scholarly journals Cross domain optimization problem with hyperheuristic approach using size stochastic move acceptance

2022 ◽  
Vol 197 ◽  
pp. 428-436
Author(s):  
Aurelius Ian ◽  
Ahmad Muklason ◽  
Faizal Mahanto
Keyword(s):  
Optimization Problem ◽  
Domain Optimization ◽  
Cross Domain

scholarly journals Comparative Analysis of Selection Hyper-Heuristics for Real-World Multi-Objective Optimization Problems

2021 ◽  
Vol 11 (19) ◽  
pp. 9153
Author(s):  
Vinicius Renan de Carvalho ◽  
Ender Özcan ◽  
Jaime Simão Sichman
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Exact Algorithms ◽  
Mathematical Function ◽  
Multi Objective Optimization ◽  
Algorithm Selection ◽  
Multi Objective ◽  
Cross Domain ◽  
Domain Performance

As exact algorithms are unfeasible to solve real optimization problems, due to their computational complexity, meta-heuristics are usually used to solve them. However, choosing a meta-heuristic to solve a particular optimization problem is a non-trivial task, and often requires a time-consuming trial and error process. Hyper-heuristics, which are heuristics to choose heuristics, have been proposed as a means to both simplify and improve algorithm selection or configuration for optimization problems. This paper novel presents a novel cross-domain evaluation for multi-objective optimization: we investigate how four state-of-the-art online hyper-heuristics with different characteristics perform in order to find solutions for eighteen real-world multi-objective optimization problems. These hyper-heuristics were designed in previous studies and tackle the algorithm selection problem from different perspectives: Election-Based, based on Reinforcement Learning and based on a mathematical function. All studied hyper-heuristics control a set of five Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta-)Heuristics (LLHs) while finding solutions for the optimization problem. To our knowledge, this work is the first to deal conjointly with the following issues: (i) selection of meta-heuristics instead of simple operators (ii) focus on multi-objective optimization problems, (iii) experiments on real world problems and not just function benchmarks. In our experiments, we computed, for each algorithm execution, Hypervolume and IGD+ and compared the results considering the Kruskal–Wallis statistical test. Furthermore, we ranked all the tested algorithms considering three different Friedman Rankings to summarize the cross-domain analysis. Our results showed that hyper-heuristics have a better cross-domain performance than single meta-heuristics, which makes them excellent candidates for solving new multi-objective optimization problems.

scholarly journals Cross-Domain Optimization of Ferroelectric Parameters for Negative Capacitance Transistors—Part I: Constant Supply Voltage

2020 ◽  
Vol 67 (1) ◽  
pp. 365-370 ◽  
Author(s):  
Sai Pentapati ◽  
Rakesh Perumal ◽  
Sourabh Khandelwal ◽  
Michael Hoffmann ◽  
Sung Kyu Lim ◽  
...  
Keyword(s):  
Supply Voltage ◽  
Negative Capacitance ◽  
Domain Optimization ◽  
Cross Domain

scholarly journals A Cross-Domain Optimization Scheme for MANET Communications in Heterogeneous Networks

2017 ◽  
Vol 3 (12) ◽  
pp. 153465 ◽  
Author(s):  
Yalong Wu ◽  
Wei Yu ◽  
Fang Yuan ◽  
Jin Zhang ◽  
Chao Lu ◽  
...  
Keyword(s):  
Heterogeneous Networks ◽  
Optimization Scheme ◽  
Domain Optimization ◽  
Cross Domain

Simulating Nanoscale Processes in Solids Using DFT and the Quasicontinuum Method

2005 ◽  
Author(s):  
Dan Negrut ◽  
Mihai Anitescu ◽  
Todd Munson ◽  
Peter Zapol
Keyword(s):  
Equilibrium Configuration ◽  
Optimization Techniques ◽  
Electronic Density ◽  
Quasicontinuum Method ◽  
Domain Optimization ◽  
First Order ◽  
Second Stage ◽  
Cross Domain ◽  
Electronic Density Distribution ◽  
Ionic Configuration

A framework is proposed for the investigation of chemical and mechanical properties of nanostructures. The methodology is based on a two-step approach to compute the electronic density distribution in and around a nanostructure, and then the equilibrium configuration of its nuclei. The Electronic Problem embeds interpolation and coupled cross-domain optimization techniques through a process called electronic reconstruction. In the second stage of the solution, the Ionic Problem repositions the nuclei of the nanostructure given the electronic density in the domain. The new ionic configuration is the solution of a nonlinear system based on a first-order optimality condition when minimizing the total energy associated with the nanostructure. The overall goal is a substantial increase in the dimension of the nanostructures that can be simulated by using approaches that include accurate DFT computation. This increase stems from the fact that during the solution of the Electronic Problem expensive DFT calculations are limited to a small number of subdomains. For the Ionic Problem, computational gains result from approximating the position of the nuclei in terms of a reduced number of representative nuclei following the quasicontinuum paradigm.

Sign in / Sign up

Export Citation Format

Share Document