scholarly journals A Cooperative Coevolutionary Cuckoo Search Algorithm for Optimization Problem

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Hongqing Zheng ◽  
Yongquan Zhou
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Solution Quality ◽  
Design Problems ◽  
Constrained Problems ◽  
Engineering Design Problems ◽  
Handling Technique ◽  
Evolutionary Operators ◽  
Dynamic Population

Taking inspiration from an organizational evolutionary algorithm for numerical optimization, this paper designs a kind of dynamic population and combining evolutionary operators to form a novel algorithm, a cooperative coevolutionary cuckoo search algorithm (CCCS), for solving both unconstrained, constrained optimization and engineering problems. A population of this algorithm consists of organizations, and an organization consists of dynamic individuals. In experiments, fifteen unconstrained functions, eleven constrained functions, and two engineering design problems are used to validate the performance of CCCS, and thorough comparisons are made between the CCCS and the existing approaches. The results show that the CCCS obtains good performance in the solution quality. Moreover, for the constrained problems, the good performance is obtained by only incorporating a simple constraint handling technique into the CCCS. The results show that the CCCS is quite robust and easy to use.

A solution method for treatment scheduling in rehabilitation hospitals with real-life requirements

2018 ◽  
Vol 30 (4) ◽  
pp. 367-386 ◽  
Author(s):  
Liyang Xiao ◽  
Mahjoub Dridi ◽  
Amir Hajjam El Hassani ◽  
Wanlong Lin ◽  
Hongying Fei
Keyword(s):  
Waiting Time ◽  
Programming Model ◽  
Search Algorithm ◽  
Real Life ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Mixed Integer ◽  
Solution Quality ◽  
Improved Cuckoo Search Algorithm ◽  
Rehabilitation Hospitals

Abstract In this study, we aim to minimize the total waiting time between successive treatments for inpatients in rehabilitation hospitals (departments) during a working day. Firstly, the daily treatment scheduling problem is formulated as a mixed-integer linear programming model, taking into consideration real-life requirements, and is solved by Gurobi, a commercial solver. Then, an improved cuckoo search algorithm is developed to obtain good quality solutions quickly for large-sized problems. Our methods are demonstrated with data collected from a medium-sized rehabilitation hospital in China. The numerical results indicate that the improved cuckoo search algorithm outperforms the real schedules applied in the targeted hospital with regard to the total waiting time of inpatients. Gurobi can construct schedules without waits for all the tested dataset though its efficiency is quite low. Three sets of numerical experiments are executed to compare the improved cuckoo search algorithm with Gurobi in terms of solution quality, effectiveness and capability to solve large instances.

Cuckoo Search Algorithm for Minimization of Power Loss and Voltage Deviation

2016 ◽  
Vol 5 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Dung A. Le ◽  
Dieu N. Vo
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Solution Quality ◽  
Optimal Reactive Power Dispatch ◽  
Nature Inspired Algorithm ◽  
Voltage Deviation

This paper proposes a cuckoo search algorithm (CSA) to solve the optimal reactive power dispatch (ORPD) problem in power system operation considering the power loss and voltage deviation. The proposed CSA is a nature inspired algorithm from the cuckoo species laying their eggs in the nest of other species. There is a probability of the host bird to discover the alien eggs in its nest and the host bird either destroys the strange egg or simply ignores the host and builds a new one. The advantages of the CSA method are few control parameters and high optimal solution quality. The proposed method has been tested on the IEEE 30 bus system and the result comparison has indicated that the proposed method can obtain better solution quality than many other methods. Therefore, the proposed CSA method can be an alternative method for solving the ORPD problem.

scholarly journals Cuckoo Search Algorithm with Chaotic Maps

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Lijin Wang ◽  
Yiwen Zhong
Keyword(s):  
Random Walk ◽  
Chaotic Maps ◽  
Search Algorithm ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Scaling Factor ◽  
Convergence Speed ◽  
Solution Quality ◽  
Two Parameters

Cuckoo search algorithm is a novel nature-inspired optimization technique based on the obligate brood parasitic behavior of some cuckoo species. It iteratively employs Lévy flights random walk with a scaling factor and biased/selective random walk with a fraction probability. Unfortunately, these two parameters are used in constant value schema, resulting in a problem sensitive to solution quality and convergence speed. In this paper, we proposed a variable value schema cuckoo search algorithm with chaotic maps, called CCS. In CCS, chaotic maps are utilized to, respectively, define the scaling factor and the fraction probability to enhance the solution quality and convergence speed. Extensive experiments with different chaotic maps demonstrate the improvement in efficiency and effectiveness.

scholarly journals Path Planning Optimization for Agricultural Spraying Robots Using Hybrid Dragonfly – Cuckoo Search Algorithm

2021 ◽  
Vol 36 (1) ◽  
pp. 412-419
Author(s):  
S. Muthukumaran ◽  
Manikandan Ganesan ◽  
J. Dhanasekar ◽  
Ganesh Babu Loganathan
Keyword(s):  
Path Planning ◽  
Agricultural Landscape ◽  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Research Work ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Computational Techniques ◽  
Solution Quality ◽  
Routing Problem

Finding collision-free paths and optimized path coverage over an agricultural landscape has been a critical research problem among scientists and researchers over the years. Key precision farming strategies such as seeding, spraying fertilizers, and harvesting require special path planning techniques for efficient operations and will directly influence reducing the running cost of the farm. The main objective of this research work is to generate an optimized sequential route in an agricultural landscape with the nominal distance. In this proposed work, a novel Hybrid Dragonfly – Cuckoo Search algorithm is proposed and implemented to generate the sequential route for achieving spraying applications in greenhouse environments. Here the agricultural routing problem is expressed as a Travelling Salesman Problem, and the simulations are performed to find the effectiveness of the proposed algorithm. The proposed algorithm has generated better results when compared with other computational techniques such as PSO in terms of both solution quality and computational efficiency.

scholarly journals Improved Lagrangian relaxation generation decision-support in presence of electric vehicles

2021 ◽  
Vol 22 (1) ◽  
pp. 598
Author(s):  
Hossein Zeynal ◽  
Zuhaina Zakaria ◽  
Ahmad Kor
Keyword(s):  
Head Start ◽  
Lagrangian Relaxation ◽  
Electric Vehicles ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Duality Gap ◽  
Solution Quality ◽  
Hard Constraints ◽  
Nonlinear Fluctuation

<p><span>Decision making strategies for resources available in macro/micro scales have long been a critical argument. Among existing methods to address such a mixed-binary optimization model, Lagrangian relaxation (LR) found universal acceptance by many utilities, offering a fast and accurate answer. This paper aims at retrofitting the solution way of LR algorithm by dint of meta-heuristic cuckoo search algorithm (CSA). When integrating CSA into LR mechanism, a tighter duality gap is catered, representing more accurate feasible solution. The key performance of CSA exhibits a head start over other classical methods such as gradient search (GS) and Newton Raphson (NR) when dealt with the relative duality gap closure in LR procedure. Further, electric vehicles (EV) with its associated hard constraints are encompassed into model to imperiling the proposed CSA-LR if encountered with nonlinear fluctuation of duality gap. Simulation results show that the proposed CSA-LR model outperforms the solution quality with/without EV as compared with conventional NR-LR method.</span></p>

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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