Shipwrecked on Fear: Selection of Electives in School Minorities in a University Using Cuckoo Search Algorithm

2014 ◽  
pp. 139-150 ◽  
Author(s):  
Alberto Ochoa-Zezzatti ◽  
Oscar Castillo ◽  
Patricia Melín ◽  
Nemesio Castillo ◽  
Sandra Bustillos ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Selection Of

scholarly journals Memory Based Cuckoo Search Algorithm for Feature Selection of Gene Expression Dataset

2021 ◽  
pp. 100572
Author(s):  
Malek Alzaqebah ◽  
Khaoula Briki ◽  
Nashat Alrefai ◽  
Sami Brini ◽  
Sana Jawarneh ◽  
...  
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Gene Expression Dataset ◽  
Selection Of

scholarly journals Identification of suitable websites for digital marketing – an approach using bio-inspired computing

2017 ◽  
Vol 7 (1.2) ◽  
pp. 239 ◽  
Author(s):  
B. Suresh Kumar ◽  
Deepshikha Bharghava ◽  
Arpan Kumar Kar ◽  
Chinwe Peace Igiri
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Integrated Approach ◽  
Cuckoo Search Algorithm ◽  
Internet Usage ◽  
Digital Marketing ◽  
Assessment Criteria ◽  
Fast Pace ◽  
The Web ◽  
Selection Of

Due to the immense growth of Internet usage, the point of convergence has moved from physical to the web. The size of the web is increasing at a very fast pace to cater to the fast-evolving needs of the businesses, governments, and societies. However, selecting or identifying the best website is challenging. The practical issue to solve the problem comprises two parts. The first part is to identify the assessment criteria for appraising websites. Second is to evaluate the websites in the context of these assessment criteria and screen them to address a specific need. However, this objective is extremely complex and computationally extremely expensive. This research proposes an approach to identify websites from the Internet. The proposed integrated approach uses the Henry Garrett ranking method and cuckoo search algorithm for ranking and selection of websites for planning digital marketing campaigns.

scholarly journals Adaptation of optimization algorithm parameters to reconstruction of chaotic attractor

2015 ◽  
Vol 56 ◽  
Author(s):  
Miglė Drūlytė ◽  
Kristina Lukoševičiūtė ◽  
Erika Mekšunaitė
Keyword(s):  
Time Series ◽  
Time Delay ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Bee Colony Optimization ◽  
Selection Of

Optimal selection of time delay for time series reconstruction is an important problem in time series analysis and forecasting. When reconstructing the time series into phase space with non-uniform time delay, a time delay selection becomes a difficult optimization problem. To solve this problem, this paper presents two optimization algorithms: cuckoo search algorithm and artificial bee colony optimization algorithm.

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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