Article
Engineering, Multidisciplinary
Shutong Xie, Zongbao He, Xingwang Huang
Summary: The multi-pass turning operation is a commonly used machining method in the manufacturing field, aiming to minimize the unit production cost. This paper proposes a Gaussian quantum-behaved bat algorithm (GQBA) to solve this problem. The algorithm incorporates the current optimal positions of quantum bats and the global best position to facilitate population diversification, and uses a Gaussian distribution to update the positions for more accurate search. Experimental results show that GQBA has better search capability and outperforms other algorithms in terms of cost reduction.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2023)
Article
Computer Science, Information Systems
Francois Xavier Rugema, Gangui Yan, Sylvere Mugemanyi, Qi Jia, Shanfeng Zhang, Christophe Bananeza
Summary: A novel Cauchy-Gaussian quantum-behaved bat algorithm (CGQBA) is proposed in this paper to solve the economic load dispatch (ELD) problem by integrating quantum mechanics theories and Gaussian and Cauchy operators into the standard bat algorithm. Experimental results show that CGQBA is effective and superior to many other algorithms.
Article
Engineering, Multidisciplinary
Qidong Chen, Jun Sun, Vasile Palade, Xiaojun Wu, Xiaoqian Shi
Summary: The proposed IG-QPSO algorithm utilizes a modified Gaussian distribution to generate random numbers, balancing global and local search abilities. Experimental results demonstrate that IG-QPSO outperforms competitors in terms of precision and robustness on both standard benchmark functions and engineering shape design problems.
ENGINEERING OPTIMIZATION
(2022)
Article
Mathematics
Yeerjiang Halimu, Chao Zhou, Qi You, Jun Sun
Summary: This paper proposes a quantum-behaved particle swarm optimization (QPSO) algorithm on Riemannian manifolds named RQPSO to solve the issues of non-convex manifold global convergence and non-differentiable mathematical models. Experimental results show that RQPSO outperforms traditional algorithms in terms of calculation speed and optimization efficiency.
Article
Physics, Multidisciplinary
Xin Cheng, Xiu-Juan Lu, Ya-Nan Liu, Sen Kuang
Summary: Four intelligent optimization algorithms, including DE, PSO, QPSO, and QEA, are compared in searching for control pulses for target quantum state preparation in closed and open quantum systems. Their control performance is compared and their differences are pointed out. The robustness of control pulses found by these four algorithms is also demonstrated and compared for uncertain quantum systems. The research shows that QPSO outperforms the other three algorithms in all the considered performance criteria, making it a powerful optimization tool for solving complex quantum control problems.
Article
Engineering, Mechanical
Ming-Wei Li, Yu-Tain Wang, Jing Geng, Wei-Chiang Hong
Summary: The paper proposes a hybrid optimization algorithm, the Chaotic Cloud Quantum Bats Algorithm (CCQBA), which improves performance by enhancing evolution mechanism, local search mechanism, mutation mechanism, and other aspects. Compared to alternative algorithms, CCQBA demonstrates significantly better convergence accuracy and speed, making it a superior method for solving complex problems.
NONLINEAR DYNAMICS
(2021)
Article
Computer Science, Artificial Intelligence
Yiying Zhang
Summary: This paper proposes an improved version of backtracking search algorithm called GMPBSA, which introduces generalized mean positions and a comprehensive learning mechanism to enhance the global search ability of BSA. Experimental results show the great potential of GMPBSA in solving challenging multimodal optimization problems.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Computer Science, Information Systems
Xiaotong Li, Wei Fang, Shuwei Zhu
Summary: This research proposes an improved BQPSO algorithm to solve the 0-1 knapsack problem. The algorithm optimizes the discretization issue by introducing a mapping strategy and a transfer function. It also addresses infeasible solutions and local optima problems with a new repair method and a diversity maintenance mechanism. Experimental results show its superiority over other ten algorithms in terms of convergence speed and search performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Abdullah Alharbi, Wael Alosaimi, Hashem Alyami, Hafiz Tayyab Rauf, Robertas Damasevicius
Summary: A novel method for effectively detecting botnet attacks in the IoT environment was proposed in this study, achieving efficient detection and classification through the use of bat algorithm and neural networks. Experimental results demonstrated the superior performance of the LGBA-NN algorithm in multi-class botnet attack detection.
Article
Computer Science, Interdisciplinary Applications
Caiyang Yu, Mengxiang Chen, Kai Cheng, Xuehua Zhao, Chao Ma, Fangjun Kuang, Huiling Chen
Summary: The SGOA, an improved grasshopper optimization algorithm combining simulated annealing mechanism with the original GOA, outperformed other algorithms in various fields and engineering problems. With promising results in benchmark function testing and engineering applications, SGOA proves to be effective in solving complex optimization problems.
ENGINEERING WITH COMPUTERS
(2022)
Article
Automation & Control Systems
Jing Bi, Haitao Yuan, Jiahui Zhai, MengChu Zhou, H. Vincent Poor
Summary: This work proposes an improved self-adaptive bat algorithm with genetic operations (SBAGO) that combines genetic algorithm (GA) and bat algorithm (BA) in a highly integrated way. SBAGO utilizes the search information of BA to perform GA's genetic operations, resulting in improved search performance. Experimental results show that SBAGO outperforms other algorithms in various metrics.
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
(2022)
Article
Computer Science, Software Engineering
Kang Liang, Xiukai Zhang, Oleg Krakhmalev
Summary: SLSL-QPSO is a software that improves the Quantum-behaved Particle Swarm Optimization (QPSO) algorithms by leveraging the concept of living and death as swarm layers. Experimental results demonstrate its superior performance in finding better optimal solutions compared to other algorithms.
Article
Automation & Control Systems
Song Liu, Shumin Zhou, Xiujuan Lu, Fang Gao, Feng Shuang, Sen Kuang
Summary: This paper presents a Lyapunov control scheme to drive finite-dimensional closed and Markovian open quantum systems into any target pure state with high fidelity and short time. The control law is established using a Lyapunov function and the optimal eigenvalues are searched using the quantum-behaved particle swarm optimization algorithm. A improved constrained QPSO algorithm is proposed for open systems with small denominator in the control law. Numerical simulations on different quantum systems demonstrate the effectiveness of the proposed control scheme.
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS
(2023)
Article
Computer Science, Artificial Intelligence
S. Mohan, Akash Sinha
Summary: This paper proposes a novel method for performing nondominated sorting in a multiobjective optimization problem using a modified directional Bat algorithm. Unlike NSGA-II, the proposed algorithm generates and compares new solutions with all previous solutions, reducing computational time and generating diverse solutions. A unique sorting method using a Nondomination matrix is introduced, which can be easily updated to include new solutions and preserve elitism. Detailed criteria are provided for the selection of new solutions. Experimental results show that the proposed algorithm is competitive and outperforms other algorithms in terms of efficiency and other performance metrics for most benchmark optimization problems. The algorithm also provides a standardized platform for nondomination sorting, applicable to any other metaheuristic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Jahedul Islam, Amril Nazir, Md Moinul Hossain, Hitmi Khalifa Alhitmi, Muhammad Ashad Kabir, Abdul-Halim M. Jallad
Summary: The study introduces a Surrogate Assisted Quantum-behaved Algorithm to address the challenges in optimizing well placement in the oil and gas industry. By utilizing various metaheuristic optimization techniques, the proposed approach demonstrates superior performance in two complex reservoirs, providing a better net present value and resolving the issue of inconsistency seen in other optimization methods.