Action-Selection Method for Reinforcement Learning Based on Cuckoo Search Algorithm

2017 ◽  
Vol 43 (12) ◽  
pp. 6771-6785 ◽  
Author(s):  
Bilal H. Abed-alguni
Keyword(s):  
Reinforcement Learning ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Action Selection ◽  
Cuckoo Search Algorithm ◽  
Selection Method

scholarly journals Adaptive Gearbox Selection Method to Improve the Performance of Industrial Robotic Arms

2018 ◽  
Vol 27 (4) ◽  
pp. 565-575
Author(s):  
Mohammad Athar Hayat ◽  
Ankush A. Mankar
Keyword(s):  
Efficient Method ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Selection Method ◽  
Modern World ◽  
Robotic Arms

Abstract Robotic arms play a major role in today’s environment. Many robotic arms are used in industries, factories, and our surrounding environments. Many types of robotic arms are used for their particular purpose. Gearbox selection is very important in increasing the performance of robotic arms. Therefore, it is necessary to select the gearbox to improve the performance of robotic arms for everyday use. Thus, an efficient method is proposed to improve the performance of robotic arms for industries. This efficient method adaptively controls the gearbox to increase the performance of industrial robotic arms. The cuckoo search algorithm is an efficient method proposed to increase the performance of industrial robotic arms. By this method, we can improve the performance of industrial robotic arms and make use of these robotic arms in the modern world. The technique has been implemented in the working platform of MATLAB, and the results have been analyzed.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Optimization strategy of wireless charger node deployment based on improved cuckoo search algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yang Wang ◽  
Feifan Wang ◽  
Yujun Zhu ◽  
Yiyang Liu ◽  
Chuanxin Zhao
Keyword(s):  
Sensor Network ◽  
Sensor Node ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Sensor Nodes ◽  
Optimization Strategy ◽  
Multi Objective ◽  
Received Power

AbstractIn wireless rechargeable sensor network, the deployment of charger node directly affects the overall charging utility of sensor network. Aiming at this problem, this paper abstracts the charger deployment problem as a multi-objective optimization problem that maximizes the received power of sensor nodes and minimizes the number of charger nodes. First, a network model that maximizes the sensor node received power and minimizes the number of charger nodes is constructed. Second, an improved cuckoo search (ICS) algorithm is proposed. This algorithm is based on the traditional cuckoo search algorithm (CS) to redefine its step factor, and then use the mutation factor to change the nesting position of the host bird to update the bird’s nest position, and then use ICS to find the ones that maximize the received power of the sensor node and minimize the number of charger nodes optimal solution. Compared with the traditional cuckoo search algorithm and multi-objective particle swarm optimization algorithm, the simulation results show that the algorithm can effectively increase the receiving power of sensor nodes, reduce the number of charger nodes and find the optimal solution to meet the conditions, so as to maximize the network charging utility.

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