Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and en...Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems(LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network(GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm,through comparative analysis with state-of-the-art peer algorithms.展开更多
OFDM-CDMA is an attractive technique for broadband wireless communication. However, the high peakto-average power ratio （PAPR） of the downlink signals, generated from multiple spread codes, remains a serious problem...OFDM-CDMA is an attractive technique for broadband wireless communication. However, the high peakto-average power ratio （PAPR） of the downlink signals, generated from multiple spread codes, remains a serious problem. In this paper, a low-complexity multiple signal representation （MSR） scheme is proposed to control the PAPR problem in downlink OFDM-CDMA systems. The proposed scheme generates multiple candidate signals by a novel user grouping scheme, which is without distortion and can provide more PAPR reduction than the conventional MSR schemes, such as partial transmit sequence （PTS） and selective mapping （SLM）. Furthermore, a low-complexity processing structure is developed using a novel joint spreading and inverse fast Fourier transform （S-IFFT） to simplify the generation of multiple candidate signals. Complexity analysis and numerical results show that the OFDM-CDMA systems employing the proposed scheme have better tradeoff between PAPR reduction and computational complexity, compared with the conventional MSR schemes.展开更多
基金supported in part by the National Natural Science Foundation of China(62506083, 62476096)Guangdong Provincial Construction Project of Teaching Quality and Teaching Reform Engineering in Undergraduate Universities ([2024] No. 30)
文摘Evolutionary multitasking optimization(EMTO) can obtain beneficial knowledge for the target task from the auxiliary task to improve its performance, which has received extensive attention in scientific research and engineering problems. Nevertheless, faced with the widespread large-scale multi-objective optimization problems(LSMOPs), the existing EMTO literature barely involves the research of LSMOPs. More importantly, these EMTO algorithms often get trapped in local optima when dealing with LSMOPs, resulting in a slow convergence speed, which is worthy of our attention. To this end, this paper proposes an EMTO algorithm dedicated to solving LSMOPs. On the one hand, given the intricate nature of LSMOPs, we propose a knowledge domination-based knowledge transfer mechanism that can flexibly transfer knowledge from multiple knowledge representations, i.e., the information distribution and distribution distance of the task population. On the other hand, we design an elite vector-guided search strategy. Specifically, the generative adversarial network(GAN) model should first be trained within the divided populations. Then, the well-trained model is used to generate a high-quality individual for the target individual. After that, the high-quality individual is combined with the top-performing individual in the current population to find the elite vector corresponding to the target individual. Finally, the elite vector is applied to guide the target individual to accelerate convergence towards the global optimum in the high-dimensional decision space. We conduct comprehensive experimental investigations on two artificial LSMOPs suites and six real-world LSMOPs to validate the efficiency and robustness of the proposed algorithm,through comparative analysis with state-of-the-art peer algorithms.
基金Supported in part by DoCoMo Beijing Labs Co., Ltd., International Sci. & Tech. Cooperation Project of the Ministry of Sci. & Tech. of China(Grant No. 2008DFA11700)the National Natural Science Foundation of China (Grant Nos. 60902026, 60602008)
文摘OFDM-CDMA is an attractive technique for broadband wireless communication. However, the high peakto-average power ratio （PAPR） of the downlink signals, generated from multiple spread codes, remains a serious problem. In this paper, a low-complexity multiple signal representation （MSR） scheme is proposed to control the PAPR problem in downlink OFDM-CDMA systems. The proposed scheme generates multiple candidate signals by a novel user grouping scheme, which is without distortion and can provide more PAPR reduction than the conventional MSR schemes, such as partial transmit sequence （PTS） and selective mapping （SLM）. Furthermore, a low-complexity processing structure is developed using a novel joint spreading and inverse fast Fourier transform （S-IFFT） to simplify the generation of multiple candidate signals. Complexity analysis and numerical results show that the OFDM-CDMA systems employing the proposed scheme have better tradeoff between PAPR reduction and computational complexity, compared with the conventional MSR schemes.
