Parameter Estimation of Nonlinear Systems by Dynamic Cuckoo Search

2017 ◽  
Vol 29 (4) ◽  
pp. 1103-1123 ◽  
Author(s):  
Qixiang Liao ◽  
Shudao Zhou ◽  
Hanqing Shi ◽  
Weilai Shi
Keyword(s):  
Simulated Annealing ◽  
Nonlinear Systems ◽  
Simulated Annealing Algorithm ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Superior Performance ◽  
Multiple Point ◽  
Crossover Operator

In order to address with the problem of the traditional or improved cuckoo search (CS) algorithm, we propose a dynamic adaptive cuckoo search with crossover operator (DACS-CO) algorithm. Normally, the parameters of the CS algorithm are kept constant or adapted by empirical equation that may result in decreasing the efficiency of the algorithm. In order to solve the problem, a feedback control scheme of algorithm parameters is adopted in cuckoo search; Rechenberg’s 1/5 criterion, combined with a learning strategy, is used to evaluate the evolution process. In addition, there are no information exchanges between individuals for cuckoo search algorithm. To promote the search progress and overcome premature convergence, the multiple-point random crossover operator is merged into the CS algorithm to exchange information between individuals and improve the diversification and intensification of the population. The performance of the proposed hybrid algorithm is investigated through different nonlinear systems, with the numerical results demonstrating that the method can estimate parameters accurately and efficiently. Finally, we compare the results with the standard CS algorithm, orthogonal learning cuckoo search algorithm (OLCS), an adaptive and simulated annealing operation with the cuckoo search algorithm (ACS-SA), a genetic algorithm (GA), a particle swarm optimization algorithm (PSO), and a genetic simulated annealing algorithm (GA-SA). Our simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.

scholarly journals A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem

2021 ◽  
Vol 13 (8) ◽  
pp. 4277
Author(s):  
Cui Zheyuan ◽  
Ali Thaeer Hammid ◽  
Ali Noori Kareem ◽  
Mingxin Jiang ◽  
Muamer N. Mohammed ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Superior Performance ◽  
Electricity Production ◽  
Short Term ◽  
Power Generators ◽  
Hydrothermal Scheduling ◽  
Solution State ◽  
Hydropower Plants

The key criteria of the short-term hydrothermal scheduling (StHS) problem is to minimize the gross fuel cost for electricity production by scheduling the hydrothermal power generators considering the constraints related to power balance; the gross release of water, and storage limitations of the reservoir, and the operating limitations of the thermal generators and hydropower plants. For addressing the same problem, numerous algorithms were being used, and related studies exist in the literature; however, they possess limitations concerning the solution state and the number of iterations it takes to reach the solution state. Hence, this article proposes using an enhanced cuckoo search algorithm (CSA) called the rigid cuckoo search algorithm (RCSA), a modified version of the traditional CSA for solving the StHS problem. The proposed RCSA improves the solution state and decreases the iteration numbers related to the CSA with a modified Lévy flight. Here, the movement distances are divided into multiple possible steps, which has infinite diversity. The effectiveness of RCSA has been validated by considering the hydrothermal power system. The observed results reveal the superior performance of RCSA among all other compared algorithms that recently have been used for the StHS problem. It is also observed that the RCSA approach has achieved minimum gross costs than other techniques. Thus, the proposed RCSA proves to be a highly effective and convenient approach for addressing the StHS problems

scholarly journals An improved cuckoo search algorithm with Stud crossover for Chinese TSP problem#

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Search Algorithm ◽  
Travelling Salesman Problem ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Cuckoo Search Algorithm ◽  
Theoretical Computer Science ◽  
Crossover Operator ◽  
Genetic Operators ◽  
Np Hard Problem ◽  
Improved Cuckoo Search Algorithm

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.

scholarly journals A Hybrid Cuckoo Search and Simulated Annealing Algorithm

2019 ◽  
Vol 28 (4) ◽  
pp. 683-698 ◽  
Author(s):  
Faisal Alkhateeb ◽  
Bilal H. Abed-alguni
Keyword(s):  
Simulated Annealing ◽  
Hybrid Algorithm ◽  
Simulated Annealing Algorithm ◽  
Performance Enhancement ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Experimental Results ◽  
Efficient Manner ◽  
Optimization Search

Abstract Simulated annealing (SA) proved its success as a single-state optimization search algorithm for both discrete and continuous problems. On the contrary, cuckoo search (CS) is one of the well-known population-based search algorithms that could be used for optimizing some problems with continuous domains. This paper provides a hybrid algorithm using the CS and SA algorithms. The main goal behind our hybridization is to improve the solutions generated by CS using SA to explore the search space in an efficient manner. More precisely, we introduce four variations of the proposed hybrid algorithm. The proposed variations together with the original CS and SA algorithms were evaluated and compared using 10 well-known benchmark functions. The experimental results show that three variations of the proposed algorithm provide a major performance enhancement in terms of best solutions and running time when compared to CS and SA as stand-alone algorithms, whereas the other variation provides a minor enhancement. Moreover, the experimental results show that the proposed hybrid algorithms also outperform some well-known optimization algorithms.

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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