The search for reliable and efficient global optimization algorithms for solving phase stability and phase equilibrium problems in applied thermodynamics is an ongoing area of research. In this study, we introduce a new algorithm, MAKHA, which is a hybrid between Monkey Algorithm (MA and Krill Herd Algorithm (KHA). Its performance is compared with the two original algorithms along with Cuckoo S...

this work aims to investigate the reliability of software products as an important attribute of computer programs; it helps to decide the degree of trustworthiness a program has in accomplishing its specific functions. This is done using the Software Reliability Growth Models (SRGMs) through the estimation of their parameters. The parameters are estimated in this work based on the available fai...

K-means algorithm is a well-known clustering algorithm. In spite of its advantages such as high speed and ease of employment, this algorithm suffers from the problem of local optima. In order to overcome this problem, a lot of works have been carried out on clustering. This paper presents a hybrid extended cuckoo optimization algorithm (ECOA) and K-means (K) algorithm called ECOA-K. The COA alg...

The Partial Transmit Sequence which reduces the PAPR (Peak-to-Average Power Ratio) in Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system using a novel optimization algorithm is proposed in this study. This novel optimization algorithm is based on a hybrid of Bacterial Foraging Optimization (BFO) and Modified Cuckoo Search algorithm (MCS) and is thus cal...

DNA microarrays have been applied successfully in diverse research fields such as gene discovery, disease diagnosis and drug discovery. The roles of the genes and the mechanisms of the underlying diseases can be identified using microarrays. Biclustering is a two dimensional clustering problem, where we group the genes and samples simultaneously. It has a great potential in detecting marker gen...

Nature inspired metaheuristic algorithms provide derivative-free solution to optimize complex problems. Cuckoo Search (CS) algorithm is one of the most modern addition to the group of nature inspired optimization metaheuristics. The Simple Recurrent Networks (SRN) were initially trained by Elman with the standard back propagation (SBP) learning algorithm which is less capable and often takes en...

RNNs have local feedback loops within the network which allows them to shop earlier accessible patterns. This network can be educated with gradient descent back propagation and optimization technique such as second-order methods; conjugate gradient, quasi-Newton, Levenberg-Marquardt have also been used for networks training [14, 15]. But still this algorithm is not definite to find the global m...

Abstract Capacity prediction of surface mount production lines is important for improving productivity, dynamic scheduling, and optimizing structure. An integrated mutual information-cuckoo search-extreme learning machine (MI-CS-ELM) model proposed the impact nonlinear high-dimensional features surface-mounted on capacity. Firstly, affecting capacity are ranked based information method, predict...