A Novel Fuzzy logic based Improved Cuckoo Search Algorithm

Cuckoo Search (CS) algorithm is a nature-inspired optimization algorithm (NIOA) with less control parameters that is stable, versatile, and easy to implement. CS has good global search capabilities, but it is prone to local optima problems. As a result, it may be possible to improve the classic CS algorithm's optimization capability. Centered on fuzzy set theory, this paper introduces an improved CS version. The population of solutions has been divided into two fuzzy sets, and each solution is assigned to one of the sets based on its fitness. The fuzzy collection centroids, global best solution advice, and Lévy distribution dependent mutation are all used to boost the population's solutions. With well-accepted objective functions such as Otsu inter class variance and Kapur's entropy, the experimental analysis has been conducted on the CEC-2014 test suite and image multi-level thresholding domain. The proposed fuzzy cuckoo search (FCS) algorithm is compared to the classical CS, PSO, FA, SMA, and BA algorithm and provides satisfactory results.

Collision Avoidance Controller for Unmanned Surface Vehicle Based on Improved Cuckoo Search Algorithm

For the dynamic collision avoidance problem of an unmanned surface vehicle (USV), a dynamic collision avoidance control method based on an improved cuckoo search algorithm is proposed. The collision avoidance model for a USV and obstacles is established on the basis of the principle of the velocity obstacle method. Simultaneously, the Convention on the International Regulations for Preventing Collisions at Sea (COLREGS) is incorporated in the collision avoidance process. For the improvement of the cuckoo algorithm, the adaptive variable step-size factor is designed to realize the adaptive adjustment of flight step-size, and a mutation and crossover strategy is introduced to enhance the population diversity and improve the global optimization ability. The improved cuckoo search algorithm is applied to the collision avoidance model to obtain an optimal collision avoidance strategy. According to the collision avoidance strategy, the desired evasion trajectory is obtained, and the tracking controller based on PID is used for the Lanxin USV. The experimental results show the feasibility and effectiveness of the proposed collision avoidance method, which provides a solution for the autonomous dynamic collision avoidance of USVs.

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

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.

Optimized Rod Size Design of Denim Fabric Grinding Robot Based on Improved Cuckoo Search Algorithm

Background: Dexterity is an important index for evaluating the motion performance of a robot. The size of the robot connecting rods directly affects the performance of flexibility. Objective: The purpose of this study is to provide an overview of optimal design methods from many pieces of literature and patents, and propose a new optimal design method for ensuring the robot completes its tasks flexibly and efficiently under workspace constraints. Methods: The kinematics and working space of the robot are analyzed to determine the range of motion of each joint. Then, a dexterity index is established based on the mean value of the global spatial condition number. Finally, an improved cuckoo algorithm is proposed, which changes the step size control factor with the number of iterations. Taking the dexterity index as the objective optimization function and the working radius as the constraint condition, the improved cuckoo search algorithm is used to optimize the size of the robot rod. Results: The improved cuckoo algorithm and proposed rod size optimized method are fully evaluated by experiments and comparative studies. The optimization design process shows that the proposed method has better solution accuracy and faster convergence speed. The optimized design results show that the robot's dexterity index has increased by 26.1%. Conclusion: The proposed method has better solution accuracy and faster convergence speed. The method was suitable for optimizing the rod parameters of the robot, and it was very meaningful to improve the motion performance of the robot.

Improved Cuckoo Search Algorithm for Future Selection

The classification of high-dimensional dataset is challenging as it contains large amount irrelevant and noisy features. Thus, feature selection is performed in the dataset to eliminate these redundant features. It reduces the dimensionality of the dataset and increases the classification accuracy. Hence, for selecting the relevant features in high dimensional data, an improved cuckoo search algorithm (ICSA) was proposed in this paper. After feature selection, the dataset undergo classification using KNN classifier and SVM classifier. The experimental process illustrates that the improved cuckoo search algorithm effectively increases the classification accuracy by reducing the number of features in the dataset. For analysing the proposed algorithm, seven UCI repository dataset have been utilised. Also, the ICS algorithm is compared with other existing algorithms for the given dataset. From the investigation process, it was concluded that the proposed algorithm selects lesser number of features and also enhances the classification accuracy than the other existing algorithms.

Using Improved SPA and ICS-LSSVM for Sustainability Assessment of PV Industry along the Belt and Road

Scientific and timely sustainability evaluation of the photovoltaic industry along the Belt and Road is of great significance. In this paper, a novel hybrid evaluation model is proposed for accurate and real-time assessment that integrates modified set pair analysis with least squares support vector machine that combines improved cuckoo search algorithm. First, the indicator system is set from five principles, namely economy, politics, society, ecological environment and resources. Then, the traditional approach is established through modifying set pair analysis on the basis of variable fuzzy set coupling evaluation theory. A modern intelligent assessment model is designed that integrates improved cuckoo search algorithm and least squares support vector machine where the concept of random weight is introduced in improved cuckoo search algorithm. In the case analysis, the relative errors calculated by the proposed model all fluctuate in the range of [−3%, 3%], indicating that it has the strongest fitting and learning ability. The empirical analysis verifies the scientificity and precision of the method and points out influencing factors. It provides a new idea for rapid and effective assessment of PV industry along the Belt and Road, as well as assistance for the sustainable development of this industry. This paper innovatively proposes the sustainability evaluation index system and evaluation model for the photovoltaic industry in countries along the Belt and Road, thus contributing to the promotion of sustainable development of the photovoltaic industry in countries along the Belt and Road.