A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

Evolutionary multi-objective optimization aims to provide a representative subset of the
Pareto front to decision makers. In practice, however, decision makers are usually interested …

We report the development of an open-source experimental design via Bayesian
optimization platform for multi-objective reaction optimization. Using high-throughput …

Python has become the programming language of choice for research and industry projects
related to data science, machine learning, and deep learning. Since optimization is an …

Software systems nowadays are complex and difficult to maintain due to continuous
changes and bad design choices. To handle the complexity of systems, software products …

This paper studies the problem of stochastic dynamic pricing and energy management
policy for electric vehicle (EV) charging service providers. In the presence of renewable …

Abstract “Framework” and “case-study” are the two most prominent features in the
optimization of architectural building design. The first can improve the speed of the process …

Fog computing is receiving considerable attention in the research community to deliver
computing resources for Internet of Things (IoT) devices. With rapid advancements in IoT, it …

Hierarchical deep neural networks are currently popular learning models for imitating the
hierarchical architecture of human brain. Single-layer feature extractors are the bricks to …

A knee point is almost always a preferred trade-off solution, if it exists in a bicriteria
optimization problem. In this article, an attempt is made to improve understanding of a knee …