In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is des...In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.展开更多
蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变...蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变机制增强种群多样性,引入Beta分布动态生成反射解决方案以探索更广的搜索空间,并采用莱维飞行处理边界违规问题。进一步融合鲸鱼优化算法的螺旋搜索更新机制,结合随机策略更新位置,显著提升了算法的收敛精度和鲁棒性。当算法陷入停滞时,引入t分布扰动变异策略,有效提高了算法跳出局部最优解的能力。通过在17个基准测试函数验证了改进策略的有效性。此外,本文还将ODBO应用于车间排产调度问题,进一步证实了其在解决实际工程问题中的有效性和可靠性。The Dung Beetle Optimization algorithm (DBO), as an emerging intelligent optimization algorithm, shows great potential in solving complex optimization problems. However, its limitations in convergence precision and susceptibility to local optima hinder its broader application. This paper proposes an improved multi-strategy Dung Beetle Optimization algorithm (ODBO), which enhances population diversity through good point set initialization and periodic mutation mechanisms. A Beta distribution is introduced to dynamically generate reflected solutions to explore a wider search space, and Lévy flight is applied to handle boundary violation issues. Additionally, the spiral search update mechanism from the Whale Optimization Algorithm is integrated, along with random strategy updates for position, significantly improving the algorithm’s convergence accuracy and robustness. When the algorithm stagnates, a t-distribution mutation perturbation strategy is introduced, effectively enhancing its ability to escape local optima. Simulations on 17 benchmark functions test functions validate the effectiveness of the improved strategies. Moreover, the application of ODBO to the job-shop scheduling problem confirms its effectiveness and reliability in solving real-world engineering problems.展开更多
基金The National Natural Science Foundation of China (No.60872075)the National High Technology Research and Development Program of China (863 Program) (No. 2008AA01Z227)
文摘In order to raise the detection precision of the extended binary phase shift keying （EBPSK） receiver, a detector based on the improved particle swarm optimization algorithm （IMPSO） and the BP neural network is designed. First, the characteristics of EBPSK modulated signals and the special filtering mechanism of the impacting filter are demonstrated. Secondly, an improved particle swarm optimization algorithm based on the logistic chaos disturbance operator and the Cauchy mutation operator is proposed, and the EBPSK detector is designed by utilizing the IMPSO-BP neural network. Finally, the simulation of the EBPSK detector based on the MPSO-BP neural network is conducted and the result is compared with that of the adaptive threshold-based decision, the BP neural network, and the PSO-BP detector, respectively. Simulation results show that the detection performance of the EBPSK detector based on the IMPSO-BP neural network is better than those of the other three detectors.
文摘蜣螂优化算法(DBO)作为一种新兴的智能优化算法,在求解复杂优化问题中显示出巨大潜力。然而,其在收敛精度和易陷入局部最优方面的局限性限制了其应用范围。本文提出了一种多策略改进的蜣螂优化算法(ODBO),通过佳点集群初始化和周期突变机制增强种群多样性,引入Beta分布动态生成反射解决方案以探索更广的搜索空间,并采用莱维飞行处理边界违规问题。进一步融合鲸鱼优化算法的螺旋搜索更新机制,结合随机策略更新位置,显著提升了算法的收敛精度和鲁棒性。当算法陷入停滞时,引入t分布扰动变异策略,有效提高了算法跳出局部最优解的能力。通过在17个基准测试函数验证了改进策略的有效性。此外,本文还将ODBO应用于车间排产调度问题,进一步证实了其在解决实际工程问题中的有效性和可靠性。The Dung Beetle Optimization algorithm (DBO), as an emerging intelligent optimization algorithm, shows great potential in solving complex optimization problems. However, its limitations in convergence precision and susceptibility to local optima hinder its broader application. This paper proposes an improved multi-strategy Dung Beetle Optimization algorithm (ODBO), which enhances population diversity through good point set initialization and periodic mutation mechanisms. A Beta distribution is introduced to dynamically generate reflected solutions to explore a wider search space, and Lévy flight is applied to handle boundary violation issues. Additionally, the spiral search update mechanism from the Whale Optimization Algorithm is integrated, along with random strategy updates for position, significantly improving the algorithm’s convergence accuracy and robustness. When the algorithm stagnates, a t-distribution mutation perturbation strategy is introduced, effectively enhancing its ability to escape local optima. Simulations on 17 benchmark functions test functions validate the effectiveness of the improved strategies. Moreover, the application of ODBO to the job-shop scheduling problem confirms its effectiveness and reliability in solving real-world engineering problems.
