This paper aims to prove the efficiency of an adapted computationally intelligence-based behavior of cats called the cat swarm optimization algorithm, that solves the open shop scheduling problem, classified as NP-hard which its importance appears in several industrial and manufacturing applications. The cat swarm optimization algorithm was applied to solve some benchmark instances from the lit...

In this article, a recent metaheuristic method, cat swarm optimization, is introduced to find the proper clustering of data sets. Two clustering approaches based on cat swarm optimization called Cat Swarm Optimization Clustering (CSOC) and K-harmonic means Cat Swarm Optimization Clustering (KCSOC) are proposed. In the proposed methods, seeking mode and tracing mode are adopted to exploit and ex...

Cat Swarm Optimization (CSO) algorithm was put forward in 2006. Despite a faster convergence speed compared with Particle Swarm Optimization (PSO) algorithm, the application of CSO is greatly limited by the drawback of "premature convergence," that is, the possibility of trapping in local optimum when dealing with nonlinear optimization problem with a large number of local extreme values. In or...

In this study, a new hybrid metaheuristic algorithm named Chaotic Sand Cat Swarm Optimization (CSCSO) is proposed for constrained and complex optimization problems. This combines the features of recently introduced SCSO with concept chaos. The basic aim to integrate chaos feature non-recurring locations into SCSO’s core search process improve global performance convergence behavior. Thus, rando...

The research field of swarm intelligence contains various algorithms inspired from the particular survival skills of the creatures in Mother Nature. Many researchers utilize these methods to solve problems in engineering and financial fields. In this review, the concept of four swarm intelligence methods, including Bat Algorithm (BA), Evolved Bat Algorithm (EBA), Cat Swarm Optimization (CSO), a...

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Object recognition is a prominent research area in the world of computer science. It is used to solve a variety of problems such as image processing, medical diagnostics, compression and surveillance. The primary goal of object recognition is to recognize different objects present in an image, even if the objects’ size, shape and other features change. The challenge in object recognition is to ...

With the rapid development of swarm intelligence research field, a large number of algorithms in swarm intelligence are proposed one after another. The strong points and the drawbacks of a specific swarm intelligence algorithm becomes clear to be seen when the number of its application increases. To overcome the handicaps, some hybrid methods are invented. In this review, three hybrid swarm int...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...