scholarly journals A comparative study of gradient-based and meta-heuristic optimization methods using Griewank benchmark function

2021 ◽  
Author(s):  
C. E. R Dalla ◽  
W. B. da Silva ◽  
J. C. S. Dutra ◽  
J. M. Colaço
Keyword(s):  
Comparative Study ◽  
Optimization Methods ◽  
Heuristic Optimization ◽  
Benchmark Function ◽  
Gradient Based

scholarly journals Automatic differentiation based for particle swarm optimization Steepest descent direction

2015 ◽  
Vol 1 (2) ◽  
pp. 90
Author(s):  
Aris Thobirin ◽  
Iwan Tri Riyadi Yanto
Keyword(s):  
Local Search ◽  
Automatic Differentiation ◽  
Optimization Methods ◽  
Global Optimum ◽  
Descent Direction ◽  
Benchmark Function ◽  
Swarm Optimization ◽  
Combination Methods ◽  
Gradient Based ◽  
Search Capability

Particle swam optimization (PSO) is one of the most effective optimization methods to find the global optimum point. In other hand, the descent direction (DD) is the gradient based method that has the local search capability. The combination of both methods is promising and interesting to get the method with effective global search capability and efficient local search capability. However, In many application, it is difficult or impossible to obtain the gradient exactly of an objective function. In this paper, we propose Automatic differentiation (AD) based for PSODD. we compare our methods on benchmark function. The results shown that the combination methods give us a powerful tool to find the solution.

Comparative Study on Layered Light-Field Displays and Optimization Methods

2019 ◽  
pp. 1073 ◽  
Author(s):  
Keita Maruyama ◽  
Keita Takahashi ◽  
Toshiaki Fujii ◽  
Munekazu Date ◽  
Hideaki Kimata
Keyword(s):  
Comparative Study ◽  
Light Field ◽  
Optimization Methods

scholarly journals Heuristic Optimization of Overloading Due to Electric Vehicles in a Low Voltage Grid

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6069
Author(s):  
Sajjad Haider ◽  
Peter Schegner
Keyword(s):  
Electric Vehicles ◽  
Low Voltage ◽  
Optimization Methods ◽  
Monte Carlo Analysis ◽  
Heuristic Optimization ◽  
Optimization Approach ◽  
Worst Case ◽  
Voltage Drops ◽  
The Individual ◽  
Nodal Voltage

It is important to understand the effect of increasing electric vehicles (EV) penetrations on the existing electricity transmission infrastructure and to find ways to mitigate it. While, the easiest solution is to opt for equipment upgrades, the potential for reducing overloading, in terms of voltage drops, and line loading by way of optimization of the locations at which EVs can charge, is significant. To investigate this, a heuristic optimization approach is proposed to optimize EV charging locations within one feeder, while minimizing nodal voltage drops, cable loading and overall cable losses. The optimization approach is compared to typical unoptimized results of a monte-carlo analysis. The results show a reduction in peak line loading in a typical benchmark 0.4 kV by up to 10%. Further results show an increase in voltage available at different nodes by up to 7 V in the worst case and 1.5 V on average. Optimization for a reduction in transmission losses shows insignificant savings for subsequent simulation. These optimization methods may allow for the introduction of spatial pricing across multiple nodes within a low voltage network, to allow for an electricity price for EVs independent of temporal pricing models already in place, to reflect the individual impact of EVs charging at different nodes across the network.

An Adjoint Based Approach for Optimal Design of Morphing SMP

2013 ◽  
Author(s):  
Shuang Wang ◽  
John C. Brigham
Keyword(s):  
Shape Change ◽  
Mechanical Energy ◽  
Optimization Methods ◽  
Design Parameters ◽  
Optimization Strategy ◽  
Thermally Activated ◽  
Gradient Based ◽  
Nonlinear Optimization Methods ◽  
Numerical Representations ◽  
Objective Functional

This work presents a strategy to identify the optimal localized activation and actuation for a morphing thermally activated SMP structure or structural component to obtain a targeted shape change or set of shape features, subject to design objectives such as minimal total required energy and time. This strategy combines numerical representations of the SMP structure’s thermo-mechanical behavior subject to activation and actuation with gradient-based nonlinear optimization methods to solve the morphing inverse problem that includes minimizing cost functions which address thermal and mechanical energy, morphing time, and damage. In particular, the optimization strategy utilizes the adjoint method to efficiently compute the gradient of the objective functional(s) with respect to the design parameters for this coupled thermo-mechanical problem.

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