Abstract This paper explores the trade-off between redeveloping an urban site with higher density and maintaining solar access for surrounding context in hot dry climate of Tel Aviv. Such trade-offs are important future development Middle East, where densification is a demographic environmental need. We explore this multi-objective optimization (MOO). Specifically, we benchmark seven MOO algori...

A multi-objective optimization (MOO) of two-element wing models with morphing flap by using computational fluid dynamics (CFD) techniques, artificial neural networks (ANN), and non-dominated sorting genetic algorithms (NSGA II), is performed in this paper. At first, the domain is solved numerically in various two-element wing models with morphing flap using CFD techniques and lift (L) and drag ...

in this paper, a novel mathematical model for a preemption multi-mode multi-objective resource-constrained project scheduling problem with distinct due dates and positive and negative cash flows is presented. although optimization of bi-objective problems with due dates is an essential feature of real projects, little effort has been made in studying the p-mmrcpsp while due dates are included i...

It is difficult for the existing multi-population genetic algorithms with space partition to be successfully applied to multi-objective optimization problems. Multi-population genetic algorithms with space partition for multi-objective optimization problems are designed in this paper in allusion to the characteristics of multi-objective optimization problems. A complicated optimization problem ...

In the data mining and machine learning (ML) discipline, feature selection problem is considered among many researchers in recent times. Feature process targets to minimize set number maximize performance accuracy by identifying optimal features. Multiple objectives are while hence multi-objective metaheuristic optimization algorithms (MOMOAs) applied. this study, literature review performed MO...

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to get...