Minimal constraint based cuckoo search algorithm for Removing Transmission Congestion and Rescheduling the Generator units

2017 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
N. Chidambararaj ◽  
K. Chitra
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Transmission Congestion ◽  
Minimal Constraint

scholarly journals Available Transfer Capability Determination for the Electricity Market using Cuckoo Search Algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 5340-5345
Author(s):  
T. L. Duong ◽  
T. T. Nguyen ◽  
N. A. Nguyen ◽  
T. Kang
Keyword(s):  
Power Systems ◽  
Electricity Market ◽  
Search Algorithm ◽  
Accurate Determination ◽  
System Security ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Available Transfer Capability ◽  
Transmission Congestion ◽  
Transfer Capability

In the electricity market, power producers and customers share a common transmission network for wheeling power from generation to consumption points. All parties in this open access environment may try to produce energy from cheaper sources for greater profit margin, which may lead to transmission congestion, which could lead to violation of voltage and thermal limits, threatening the system security. To solve this, available transfer capability (ATC) must be accurately estimated and optimally utilized. Thus, accurate determination of ATC to ensure system security while serving power transactions is an open and trending research topic. Many optimization approaches to deal with the problem have been proposed. In this paper, Cuckoo Search Algorithm (CSA) is applied for determining ATC problem between the buses in deregulated power systems without violating system constraints such as thermal, voltage constraints. The suggested methodology is tested on IEEE 14 and IEEE 24-bus for normal and contingency cases. The simulation results are compared with the corresponding results of EP, PSO, and GWO and show that the CSA is an effective method for determining ATC.

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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