scholarly journals Read-across predictions of nanoparticle hazard endpoints: a mathematical optimization approach

2019 ◽  
Vol 1 (9) ◽  
pp. 3485-3498 ◽  
Author(s):  
Dimitra-Danai Varsou ◽  
Antreas Afantitis ◽  
Georgia Melagraki ◽  
Haralambos Sarimveis
Keyword(s):  
Genetic Algorithms ◽  
Mathematical Optimization ◽  
Optimization Approach

Development of a novel read-across methodology for the prediction of toxicity related endpoints of nanoparticles based on genetic algorithms.

scholarly journals KPLS optimization approach using genetic algorithms

2020 ◽  
Vol 170 ◽  
pp. 1153-1160 ◽  
Author(s):  
Jorge Daniel Mello-Román ◽  
Adolfo Hernandez
Keyword(s):  
Genetic Algorithms ◽  
Optimization Approach

scholarly journals Feature Selection in Data Envelopment Analysis: A Mathematical Optimization approach

Omega ◽  
2020 ◽  
Vol 96 ◽  
pp. 102068 ◽  
Author(s):  
Sandra Benítez-Peña ◽  
Peter Bogetoft ◽  
Dolores Romero Morales
Keyword(s):  
Feature Selection ◽  
Data Envelopment Analysis ◽  
Mathematical Optimization ◽  
Optimization Approach ◽  
Data Envelopment

scholarly journals Hybrid GA-SOCP Approach for Placement and Sizing of Distributed Generators in DC Networks

2020 ◽  
Vol 10 (23) ◽  
pp. 8616 ◽  
Author(s):  
Oscar Danilo Montoya ◽  
Walter Gil-González ◽  
Luis Fernando Grisales-Noreña
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Mathematical Optimization ◽  
Optimal Solution ◽  
Distribution Networks ◽  
Optimization Approach ◽  
Global Optimal Solution ◽  
Distributed Generators ◽  
Second Order Cone ◽  
Global Optimal

This research addresses the problem of the optimal location and sizing distributed generators (DGs) in direct current (DC) distribution networks from the combinatorial optimization. It is proposed a master–slave optimization approach in order to solve the problems of placement and location of DGs, respectively. The master stage applies to the classical Chu & Beasley genetic algorithm (GA), while the slave stage resolves a second-order cone programming reformulation of the optimal power flow problem for DC grids. This master–slave approach generates a hybrid optimization approach, named GA-SOCP. The main advantage of optimal dimensioning of DGs via SOCP is that this method makes part of the exact mathematical optimization that guarantees the possibility of finding the global optimal solution due to the solution space’s convex structure, which is a clear improvement regarding classical metaheuristic optimization methodologies. Numerical comparisons with hybrid and exact optimization approaches reported in the literature demonstrate the proposed hybrid GA-SOCP approach’s effectiveness and robustness to achieve the global optimal solution. Two test feeders compose of 21 and 69 nodes that can locate three distributed generators are considered. All of the computational validations have been carried out in the MATLAB software and the CVX tool for convex optimization.

scholarly journals Design of low-power CMOS VLSI circuits using multi-objective optimization in genetic algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 215-224
Author(s):  
Mahnoor Maghroori ◽  
Mehdi Dolatshahi
Keyword(s):  
Genetic Algorithms ◽  
Integrated Circuits ◽  
Circuit Simulation ◽  
Cmos Technology ◽  
Vlsi Circuits ◽  
Design Tool ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Cmos Vlsi

This paper presents a design CAD tool for automated design of digital CMOS VLSI circuits. In order to fit the circuit performance into desired specifications, a multi-objective optimization approach based on genetic algorithms (GA) is proposed and the transistor sizes are calculated based on the analytical equations describing the behavior of the circuit. The optimization algorithm is developed in MATLAB and the performance of the designed circuit is verified using HSPICE simulations based on 0.18µm CMOS technology parameters. Different digital integrated circuits were successfully designed and verified using the proposed design tool. It is also shown in this paper that, the design results obtained from the proposed algorithm in MATLAB, have a very good agreement with the obtained circuit simulation results in HSPICE.

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