scholarly journals An Effective Heuristic for Multidepot Low-Carbon Vehicle Routing Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
LiLing Liu ◽  
LiFang Lai
Keyword(s):  
Vehicle Routing ◽  
Fuel Consumption ◽  
Fruit Fly ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Low Carbon ◽  
Routing Problem ◽  
Solution Approach ◽  
Fruit Fly Optimization ◽  
Low Carbon Economy

Low-carbon economy has been a hot research topic in recent years. This paper firstly considers the vehicle load weight, the key factors affecting the fuel consumption, to establish the fuel consumption model, and then constructs the vehicle routing planning model in the last mile delivery with multiple depots within time windows. In order to solve this problem, we improve the classical fruit fly algorithm which is easy to fall into the local optimum, and the improved fruit fly optimization algorithm is designed and integrated with genetic algorithm. Computational results show that our solution approach is capable of solving instances with up to 48 customers and 4 different depots. The effectiveness and efficiency of the model and multigroup fruit fly algorithm are verified through case study.

Multi-step network traffic prediction using echo state network with a selective error compensation strategy

2021 ◽  
pp. 014233122110502
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski ◽  
Li Zhang
Keyword(s):  
Network Traffic ◽  
Error Compensation ◽  
Cloud Model ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Local Optimum ◽  
Echo State Network ◽  
Fruit Fly Optimization Algorithm ◽  
Compensation Strategy ◽  
Fruit Fly Optimization

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

scholarly journals Multiple Swarm Fruit Fly Optimization Algorithm Based Path Planning Method for Multi-UAVs

2020 ◽  
Vol 10 (8) ◽  
pp. 2822 ◽  
Author(s):  
Kunming Shi ◽  
Xiangyin Zhang ◽  
Shuang Xia
Keyword(s):  
Path Planning ◽  
Nonlinear Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Fruit Fly ◽  
Local Optimum ◽  
Planning Problem ◽  
Fruit Fly Optimization Algorithm ◽  
Nonlinear Optimization Problem ◽  
Fruit Fly Optimization

The path planning of unmanned aerial vehicles (UAVs) in the threat and countermeasure region is a constrained nonlinear optimization problem with many static and dynamic constraints. The fruit fly optimization algorithm (FOA) is widely used to handle this kind of nonlinear optimization problem. In this paper, the multiple swarm fruit fly optimization algorithm (MSFOA) is proposed to overcome the drawback of the original FOA in terms of slow global convergence speed and local optimum, and then is applied to solve the coordinated path planning problem for multi-UAVs. In the proposed MSFOA, the whole fruit fly swarm is divided into several sub-swarms with multi-tasks in order to expand the searching space to improve the searching ability, while the offspring competition strategy is introduced to improve the utilization degree of each calculation result and realize the exchange of information among various fruit fly sub-swarms. To avoid the collision among multi-UAVs, the collision detection method is also proposed. Simulation results show that the proposed MSFOA is superior to the original FOA in terms of convergence and accuracy.

scholarly journals An Improved Fruit Fly Optimization Algorithm Inspired from Cell Communication Mechanism

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Chuncai Xiao ◽  
Kuangrong Hao ◽  
Yongsheng Ding
Keyword(s):  
Carbon Fibers ◽  
Optimization Algorithm ◽  
Cell Communication ◽  
Fruit Fly ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Communication Mechanism ◽  
Evolution Stage

Fruit fly optimization algorithm (FOA) invented recently is a new swarm intelligence method based on fruit fly’s foraging behaviors and has been shown to be competitive with existing evolutionary algorithms, such as particle swarm optimization (PSO) algorithm. However, there are still some disadvantages in the FOA, such as low convergence precision, easily trapped in a local optimum value at the later evolution stage. This paper presents an improved FOA based on the cell communication mechanism (CFOA), by considering the information of the global worst, mean, and best solutions into the search strategy to improve the exploitation. The results from a set of numerical benchmark functions show that the CFOA outperforms the FOA and the PSO in most of the experiments. Further, the CFOA is applied to optimize the controller for preoxidation furnaces in carbon fibers production. Simulation results demonstrate the effectiveness of the CFOA.

Variables Screening Method Based on the Algorithm of Combining Fruit Fly Optimization Algorithm and RBF Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 3225-3230
Author(s):  
Fu Qiang Xu ◽  
You Tian Tao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Optimization Algorithm ◽  
Rbf Neural Network ◽  
Screening Method ◽  
Fruit Fly ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
The Mean

The form of fruit fly optimization algorithm (FOA) is easy to learn and has the characteristics of quick convergence and not readily dropping into local optimum. This paper presents the optimization of RBF neural network by means of FOA and establishment of network model, adopting it with the combination of the evaluation of the mean impact value (MIV) to select variables. The validity of this model is tested by two actual examples, furthermore, it is simpler to learn, more stable and practical.

scholarly journals IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ambika Aggarwal ◽  
Priti Dimri ◽  
Amit Agarwal ◽  
Madhushi Verma ◽  
Hesham A. Alhumyani ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Fruit Fly ◽  
Search Space ◽  
Workflow Scheduling ◽  
Local Optimum ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Qos Parameters ◽  
Computing Platforms

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.

Task Scheduling based on Fruit Fly Optimization Algorithm in Mobile Cloud Computing

2020 ◽  
Vol 16 (4) ◽  
pp. 618
Author(s):  
Xuan Chen ◽  
Zhengjiang Song ◽  
Hongfeng Zheng ◽  
Zhiping Wan
Keyword(s):  
Cloud Computing ◽  
Task Scheduling ◽  
Optimization Algorithm ◽  
Mobile Cloud Computing ◽  
Fruit Fly ◽  
Mobile Cloud ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization
Sign in / Sign up

Export Citation Format

Share Document