Optimization Algorithm Performance in Determining Optimal Controls in Human Movement Analyses

1999 ◽  
Vol 121 (2) ◽  
pp. 249-252 ◽  
Author(s):  
R. R. Neptune
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Human Movement ◽  
Programming Algorithm ◽  
Optimal Controls ◽  
Tracking Problem ◽  
Algorithm Performance ◽  
Gradient Based ◽  
Optimization Routine ◽  
Downhill Simplex

The objective of this study was to evaluate the performance of different multivariate optimization algorithms by solving a “tracking” problem using a forward dynamic model of pedaling. The tracking problem was defined as solving for the muscle controls (muscle stimulation onset, offset, and magnitude) that minimized the error between experimentally collected kinetic and kinematic data and the simulation results of pedaling at 90 rpm and 250 W. Three different algorithms were evaluated: a downhill simplex method, a gradient-based sequential quadratic programming algorithm, and a simulated annealing global optimization routine. The results showed that the simulated annealing algorithm performed far superior to the conventional routines by converging more rapidly and avoiding local minima.

The Merits of a Parallel Genetic Algorithm in Solving Hard Optimization Problems

2003 ◽  
Vol 125 (1) ◽  
pp. 141-146 ◽  
Author(s):  
A. J. Knoek van Soest ◽  
L. J. R. Richard Casius
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Simplex Algorithm ◽  
Programming Algorithm ◽  
Parallel Genetic Algorithm ◽  
Local Optima ◽  
Optimal Region ◽  
Downhill Simplex

A parallel genetic algorithm for optimization is outlined, and its performance on both mathematical and biomechanical optimization problems is compared to a sequential quadratic programming algorithm, a downhill simplex algorithm and a simulated annealing algorithm. When high-dimensional non-smooth or discontinuous problems with numerous local optima are considered, only the simulated annealing and the genetic algorithm, which are both characterized by a weak search heuristic, are successful in finding the optimal region in parameter space. The key advantage of the genetic algorithm is that it can easily be parallelized at negligible overhead.

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.

An Effective Algorithm for Tool-Path Airtime Optimization during Leather Cutting

2010 ◽  
Vol 102-104 ◽  
pp. 373-377 ◽  
Author(s):  
Wei Bo Yang ◽  
Yan Wei Zhao ◽  
Jing Jie ◽  
Wan Liang Wang
Keyword(s):  
Simulated Annealing ◽  
Tool Path ◽  
Simulated Annealing Algorithm ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Genetic Simulated Annealing Algorithm ◽  
Generalized Traveling Salesman Problem ◽  
Hybrid Intelligence ◽  
Optimal Contour ◽  
Cutting Path

Tool-path airtime optimization problem during multi-contour processing in leather cutting is regarded as generalized traveling salesman problem. A hybrid intelligence algorithm is proposed. The improved genetic simulated annealing algorithm is applied to optimize cutting path selected arbitrarily firstly, and an optimal contour sequence is founded, then problem is changed into multi- segment map problem solved with dynamic programming algorithm. The algorithm's process and its various parameters are given simultaneously, and its performance is compared with simulated annealing and standard genetic algorithm alone. The results show that the algorithm is more effective.

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