scholarly journals Joint Successful Transmission Probability, Delay, and Energy Efficiency Caching Optimization in Fog Radio Access Network

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1847
Author(s):  
Alaa Bani-Bakr ◽  
Kaharudin Dimyati ◽  
MHD Nour Hindia ◽  
Wei Ru Wong ◽  
Tengku Faiz Tengku Mohmed Noor Izam
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Access Network ◽  
Cuckoo Search ◽  
Transmission Probability ◽  
Cuckoo Search Algorithm ◽  
Radio Access ◽  
Cache Placement ◽  
Fog Radio Access Network ◽  
Successful Transmission Probability

The fog radio access network (F-RAN) is considered an efficient architecture for caching technology as it can support both edge and centralized caching due to the backhauling of the fog access points (F-APs). Successful transmission probability (STP), delay, and energy efficiency (EE) are key performance metrics for F-RAN. Therefore, this paper proposes a proactive cache placement scheme that jointly optimizes STP, delay, and EE in wireless backhauled cache-enabled F-RAN. First, expressions of the association probability, STP, average delay, and EE are derived using stochastic geometry tools. Then, the optimization problem is formulated to obtain the optimal cache placement that maximizes the weighted sum of STP, EE, and negative delay. To solve the optimization problem, this paper proposes the normalized cuckoo search algorithm (NCSA), which is a novel modified version of the cuckoo search algorithm (CSA). In NCSA, after generating the solutions randomly via Lévy flight and random walk, a simple bound is applied, and then the solutions are normalized to assure their feasibility. The numerical results show that the proposed joint cache placement scheme can effectively achieve significant performance improvement by up to 15% higher STP, 45% lower delay, and 350% higher EE over the well-known benchmark caching schemes.

scholarly journals Multi-Objective Caching Optimization for Wireless Backhauled Fog Radio Access Network

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 708
Author(s):  
Alaa Bani-Bakr ◽  
MHD Nour Hindia ◽  
Kaharudin Dimyati ◽  
Effariza Hanafi ◽  
Tengku Faiz Tengku Mohmed Noor Izam
Keyword(s):  
Pareto Front ◽  
Search Algorithm ◽  
Access Network ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Multi Objective ◽  
Radio Access ◽  
Caching Scheme ◽  
Cache Placement ◽  
Fog Radio Access Network

Proactive content caching in a fog radio access network (F-RAN) is an efficient technique used to alleviate delivery delay and traffic congestion. However, the symmetric caching of the content is impractical due to the dissimilarity among the contents popularity. Therefore, in this paper, a multi-objective random caching scheme to balance the successful transmission probability (STP) and delay in wireless backhauled F-RAN is proposed. First, stochastic geometry tools are utilized to derive expressions of the association probability, STP, and average delivery delay. Next, the complexity is reduced by considering the asymptotic STP and delay in the high signal-to-noise ratio (SNR) regime. Then, aiming at maximizing the STP or minimizing the delay, the multi-objective cache placement optimization problem is formulated. A novel projected multi-objective cuckoo search algorithm (PMOCSA) is proposed to obtain the Pareto front of the optimal cache placement. The numerical results show that PMOCSA outperforms the original multi-objective cuckoo search algorithm (MOCSA) in terms of convergence to a feasible Pareto front and its rate. It also shows that the proposed multi-objective caching scheme significantly outperforms the well-known benchmark caching schemes by up to 40% higher STP and 85% lower average delay.

Solving multi-objective optimization problem using cuckoo search algorithm based on decomposition

2020 ◽  
Vol 51 (1) ◽  
pp. 143-160
Author(s):  
Liang Chen ◽  
Wenyan Gan ◽  
Hongwei Li ◽  
Kai Cheng ◽  
Darong Pan ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Multi Objective Optimization ◽  
Multi Objective

Discrete Non-Integer Order Differentiator Models Using Moth-Flame Optimization Algorithm

2017 ◽  
Vol 261 ◽  
pp. 394-401 ◽  
Author(s):  
Shibendu Mahata ◽  
Suman Kumar Saha ◽  
Rajib Kar ◽  
Durbadal Mandal
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Mapping Function ◽  
Cuckoo Search ◽  
Simplex Algorithm ◽  
Cuckoo Search Algorithm ◽  
Optimization Approach ◽  
Integer Order ◽  
Domain Mapping ◽  
Moth Flame Optimization Algorithm

Discrete rational approximation models to the non-integer order differentiator sλ, where λ ε (0, 1), using Moth-Flame Optimization (MFO) algorithm is proposed in this paper. The proposed metaheuristic optimization approach used to design the discrete non-integer order differentiators (DNODs) does not employ any s-to-z domain mapping function to perform the discretization operation. Frequency domain characteristics of DNODs, solution reliability, and algorithm convergence performances are investigated among MFO and an advanced evolutionary algorithm called Particle Swarm Optimization with adaptive inertia weight (PSO-w). Results demonstrate the effectiveness of MFO in outperforming PSO-w in solving this non-linear and multimodal optimization problem. The proposed DNODs also exhibit better performance in comparison with the designs based on techniques such as Nelder-Mead Simplex algorithm and Cuckoo Search Algorithm published in recent literature.

scholarly journals An Improved Hybrid Encoding Cuckoo Search Algorithm for 0-1 Knapsack Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Yanhong Feng ◽  
Ke Jia ◽  
Yichao He
Keyword(s):  
Optimization Problem ◽  
Feasible Solution ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Genetic Mutation ◽  
Cuckoo Search Algorithm ◽  
Knapsack Problems ◽  
Local Optimum ◽  
Transform Method ◽  
Continuous Space

Cuckoo search (CS) is a new robust swarm intelligence method that is based on the brood parasitism of some cuckoo species. In this paper, an improved hybrid encoding cuckoo search algorithm (ICS) with greedy strategy is put forward for solving 0-1 knapsack problems. First of all, for solving binary optimization problem with ICS, based on the idea of individual hybrid encoding, the cuckoo search over a continuous space is transformed into the synchronous evolution search over discrete space. Subsequently, the concept of confidence interval (CI) is introduced; hence, the new position updating is designed and genetic mutation with a small probability is introduced. The former enables the population to move towards the global best solution rapidly in every generation, and the latter can effectively prevent the ICS from trapping into the local optimum. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Experiments with a large number of KP instances show the effectiveness of the proposed algorithm and its ability to achieve good quality solutions.

scholarly journals Evaluation of Fast Evolutionary Programming, Firefly Algorithm and Mutate-Cuckoo Search Algorithm In Single-Objective Optimization

2019 ◽  
Vol 9 (1) ◽  
pp. 1
Author(s):  
Muhammad Zakyizzuddin Bin Rosselan ◽  
Shahril Irwan Bin Sulaiman ◽  
Norhalida Othman
Keyword(s):  
Evolutionary Algorithm ◽  
Firefly Algorithm ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Evolutionary Programming ◽  
Cuckoo Search Algorithm ◽  
Computational Time ◽  
Fitness Value ◽  
Single Objective

In this study proposes an evaluation of different computational intelligences, i.e Fast-Evolutionary Algorithm (FEP), Firefly Algorithm (FA) and Mutate-Cuckoo Search Algorithm (MCSA) for solving single-objective optimization problem. FEP and MCSA are based on the conventional Evolutionary Programming (EP) and Cuckoo Search Algorithm (CSA) with modifications and adjustment to boost up their search ability. In this paper, four different benchmark functions were used to compare the optimization performance of these three algorithms. The results showed that MCSA is better compare with FEP and FA in term of fitness value while FEP is fastest algorithm in term of computational time compare with other two algorithms.

scholarly journals Optimization of the operation of Dokan hydropower development using Cuckoo Search Algorithm

2022 ◽  
Vol 1216 (1) ◽  
pp. 012016
Author(s):  
K Ahmad-Rashid
Keyword(s):  
Optimization Problem ◽  
Initial Level ◽  
Search Algorithm ◽  
Energy Generation ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Metaheuristic Algorithms ◽  
Hydropower Plant ◽  
Hydropower Development

Abstract In this paper one of the recently developed metaheuristic algorithms, the Cuckoo Search algorithm is used for the optimization of the operation of a large hydropower plant in Kurdistan, Iraq. The optimization problem is to realize an annual planned energy generation with monthly imposed fractions. The obtained results are excellent, nevertheless, there are some limitations of the algorithm determined by the initial level into the reservoir and a certain correlation between the type of the year, the starting level and the planned energy to be realized.

scholarly journals A fuzzy adaptive controller for cuckoo search algorithm in active suspension system

2018 ◽  
Vol 39 (3) ◽  
pp. 761-771
Author(s):  
Chun-Tang Chao ◽  
Ming-Tang Liu ◽  
Chi-Jo Wang ◽  
Juing-Shian Chiou
Keyword(s):  
Genetic Algorithm ◽  
Pid Controller ◽  
Optimization Problem ◽  
Controller Design ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Local Optima ◽  
Pid Controller Design ◽  
Fuzzy Adaptive

This paper presents a fuzzy adaptive cuckoo search algorithm to improve the cuckoo search algorithm, which may easily fall into a local optimum when handling multiobjective optimization problems. The Fuzzy–Proportional-Integral-Derivative (PID) controller design for an active micro-suspension system has been incorporated into the proposed fuzzy adaptive cuckoo search algorithm to improve both driving comfort and road handling. In the past research, a genetic algorithm was often applied in Fuzzy–PID controller design. However, when the dimension is high and there are numerous local optima, the traditional genetic algorithm will not only have a premature convergence, but may also be trapped in the local optima. In the proposed fuzzy adaptive cuckoo search, all parameters of the PID controller and fuzzy rules are real coded to 75 bits in the optimization problem. Moreover, a fuzzy adaptive strategy is proposed for dynamically adjusting the learning parameters in the fuzzy adaptive cuckoo search, and this indeed enables global convergence. Experimental results verify that the proposed fuzzy adaptive cuckoo search algorithm can shorten the computing time in the evolution process and increase accuracy in the multiobjective optimization problem.

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