Review on cat swarm optimization algorithms

Clustering is a NP-hard problem that is used to find the relationship between patterns in a given set of patterns. It is an unsupervised technique that is applied to obtain the optimal cluster centers, especially in partitioned based clustering algorithms. On the other hand, cat swarm optimization (CSO) is a new meta-heuristic algorithm that has been applied to solve various optimization problems and it provides better results in comparison to other similar types of algorithms. However, this algorithm suffers from diversity and local optima problems. To overcome these problems, we are proposing an improved version of the CSO algorithm by using opposition-based learning and the Cauchy mutation operator. We applied the opposition-based learning method to enhance the diversity of the CSO algorithm and we used the Cauchy mutation operator to prevent the CSO algorithm from trapping in local optima. The performance of our proposed algorithm was tested with several artificial and real datasets and compared with existing methods like K-means, particle swarm optimization, and CSO. The experimental results show the applicability of our proposed method.

Well placement optimization using an analytical formula-based objective function and cat swarm optimization algorithm

In this chapter, we present a new population-based method, called cat swarm optimization (CSO) algorithm, which imitates the natural behaviour of cats. We first describe the general knowledge of the behaviour of cats in Sect. 6.1. Then, the fundamentals and performance of CSO are introduced in Sect. 6.2. Next, some selected variations of CSO are explained in Sect. 6.3. Right after this, Sect. 6.4 presents a representative CSO application. Finally, Sect. 6.5 summarises this chapter.

Cat swarm optimization (CSO) is a novel metaheuristic based on swarm intelligence, presented in 2006 has demonstrated great potential generating good results and excellent performances simulating the behavior of domestic cats using two behavior: seeking and tracing mode, this mode are classified using a mixture rate (MR), this parameter finally defines the number of individuals who work by exploring and exploiting. This work presents an improvement structure of a binary cat swarm optimization using a total reboot of the population when loss diversity it is detected.

Structural damage identification (SDI) serves as an indirect approach that has the potential to meet real‐time monitoring of structures. However, the identification accuracy and efficiency of some methods need to be improved, especially when there are some uncertain interfering factors or noise. This paper presents a new optimization algorithm and an improved objective function for inverse problems of SDI, offering an effective solution for bridge damage identification under uncertain noise interference and incomplete modal data. In this study, by hybridizing the whale optimization algorithm and the sand cat swarm optimization, a novel whale‐sand cat swarm optimization (W‐SCSO) method is proposed for SDI. The cubic chaotic mapping is introduced for initialization of the W‐SCSO method, and then the lens opposition‐based learning and the stochastic differential mutation are employed to enhance the search capability and convergence accuracy of the proposed algorithm. Besides, the mode shape curvature, the frequency change ratio, and the L 1/2 sparse regularization are used to improve the objective function. Four other existing state‐of‐the‐art methods are used to verify the performance of the proposed W‐SCSO method by the CEC2017 benchmark functions and a simply supported beam finite model. The comparative analysis highlights the feasibility and effectiveness of the proposed method in the considered cases. Moreover, an aluminum alloy simply supported beam was conducted for the SDI experiment to further prove the effectiveness of the improved method in practice. Simulation and experimental results show that the proposed method effectively locates and quantifies stiffness reduction in bridge structures, which maintains high accuracy in damage identification despite potential modal incompleteness and uncertain measurement noise interference.

SummaryThis chapter proposes a novel color image compression technique based on Block Truncation Coding (BTC) using Cat Swarm Optimization (CSO). The original BTC method compresses a block of a grayscale image into a bitmap and a pair of quantization numbers. For a color image, which is encoded using R, G, and B channels, the BTC method enhances the compression rate using a common bitmap instead of three naturally formed bitmaps. However, it is difficult to find an efficient common bitmap due to its complexity. The CSO algorithm is generated by modeling the behaviors of cats, which show better performance in achieving the goal of finding best solution than other optimization algorithms. In this chapter, we adopt Cat Swarm Optimization (CSO) to search for a near optimal solution for this problem. The experimental results indicate that the proposed method could improve the decompressed BTC color image quality by obtaining a near optimal solution of the common bit map.

Low frequency oscillations are observed when large power systems are interconnected by relatively weak tie-lines. Power System Stabilizers (PSS) are incorporated in the excitation system of the generators to enhance damping of these low frequency oscillations. In this paper, the design of multiple PSS in a multi-machine power system (MMPS) using Cat Swarm Optimization (CSO) algorithm has been proposed. The design problem of the PSS has been formulated as an optimization problem and CSO has been employed to search for optimal controller parameters. It has been shown that the stability performance of the system can be improved by minimizing the eigenvalue based objective function comprising of the damping factor and the damping ratio of the poorly damped electromechanical modes. Eigenvalue analysis and nonlinear simulation results have also been presented for various loading conditions and system configurations to show the effectiveness and robustness of the proposed controllers and their ability to provide efficient damping of low frequency oscillations.

The Job shop scheduling problem is known as a combinatorial optimization problem that aims to find best sequence of operations with optimal execution time called makespan. Various algorithms are used to resolve it. This research paper aims to provide a new adaptation to solve the job shop scheduling problem. Discrete cat swarm optimization algorithm (CSO) consists of two modes, which are: the seeking mode when the cat is resting and the tracing mode when the cat is hunting. These two modes are combined by a mixture ratio. The result applied to some benchmark instances and proves thus the performance of this adaptation.

It is common for banking system to analyse the feasibility of credit application before its approval. Although this process has been carefully done, there is no warranty that all credits will be repaid smoothly. This study aimed to know the accuracy of Cat Swarm Optimisation (CSO) algorithm in classifying small loans’ performance that is approved by Bank Rakyat Indonesia (BRI), one of several public banks in Indonesia. Data collected from 200 lenders were used in this work. The data matrix consists of 9 independent variables that represent profile of the credit, and one categorical dependent variable reflects credit’s performance. Prior to the analyses, data was divided into two data subset with equal size. Ordinal logistic regression (OLR) procedure is applied for the first subset and gave 3 out of 9 independent variables i.e. the amount of credit, credit’s period, and income per month of lender proved significantly affect credit performance. By using significantly estimated parameters from OLR procedure as the initial values for observations at the second subset, CSO procedure started. This procedure gave 76 percent of classification accuracy of credit performance, slightly better compared to 64 percent resulted from OLR procedure.

The cat swarm optimization (CSO) is regarded as one of the intellectual optimization techniques that have the ability to show a good performance in the machine learning environment. The modified and improved version of the cat swarm optimization algorithm proposed in this paper provides a unique characteristic of parallel computation. The modified algorithm uses two types of weight expression to calculate the new weight for each iteration of the algorithm. The weight expression which helps in getting a better value of global best during the evaluation procedure is taken into consideration in every iteration. This modified algorithm takes the best from both the modified weight expression. Simulation results show that this modified version of CSO performs reasonably well in comparison to normal CSO.KeywordsCat swarm optimizationGlobal best-guided modified cat swarm optimizationBenchmark functions

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

SummaryMelanoma is the most prevalent type of skin cancer, affecting skin surface cells. The risk features of the disease are minimized by finding the disease at time or in the initial stage. Even through, various skin cancer detection methods are developed, detecting the disease using discriminative features still results a challenging task. An efficient detection strategy based on the proposed Lion Cat Swarm Optimization‐based Deep Neuro Fuzzy Network (LCSO‐based DNFN) is created to identify skin cancer in its early stages. The LCSO is the integration of the lion optimization algorithm (LOA) and cat swarm optimization (CSO) algorithm. To make treatment measure at right time increases the probability of survival and so detecting the disease using deep learning classifier is performed with hybrid optimization algorithm. The segmentation result is effective through the fusion model and it ensures to achieve accurate detection process. Rather than extracting the features, the proposed method generates optimal result through data augmentation. The performance of the proposed LCSO‐based DNFN is determined using skin disease dataset, and the introduced approach obtained outstanding performance with the measures of accuracy, sensitivity, and specificity with the values of 93.10%, 92.95%, and 92.87%, respectively.

Agriculture plays a vital role in the economy and crop disease causes huge financial losses every year. The losses can be reduced by detecting the disease accurately. The variation in light intensity and complex background of the agricultural field in detecting the maize leaves disease are the biggest challenges. An optimization algorithm, named Cat Swarm Political Optimizer Algorithm (CSPOA) has been developed in this research to detect the disease of a maize plant leaf. Our proposed algorithm is an integration of the Cat Swarm Optimization (CSO) and Political Optimizer (PO) algorithm. Anisotropic filtering performs pre-processing for removing noise and the Region of Interest (ROI) extraction for enhancing the image quality. The super resolution image is obtained from the Low Resolution (LR) images using kernel regression model. After obtaining the super resolution image, the salient map extraction has been carried out for representing the saliency. Finally, the maize plant leaves disease classification process is done using General Adversarial Network (GAN) for identifying the maize leaves disease. The training of GAN develops the CSPOA. On comparing with the existing maize plant leaves disease detection approaches, the developed CSPOA-based GAN performed with a maximum accuracy 0.9056, maximum sensitivity 0.9599, and the maximum specificity 0.9592, respectively.

Software defined networks (SDN) remain a hot research field as it provides controllable networking operations. The SDN controller can be treated as the operating system of the SDN model and it holds the responsibility of performing different networking applications. Despite the benefits of SDN, security remains a challenging problem. At the same time, distributed denial of services (DDoS) is a typical attack on SDN owing to centralized architecture, especially at the control layer of the SDN. This article develops a new Cat Swarm Optimization with Fuzzy Rule Base Classification (CSO-FRBCC) model for cybersecurity in SDN. The presented CSO-FRBCC model intends to effectually categorize the occurrence of DDoS attacks in SDN. To achieve this, the CSO-FRBCC model primarily pre-processes the input data to transform it to a uniform format. Besides, the CSO-FRBCC model employs FRBCC classifier for the recognition and classification of intrusions. Moreover, the parameter optimization of the FRBCC classification model is adjusted by the use of cat swarm optimization (CSO) algorithm which results in improved performance. A comprehensive set of simulations were carried out on benchmark dataset and the results highlighted the enhanced outcomes of the CSO-FRBCC model over the other recent approaches.

One of the issues in cloud computing is workflow scheduling. A workflow models the process of executing an application comprising a set of steps and its objective is to simplify the complexity of application management. Workflow scheduling maps each task to a proper resource and sorts tasks on each resource to meet some efficiency measures such as processing and transmission costs, load balancing, quality of service, and etc. Task scheduling is an NP-Complete problem. In this study, meta-heuristic firefly algorithm (FA) is used to present a workflow scheduling algorithm. The purpose of the proposed scheduling algorithm is to explore optimal schedules such that the cost of processing and transmission of the whole workflow are minimized while there will be load balancing among the processing stations. The proposed algorithm is implemented in MATLAB and its efficiency is compared with cat swarm optimization (CSO) algorithm. The evaluations show that the proposed algorithm outperforms CSO in finding better solutions.