Variant Of Cuckoo Search Algorithm Research Articles

A discrete variant of cuckoo search algorithm to solve the Travelling Salesman Problem and path planning for autonomous trolley inside warehouse

Recently, order picking routing (OPR) for robots inside modern warehouses have become one of the most challenging problems. The process of OPR can be formulated as a Travelling Salesman Problem (TSP). Traditional techniques used to solve this problem usually require a long execution time and are problem-specific. Meta-heuristic optimisation techniques have been applied to solve this problem and have shown outstanding results. In this study, we solve the OPR problem using a newly proposed discrete variant of the cuckoo search algorithm. Five modifications were made to the current discrete cuckoo search algorithm. The proposed variant was applied to a traditional TSP problem. Then, the proposed algorithm was customised to solve the OPR problem in a warehouse environment. Finally, the proposed algorithm was applied to a physical prototype. It was then compared with genetic, particle swarm optimisation, and ant colony optimisation algorithms. Simulation and practical results proved the significant performance of the proposed algorithm over all other algorithms, especially in solving complex problems.

Perturbation and Repository based Diversified Cuckoo Search in reconstruction of Gene Regulatory Network: A new Cuckoo Search approach

Cuckoo Search (CS), a population based meta-heuristic technique is motivated by the breeding characteristics of cuckoos. The main disadvantage of CS is its local stagnation due to weak exploration capability. To improve the exploration capacity and maintain appropriate balance between exploration and exploitation, this paper proposes a new variant of Cuckoo Search algorithm, namely, Perturbation and Repository based Diversified Cuckoo Search (PRDCS). It incorporates two modifications: Memorisation of the set of best solutions and four single-component successive Perturbations. For Memorisation, PRDCS uses a Repository to store solutions of the population having the same best Cost value. Any member (best solution) from the Repository is selected randomly for applying four single-component successive Perturbations on it. PRDCS has been applied to CEC2005 benchmark suit for performance validation. The computational experiment on CEC 2005 benchmarks suit has established that PRDCS is superior to the state-of-the-art algorithms in respect of the performance metrics used. Furthermore, PRDCS has been applied to Gene Regulatory Network (GRN) reconstruction using Recurrent Neural Network (PRDCS-RNN) model and Pearson Correlation Coefficient has been computed for the reconstructed GRN to remove the false positive edges. Application of the Proposed PRDCS-RNN on two real life gene expression datasets, IRMA-OFF and IRMA-ON reveals that, it is superior to the state-of-the-art algorithms in respect of performance metrics like F-score, Sensitivity, Specificity and Precision in producing well reconstructed GRN.

Optimal Parameters of OUPFC and GUPFC Under Renewable Energy Power Variation Using Cuckoo Search Algorithm Variants

In this paper, improved variants of cuckoo search algorithm are proposed for optimizing the controlling variables of flexible ac transmission system devices towards voltage stability enhancement and active power loss minimization by considering renewable energy sources intermittency in the network. Primarily, the optimal location for flexible ac transmission system devices is determined using line stability index and later the control parameters of generalized unified power flow controller and optimal unified power flow controller are optimized at different intermittency levels of renewable energy sources using three cuckoo search algorithm variants. The case studies are performed on standard IEEE 14-, 30-, 57-bus test systems. The superiority of proposed cuckoo search algorithm variants (linearly increased switching parameter, exponentially increased switching parameter and increased switching parameter in a power of three) in solving the multi-objective, non-linear complex optimization problem over time varying acceleration coefficient—particle swarm optimization variants is presented by illustrating various case studies.

The use of Metaheuristic Algorithms in Early Prediction and Forecasting of Flood – A Use of Cuckoo Search Algorithm based Optimization for Flood Controlling

Flood is one of the disasters which have multiple impacts on the society and industry. It has severe impacts on the urban economy and has forced the scholars to develop resiliency plans. Various types of flood forecasting techniques developed by the scholars and have certain limitations. There are various types of multiple modeling techniques which are being used for flood controlling and each has certain limitations. The optimization techniques along with the artificial intelligence algorithms can be helpful for monitoring and early prediction of flood. The neural network models promises better accuracy compared to convention models for prediction, but they face great difficulties in selection of appropriate model parameters. In the said context, here an effort has been made to explore the importance of Cuckoo theorem in flood management. The cuckoo search algorithm can be used for parameter tuning. The hybrid approach of using cuckoo search algorithm with neural networks has given far better accuracy compared to standalone algorithms. The use of such Cuckoo Search Metaheuristic algorithm will help us to predict early warning system than any other method and helps us to align the flood controlling activities. The paper presents the used of variants of cuckoo search algorithm for early flood prediction. The paper unfolds major insights of flood scenarios along with the significance of flood control and monitoring.

An improved adaptive cuckoo search algorithm based on the population feature and iteration information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Lévy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

A Improved Adaptive Cuckoo Search Algorithm Based the Population Feature and Iteration Information

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

An Innovative Damped Cuckoo Search Algorithm With a Comparative Study Against Other Adaptive Variants

This paper aims to find the best variant of Cuckoo Search Algorithm that makes the step size of Levy flight adaptive. For this reason, we introduce a new variant of CSA called Damped Cuckoo Search (DCS) in which the step size of Levy flight is adaptive via the concept of damped oscillations that exist in most second order control systems. Moreover, we propose two other methods that tune the step size of Levy flight based on chaotic maps. Then a deep comparative study is conducted among almost all variants of CSA that appeared in the last decade that modifies the step size of Levy flight. All these variants are tested on CEC2017 benchmark functions. Statistical analyses are performed using the Friedman test followed by four post-hoc procedures to hold paired comparisons between the proposed DCS and the other CSA variants. Also, graphical statistical analyses are conducted on all variants via Box Plots. Finally, convergence graphs for all the variants are illustrated as well to show the speed of solution improvement over generations. Simulation results prove that the proposed DCS outperforms all other variants with a large degree of significance. Moreover, DCS increases the speed of convergence in comparison with the other variants. The box plot graphs prove that DCS has the most compact distribution for all results obtained in all runs on most functions.

A new variant of cuckoo search algorithm with self adaptive parameters to solve complex RFID network planning problem

With the paradigm of the Internet of things, each object in the physical world can be remotely identified, controlled, and located through networks. Thanks to their low cost and their small form, the Radio frequency identification (RFID) tags are frequently used to tag objects . The tags or objects are often distributed in large geographic areas. Due to the limit of the interrogation range of RFID readers, multiple readers should be deployed to read the information stored on all tags. The major challenge in an RFID network design is to find the optimal placement and parameters of readers in order to meet the essential requirements of an RFID system such as coverage, load balance and interference between readers. This challenge has led to a new research area known in the literature as the RFID network planning problem. This problem is characterized by a large number of constraints as well as numerous objectives and it proves to be NP-hard. In this paper, we develop a novel optimization algorithm, namely the self adaptive cuckoo search (SACS) algorithm, to solve this complex problem. In the SACS algorithm, the control parameters of the cuckoo search (CS) algorithm are adjusted dynamically in real time. The self-adaptation phenomenon allows the evolutionary algorithm to be more flexible and closer to natural evolution. The experimental results on 13 standard benchmark functions demonstrate that the proposed algorithm is more efficient than five adaptive variants of the CS algorithm. In the second part of the paper, the SACS algorithm is also used to solve three difficult RFID network planning instances. The simulation studies show that the SACS algorithm obtains better solutions for the RFID network planning problem than the original CS, four adaptive CS variants, the GA and the PSO in terms of optimization and robustness. To test the effectiveness of the SACS algorithm on a real problem, a case study is carried out.

DV-hop localisation algorithm with DDICS

Dimension by dimension improvement-based cuckoo search algorithm DDICS is a recent proposed variant of cuckoo search algorithm. The experimental results illustrate DDICS is superior to several other evolutionary algorithms when solving un-constraint numerical benchmarks. In this paper, we employ it to improve the performance of distance vector-hop method DV-hop. The simulation results show it achieves the best performance when compared with two other DV-hop algorithms.

A Nonhomogeneous Cuckoo Search Algorithm Based on Quantum Mechanism for Real Parameter Optimization.

Cuckoo search (CS) algorithm is a nature-inspired search algorithm, in which all the individuals have identical search behaviors. However, this simple homogeneous search behavior is not always optimal to find the potential solution to a special problem, and it may trap the individuals into local regions leading to premature convergence. To overcome the drawback, this paper presents a new variant of CS algorithm with nonhomogeneous search strategies based on quantum mechanism to enhance search ability of the classical CS algorithm. Featured contributions in this paper include: 1) quantum-based strategy is developed for nonhomogeneous update laws and 2) we, for the first time, present a set of theoretical analyses on CS algorithm as well as the proposed algorithm, respectively, and conclude a set of parameter boundaries guaranteeing the convergence of the CS algorithm and the proposed algorithm. On 24 benchmark functions, we compare our method with five existing CS-based methods and other ten state-of-the-art algorithms. The numerical results demonstrate that the proposed algorithm is significantly better than the original CS algorithm and the rest of compared methods according to two nonparametric tests.

Parameter estimation of Takagi–Sugeno fuzzy system using heterogeneous cuckoo search algorithm

In this paper, a novel method, called intelligent Takagi–Sugeno Modeling (iTaSuM), for identifying the structure and parameters of T–S fuzzy system is developed based on heterogeneous cuckoo search algorithm (HeCoS) to overcome the drawbacks that classical cuckoo search algorithm. HeCoS is a new variant of cuckoo search algorithm with heterogeneous searching strategies based on the quantum mechanism. Through the experimental analysis, we demonstrate that the proposed algorithm has a balance between exploration and exploitation. Comparing with other existing methods, we achieve that iTaSuM can generate good fuzzy system model with high accuracy and strong generalization ability.