ADAPTIVE RANDOM SEARCH AND SIMULATED ANNEALING OPTIMIZERS: ALGORITHMS AND APPLICATION ISSUES

2010 ◽  
pp. 57-109 ◽  
Author(s):  
Jacek M. Jeżowski ◽  
Grzegorz Poplewski ◽  
Roman Bochenek
Keyword(s):  
Simulated Annealing ◽  
Random Search ◽  
Adaptive Random Search

scholarly journals Heterogeneous Simulated Annealing Teams: An Optimizing Search Algorithm Inspired by Engineering Design Teams

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Engineering Design ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Design Teams ◽  
Design Cognition ◽  
Gradient Based ◽  
Multi Agent ◽  
Annealing Algorithms

Although insights uncovered by design cognition are often utilized to develop the methods used by human designers, using such insights to inform computational methodologies also has the potential to improve the performance of design algorithms. This paper uses insights from research on design cognition and design teams to inform a better simulated annealing search algorithm. Simulated annealing has already been established as a model of individual problem solving. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. Each agent controls an adaptive annealing schedule, allowing the team develop heterogeneous search strategies. Such diversity is a natural part of engineering design, and boosts performance in other multi-agent algorithms. Further, interaction between agents in HSAT is structured to mimic interaction between members of a design team. Performance is compared to several other simulated annealing algorithms, a random search algorithm, and a gradient-based algorithm. Compared to other algorithms, the team-based HSAT algorithm returns better average results with lower variance.

scholarly journals Constructing Synthetic Samples

2016 ◽  
Vol 32 (1) ◽  
pp. 113-127
Author(s):  
Hua Dong ◽  
Glen Meeden
Keyword(s):  
Optimization Problem ◽  
Search Algorithm ◽  
Random Search ◽  
The United States ◽  
Simulation Studies ◽  
Synthetic Sample ◽  
Population Means ◽  
Adaptive Random Search ◽  
Random Search Algorithm ◽  
Two Populations

Abstract We consider the problem of constructing a synthetic sample from a population of interest which cannot be sampled from but for which the population means of some of its variables are known. In addition, we assume that we have in hand samples from two similar populations. Using the known population means, we will select subsamples from the samples of the other two populations which we will then combine to construct the synthetic sample. The synthetic sample is obtained by solving an optimization problem, where the known population means, are used as constraints. The optimization is achieved through an adaptive random search algorithm. Simulation studies are presented to demonstrate the effectiveness of our approach. We observe that on average, such synthetic samples behave very much like actual samples from the population of interest. As an application we consider constructing a one-percent synthetic sample for the missing 1890 decennial sample of the United States.

Numerical Optimization of the Thermoelectric Cooling Devices

2006 ◽  
Vol 129 (3) ◽  
pp. 339-347 ◽  
Author(s):  
Boris Abramzon
Keyword(s):  
Electric Current ◽  
Optimization Problems ◽  
Random Search ◽  
Thermal Conductance ◽  
Numerical Approach ◽  
Physical Parameters ◽  
Optimization Approach ◽  
Independent Variables ◽  
Adaptive Random Search ◽  
Optimization Search

The present study proposes a unified numerical approach to the problem of optimum design of the thermoelectric devices for cooling electronic components. The standard mathematical model of a single-stage thermoelectric cooler (TEC) with constant material properties is employed. The model takes into account the thermal resistances from the hot and cold sides of the TEC. Values of the main physical parameters governing the TEC performance (Seebeck coefficient, electrical resistance, and thermal conductance) are derived from the manufacturer catalog data on the maximum achievable temperature difference, and the corresponding electric current and voltage. The optimization approach is illustrated with several examples for different design objective functions, variables, and constraints. The objective for the optimization search is the maximization of the total cooling rate or the performance coefficient of the cooling device. The independent variables for the optimization search are as follows: The number of the thermoelectric modules, the electric current, and the cold side temperature of the TEC. Additional independent variables in other cases include the number of thermoelectric couples and the area-to-height ratio of the thermoelectric pellet. In the present study, the optimization problems are solved numerically using the so-called multistart adaptive random search method.

Simulation Optimization Using Multi-Time-Scale Adaptive Random Search

2019 ◽  
Vol 36 (06) ◽  
pp. 1940014
Author(s):  
Qi Zhang ◽  
Jiaqiao Hu
Keyword(s):  
Search Algorithm ◽  
Numerical Study ◽  
Random Search ◽  
Solution Space ◽  
Population Based ◽  
Global Optimum ◽  
Adaptive Random Search ◽  
Candidate Solution ◽  
Approximation Type ◽  
Effective Use

We propose a random search algorithm for seeking the global optimum of an objective function in a simulation setting. The algorithm can be viewed as an extension of the MARS algorithm proposed in Hu and Hu (2011) for deterministic optimization, which iteratively finds improved solutions by modifying and sampling from a parameterized probability distribution over the solution space. However, unlike MARS and many other algorithms in this class, which are often population-based, our method only requires a single candidate solution to be generated at each iteration. This is primarily achieved through an effective use of past sampling information by means of embedding multiple nested stochastic approximation type of recursions into the algorithm. We prove the global convergence of the algorithm under general conditions and discuss two special simulation noise cases of interest, in which we show that only one simulation replication run is needed for each sampled solution. A preliminary numerical study is also carried out to illustrate the algorithm.

An examination of the adaptive random search technique

AIChE Journal ◽  
1976 ◽  
Vol 22 (4) ◽  
pp. 744-750 ◽  
Author(s):  
M. W. Heuckroth ◽  
J. L. Gaddy ◽  
L. D. Gaines
Keyword(s):  
Random Search ◽  
Search Technique ◽  
Adaptive Random Search

Adaptive Random Search with Intensification and Diversification Combined with Genetic Algorithm

2006 ◽  
Vol 10 (6) ◽  
pp. 921-930 ◽  
Author(s):  
Dongkyu Sohn ◽  
◽  
Hiroyuki Hatakeyama ◽  
Shingo Mabu ◽  
Kotaro Hirasawa ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Optimization Method ◽  
Objective Functions ◽  
Adaptive Random Search ◽  
Searching Ability

A novel optimization method named RasID-GA (an abbreviation of Adaptive Random Search with Intensification and Diversification combined with Genetic Algorithm) is proposed in order to enhance the searching ability of conventional RasID, which is a kind of Random Search with Intensification and Diversification. In this paper, the timing of switching from RasID to GA, or from GA to RasID is also studied. RasID-GA is compared with parallel RasIDs and GA using 23 different objective functions, and it turns out that RasID-GA performs well compared with other methods.

Sign in / Sign up

Export Citation Format

Share Document