Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks

Zhu, Wenchao; Xu, Pingping; Bui, ThiOanh; Wu, Guilu; Yang, Yan

doi:10.1007/s11432-016-9143-6

Cited by 6 publications

(7 citation statements)

References 21 publications

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“…Where four original algorithms are GBO, 76 MFO, 57 DE, 91 BA, 104 and eight advanced algorithms are CCMWOA, 94 HGWO, 105 CMFO, 106 SCADE, 107 m_SCA, 108 LGMSFOA, 109 CGSCA. 110 Table 5 displays the test data on GOMGBO and the rest algorithms. Avg is the average test result.…”

Section: Comparison Against Other Competitive Peersmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

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Gradient‐based optimizer (GBO) is a metaphor‐free mathematic‐based algorithm proposed in recent years. Encouraged by the gradient‐based Newton's method, this algorithm combines with population‐based evolutionary methods. The disadvantage of the traditional GBO algorithm is that the global search ability of the algorithm is too strong, and the local search ability is too weak; accordingly, it is difficult to obtain the global optimal solution efficiently. Therefore, a new improved GBO algorithm (GOMGBO) is developed to mitigate such performance concerns by introducing a Gaussian bare‐bones mechanism, an opposition‐based learning mechanism, and a moth spiral mechanism enhanced GBO algorithm. The proposed GOMGBO has been compared against many famous methods and improved variants on 30 benchmark functions. The experimental results show that GOMGBO has apparent advantages in convergence speed and precision. In addition, this paper analyzes the balance and diversity of the GOMGBO algorithm and compares GOMGBO with other algorithms on several engineering problems. The experimental results show that the GOMGBO algorithm is also better than the competitive algorithm in engineering problems. This study uses the GOMGBO algorithm to optimize kernel extreme learning machine (KELM), and a new GOMGBO‐KELM model is proposed. The model is used to deal with four clinical disease diagnosis problems. Compared with GBO‐KELM, back propagation neural network algorithm, and other models, comparative experiments show that GOMGBO‐KELM has high performance in dealing with practical cases. We invite the community to investigate further our method for solving problems more efficiently with reasonable speed and efficiency. Readers of this study can refer to https://aliasgharheidari.com for any guidance about the proposed GOMGBO method.

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Section: Comparison Against Other Competitive Peersmentioning

confidence: 99%

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Qiao

Shan

Jiang

et al. 2021

Int J of Intelligent Sys

View full text Add to dashboard Cite

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“…To further evaluate the performance of MOLGOA, this impressive method was compared with other sophisticated algorithms in this chapter, including improved GOA (IGOA) [37], CSSA [63], ESSA [64], CGSCA [65], m_SCA [66], LWOA [67], BWOA [4], CCMWOA [5], CLPSO [68], and GL25 [69]. e detailed illustration of the comparison functions and the parameters involved in the experiment are listed in Table 3.…”

Section: Comparison With the State-of-e-art Algorithmsmentioning

confidence: 99%

An Improved Grasshopper Optimizer for Global Tasks

et al. 2020

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The grasshopper optimization algorithm (GOA) is a metaheuristic algorithm that mathematically models and simulates the behavior of the grasshopper swarm. Based on its flexible, adaptive search system, the innovative algorithm has an excellent potential to resolve optimization problems. This paper introduces an enhanced GOA, which overcomes the deficiencies in convergence speed and precision of the initial GOA. The improved algorithm is named MOLGOA, which combines various optimization strategies. Firstly, a probabilistic mutation mechanism is introduced into the basic GOA, which makes full use of the strong searchability of Cauchy mutation and the diversity of genetic mutation. Then, the effective factors of grasshopper swarm are strengthened by an orthogonal learning mechanism to improve the convergence speed of the algorithm. Moreover, the application of probability in this paper greatly balances the advantages of each strategy and improves the comprehensive ability of the original GOA. Note that several representative benchmark functions are used to evaluate and validate the proposed MOLGOA. Experimental results demonstrate the superiority of MOLGOA over other well-known methods both on the unconstrained problems and constrained engineering design problems.

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“…As evidenced in the above analysis (Eqs. (6) and (15)), enlarging the number of sleeping sBSs in the system increases the number of user requests at each BS, and hence lengthens the queue [24]. Additionally, at the given traffic arrival rate, the delays in the video and file contents are proportional to the corresponding queue lengths (Eqs.…”

Section: Mean Delaymentioning

confidence: 99%

Delay-constrained sleeping mechanism for energy saving in cache-aided ultra-dense network

Pei

Gong

Gao

et al. 2019

Sci. China Inf. Sci.

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We investigate an energy-saving sleeping mechanism in a cache-aided ultra-dense network (UDN) with delay constraints. As in existing works, we consider the video and file contents of the UDN. The video contents are cached at and delivered by a small-cell base-station (sBS). The cache-aided sBS cooperates with a macro-cell base-station (mBS) to service the file contents. The optimal sleeping strategy that conserves energy under the delay constraint is formulated as an energy-consumption minimization problem under the network stability condition with a guaranteed delay constraint. To find its solution, the minimization problem is transformed into a joint optimization problem of energy consumption and delay by the Lyapunov technique. A delay-constrained sleeping algorithm is proposed, and its effectiveness is confirmed by the numerical results of a simulation study. A tradeoff between energy consumption and delay, achieved by adjusting the weighting factor in the cache-aided UDN, is also demonstrated.

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Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks

Cited by 6 publications

References 21 publications

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

Gaussian bare‐bones gradient‐based optimization: Towards mitigating the performance concerns

An Improved Grasshopper Optimizer for Global Tasks

Delay-constrained sleeping mechanism for energy saving in cache-aided ultra-dense network

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