A new clustering method based on the bio‐inspired cuttlefish optimization algorithm

Abstract

Most of the well‐known clustering methods based on distance measures, distance metrics and similarity functions have the main problem of getting stuck in the local optima and their performance strongly depends on the initial values of the cluster centers. This paper presents a new approach to enhance the clustering problems with the bio‐inspired Cuttlefish Algorithm (CFA) by searching the best cluster centers that can minimize the clustering metrics. Various UCI Machine Learning Repository datasets are used to test and evaluate the performance of the proposed method. For the sake of comparison, we have also analysed several algorithms such as K‐means, Genetic Algorithm and the Particle Swarm Optimization (PSO) Algorithm. The simulations and obtained results demonstrate that the performance of the proposed CFA‐Clustering method is superior to the other counterpart algorithms in most cases. Therefore, the CFA can be considered as an alternative stochastic method to solve clustering problems.

Rule Generation Based on Modified Cuttlefish Algorithm for Intrusion Detection System

“…For example, the protocol_type attribute consists of three categorical values (tcp, udp, icmp), and these values are converted to (10, 20, and 30), respectively. For instance, if an attribute consists of 100 categorical values, these values are converted to (10,20,30, …, 1000), respectively.…”

Integration of CFA 2 FB control scheme with modified shunt active line conditioner ( MSALC ) connected distribution system for mitigation of harmonics

“…The CFA is very latest version of the redesigned metaheuristic algorithms. [48][49][50][51][52][53][54][55] This algorithm represents the color-changing behavior that cuttlefish use for solving global optimization problems of numerical. Reflection and Visibility are the important function in the presented algorithm.…”

“…After that, it will become more and more fundamental to bring together all the optimization systems that they are capable of overcoming these shortcomings and effectively handling indistinguishable problems. The CFA is very latest version of the redesigned metaheuristic algorithms 48‐55 . This algorithm represents the color‐changing behavior that cuttlefish use for solving global optimization problems of numerical.…”

A novel data clustering approach based on whale optimization algorithm

“…Authors have proved the effectiveness of their hybrid approach using eight benchmark datasets. A Cuttlefish Algorithm guided data clustering approach is presented in Eesa and Orman (2019). The replacement of random numbers by chaotic numbers in Hariss hawks optimization algorithm for the data clustering problem improves the overall performance of the algorithm (Singh, 2020).…”