The present work is focused on enhancing the overall thermo-hydraulic performance of a previously proposed C-shaped printed circuit heat exchanger (PCHEs) using Machine Learning (ML) Algorithms. In this context, CFD analysis carried out 81 different channel configurations geometry, and computed data used to train three ML algorithms. Later, geometry optimized by coupling trained model with mult...

Software development is a multistage process. Minimizing the project duration and minimizing the project cost are two objectives of software projects. These two goals are often in conflict with each other. The most important influencing factor of these two objectives is human resource allocation. The best compromised human resource allocation plan based on these two objectives should be provide...

Financial health of many organizations now-a-days is being affected by investment in software and their cost estimation. Therefore, to provide effective cost estimation models are the most complex activity in software engineering fields. This paper presents a fuzzy clustering and optimization model for software cost estimation. The proposed model uses Pearson product-moment correlation coeffici...

In this paper we propose a two-stage Multi-Population Genetic Algorithm (MPGA) to solve parallel machine scheduling problems with multiple objectives. In the first stage, multiple objectives are combined via the multiplication of the relative measure of each objective. Solutions of the first stage are arranged into several sub-populations, which become the initial populations of the second stag...

A matrix formulation for an adaptive genetic algorithm is developed using mutation matrix and crossover matrix. Selection, mutation, and crossover are all parameter-free in the sense that the problem at a particular stage of evolution will choose the parameters automatically. This time dependent selection process was first developed in MOGA (mutation only genetic algorithm) [Szeto and Zhang, 20...

Fuzzy models describe nonlinear input-output relationships with linguistic fuzzy rules. A hierarchical fuzzy modeling is promising for identification of fuzzy models of target systems that have many input variables. In the identification, (1) determination of a hierarchical structure of submodels, (2) selection of input variables of each submodel, (3) division of input and output space, (4) tun...