Using Genetic Algorithms With Niche Formation to Solve the Minimum Distance Problem Among Concave Objects

2002 ◽  
Author(s):  
J. A. Carretero ◽  
M. A. Nahon ◽  
O. Ma
Keyword(s):  
Minimum Distance ◽  
Optimization Technique ◽  
Combinatorial Problem ◽  
The Body ◽  
Optimization Approach ◽  
Multiple Minima ◽  
Niche Formation ◽  
Distance Problem ◽  
Set Of Points ◽  
Global Optimization Technique

In this paper an optimization approach is used to solve the problem of finding the minimum distance between concave objects, without the need for partitioning the objects into convex sub-objects. Since the optimization problem is not unimodal (i.e., has more than one local minimum point), a global optimization technique, namely a Genetic Algorithm, is used to solve the concave problem. In order to reduce the computational expense of evaluating the constraints at runtime, the objects’ geometry is replaced by a set of points on the surface of the body. This reduces the problem to a combinatorial problem where the combination of points (one on each body) that minimizes the distance will be the solution. Additionally, niche formation is used to allow the minimum distance algorithm to track multiple minima rather than exclusively looking for the global minimum.

scholarly journals Global Optimization of an Analog Method by Means of Genetic Algorithms

2017 ◽  
Vol 145 (4) ◽  
pp. 1275-1294 ◽  
Author(s):  
Pascal Horton ◽  
Michel Jaboyedoff ◽  
Charles Obled
Keyword(s):  
Genetic Algorithms ◽  
Global Optimization ◽  
Degrees Of Freedom ◽  
Optimization Technique ◽  
Global Optimum ◽  
Optimization Approach ◽  
Local Weather ◽  
Temporal Windows ◽  
Global Optimization Technique ◽  
Selection Of

Abstract Analog methods are based on a statistical relationship between synoptic meteorological variables (predictors) and local weather (predictand, to be predicted). This relationship is defined by several parameters, which are often calibrated by means of a semiautomatic sequential procedure. This calibration approach is fast, but has strong limitations. It proceeds through successive steps, and thus cannot handle all parameter dependencies. Furthermore, it cannot automatically optimize some parameters, such as the selection of pressure levels and temporal windows (hours of the day) at which the predictors are compared. To overcome these limitations, the global optimization technique of genetic algorithms is considered, which can jointly optimize all parameters of the method, and get closer to a global optimum, by taking into account the dependencies of the parameters. Moreover, it can objectively calibrate parameters that were previously assessed manually and can take into account new degrees of freedom. However, genetic algorithms must be tailored to the problem under consideration. Multiple combinations of algorithms were assessed, and new algorithms were developed (e.g., the chromosome of adaptive search radius, which is found to be very robust), in order to provide recommendations regarding the use of genetic algorithms for optimizing several variants of analog methods. A global optimization approach provides new perspectives for the improvement of analog methods, and for their application to new regions or new predictands.

How to design a more stable dental implant: A topology optimization approach

2021 ◽  
pp. 095441192110480
Author(s):  
Mohammad Reza Niroomand ◽  
Hamidreza Toutounchi ◽  
Sayedali Mousavi
Keyword(s):  
Topology Optimization ◽  
Bone Growth ◽  
Optimization Technique ◽  
Element Model ◽  
Influential Factors ◽  
The Body ◽  
Optimization Approach ◽  
Maximum Displacement ◽  
Mandibular Segment ◽  
The Stability

The body shape design is one of the most influential factors in the success of dental implants. This study presents a strategy to design the geometrical features of a threaded implant. The topology optimization technique is applied to identify appropriate spaces in the implant body to be removed for bone growth. The exact shape, position, and dimensions of the spaces are determined using a finite element model. This model consists of a mandibular segment, implant, abutment, and crown. During the optimization process, some grooves and holes are created in the implant by removing redundant materials. Bone growth into these spaces causes mechanical locking between the implant and surrounding bone. The smoothing process is performed following the optimization to remove stress concentration. The results indicate that this design strategy reduces the maximum displacement of the implant by approximately 20%. Moreover, a reduction in the implant’s volume and an increase in the contact area between the implant and bone are obtained. All mentioned issues would increase the stability and reduce the risk of implant loosening. Finally, using conventional production methods, the optimal implant was produced from titanium alloy to demonstrate the possibility of production of the proposed design.

A Maxmin Distance Problem

1972 ◽  
Vol 94 (2) ◽  
pp. 155-158 ◽  
Author(s):  
R. Aggarwal ◽  
G. Leitmann
Keyword(s):  
Minimum Distance ◽  
Terminal State ◽  
Initial State ◽  
Closed Set ◽  
Distance Problem

The problem of maximizing the minimum distance of a dynamical system’s state from a given closed set, while transferring the system from a given initial state to a given terminal state, is considered. Two different methods of solution of this problem are given.

scholarly journals Energy management strategies for combined heat and electric power micro-grid

2016 ◽  
Vol 20 (4) ◽  
pp. 1091-1103 ◽  
Author(s):  
Marina Barbaric ◽  
Drazen Loncar
Keyword(s):  
Energy Management ◽  
Renewable Energy Sources ◽  
Storage System ◽  
Optimization Technique ◽  
Management Strategies ◽  
Distribution Networks ◽  
Energy Storage System ◽  
Optimization Approach ◽  
Micro Grid ◽  
Additional Rule

The increasing energy production from variable renewable energy sources such as wind and solar has resulted in several challenges related to the system reliability and efficiency. In order to ensure the supply-demand balance under the conditions of higher variability the micro-grid concept of active distribution networks arising as a promising one. However, to achieve all the potential benefits that micro-gird concept offer, it is important to determine optimal operating strategies for micro-grids. The present paper compares three energy management strategies, aimed at ensuring economical micro-grid operation, to find a compromise between the complexity of strategy and its efficiency. The first strategy combines optimization technique and an additional rule while the second strategy is based on the pure optimization approach. The third strategy uses model based predictive control scheme to take into account uncertainties in renewable generation and energy consumption. In order to compare the strategies with respect to cost effectiveness, a residential micro-grid comprising photovoltaic modules, thermal energy storage system, thermal loads, electrical loads as well as combined heat and power plant, is considered.

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