002/andp.19163561702.
- Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization, 11, 341–359. doi:10.1023/a:1008202821328.
- Grid-based ga path planning with improved cost function for an over-actuated hover-capable auv. In 2014 IEEE/OES Autonomous Underwater Vehicles (AUV) (pp. 1--8). doi:10.1109/AUV.2014.7054426.
- Comparative study of particle swarm optimization variants in complex mathematics functions. Studies in computational intelligence, (p. 223–235). doi:10.1007/978-3-642-33021-6_18.
- cpso-cnn: An efficient pso-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm and Evolutionary Computation, 49, 114--123. URL: https://www.sciencedirect.com/science/article/pii/S2210650218310083. doi:https://doi.org/10.1016/j.swevo.2019.06.002.
- Yang, X.-S. (2009). Firefly algorithms for multimodal optimization, . (p. 169–178). doi:10.1007/978-3-642-04944-6_14.