Cuckoo search algorithm based on frog leaping local search and chaos theory

2015 ◽  
Vol 266 ◽  
pp. 1083-1092 ◽  
Author(s):  
Xueying Liu ◽  
Meiling Fu
Keyword(s):  
Local Search ◽  
Chaos Theory ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm

scholarly journals A Binary Machine Learning Cuckoo Search Algorithm Improved by a Local Search Operator for the Set-Union Knapsack Problem

Mathematics ◽  
2021 ◽  
Vol 9 (20) ◽  
pp. 2611
Author(s):  
José García ◽  
José Lemus-Romani ◽  
Francisco Altimiras ◽  
Broderick Crawford ◽  
Ricardo Soto ◽  
...  
Keyword(s):  
Local Search ◽  
Knapsack Problem ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Local Search Operator ◽  
Insight Into

Optimization techniques, specially metaheuristics, are constantly refined in order to decrease execution times, increase the quality of solutions, and address larger target cases. Hybridizing techniques are one of these strategies that are particularly noteworthy due to the breadth of applications. In this article, a hybrid algorithm is proposed that integrates the k-means algorithm to generate a binary version of the cuckoo search technique, and this is strengthened by a local search operator. The binary cuckoo search algorithm is applied to the NP-hard Set-Union Knapsack Problem. This problem has recently attracted great attention from the operational research community due to the breadth of its applications and the difficulty it presents in solving medium and large instances. Numerical experiments were conducted to gain insight into the contribution of the final results of the k-means technique and the local search operator. Furthermore, a comparison to state-of-the-art algorithms is made. The results demonstrate that the hybrid algorithm consistently produces superior results in the majority of the analyzed medium instances, and its performance is competitive, but degrades in large instances.

A multi-objective discrete cuckoo search algorithm with local search for community detection in complex networks

2016 ◽  
Vol 30 (07) ◽  
pp. 1650080 ◽  
Author(s):  
Xu Zhou ◽  
Yanheng Liu ◽  
Bin Li
Keyword(s):  
Local Search ◽  
Community Detection ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Initial Population ◽  
Nest Location ◽  
Multi Objective ◽  
Local Search Strategy ◽  
First Time

Detecting community is a challenging task in analyzing networks. Solving community detection problem by evolutionary algorithm is a heated topic in recent years. In this paper, a multi-objective discrete cuckoo search algorithm with local search (MDCL) for community detection is proposed. To the best of our knowledge, it is first time to apply cuckoo search algorithm for community detection. Two objective functions termed as negative ratio association and ratio cut are to be minimized. These two functions can break through the modularity limitation. In the proposed algorithm, the nest location updating strategy and abandon operator of cuckoo are redefined in discrete form. A local search strategy and a clone operator are proposed to obtain the optimal initial population. The experimental results on synthetic and real-world networks show that the proposed algorithm has better performance than other algorithms and can discover the higher quality community structure without prior information.

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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