An Improved Simulated Annealing Algorithm for Optimal Design

1995 ◽  
Author(s):  
Sherif Aly ◽  
Madara M. Ogot ◽  
Richard Pelz
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Simulated Annealing Algorithm ◽  
Random Search ◽  
Computation Time ◽  
Global Optimum ◽  
Search Method ◽  
Local Search Method ◽  
Iterative Improvement

Abstract A new algorithm based on the simulated annealing (SA) optimization algorithm is presented. This approach, simulated annealing with random search iterative improvement (SAWI), essentially initiates the SA process to locate the neighborhood of the global optimum. Prior to the convergence of SA, the algorithm switches to random search iterative improvement, a local search method, to converge to the optimum. The key to the effectiveness of SAWI is identifying when the premature termination of SA should occur. This paper presents the results of a parametric study conducted on the transition parameter, illustrating the effects of delayed and premature transition to the local search method, on the final solution. Two examples are presented and discussed to illustrate the efficacy of the algorithm. The results of these examples demonstrate that SAWI makes significant reductions in computation time while maintaining the simplicity of the original SA algorithm and without loss in quality of solution.

AN ASSIGNMENT-BASED LOCAL SEARCH METHOD FOR SOLVING VEHICLE ROUTING PROBLEMS

2005 ◽  
Vol 22 (01) ◽  
pp. 85-104 ◽  
Author(s):  
L. ZENG ◽  
H. L. ONG ◽  
K. M. NG
Keyword(s):  
Simulated Annealing ◽  
Local Search ◽  
Vehicle Routing ◽  
Search Method ◽  
Benchmark Problems ◽  
Heuristic Methods ◽  
Vehicle Routing Problems ◽  
Local Search Method ◽  
Routing Problems ◽  
Improvement Algorithm

In this paper, we propose an assignment-based local search method for solving vehicle routing problems. This method is a multi-route improvement algorithm that can operate on several routes at a time. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

Fullbore Image Construction of Imaging Logging Based on the Improved Texture Inpainting Method

2014 ◽  
Vol 571-572 ◽  
pp. 825-828
Author(s):  
Xiang Zhang ◽  
Jun Hua Wang ◽  
Xiao Ling Xiao
Keyword(s):  
Local Search ◽  
Texture Synthesis ◽  
Synthesis Method ◽  
Image Inpainting ◽  
Search Method ◽  
Local Search Method ◽  
Synthesis Algorithm ◽  
Image Construction ◽  
Tv Model ◽  
Improved Algorithm

The image inpainting method based on CriminiciA’s algorithm is slowly complete the image for large blank area. An improved algorithm based on the classic texture synthesis algorithm for image inpainting is proposed for imaging logging inpainting, which is used to generate the fullbore image. Two schemes, the local search method and priority calculation with TV model, are employed in the improved texture synthesis method. Some examples were given to demonstrate the effectiveness of the proposed algorithm on dealing with fullbore image construction with large blank area and raising efficiency obviously.

scholarly journals Simulated Annealing with Previous Solutions Applied to DNA Sequence Alignment

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Ernesto Liñán-García ◽  
Lorena Marcela Gallegos-Araiza
Keyword(s):  
Simulated Annealing ◽  
Sequence Alignment ◽  
Simulated Annealing Algorithm ◽  
Multiple Sequence ◽  
Heating And Cooling ◽  
Dna Sequence Alignment ◽  
Alignment Problem ◽  
Simple Change ◽  
Annealing Algorithm

A new algorithm for solving sequence alignment problem is proposed, which is named SAPS (Simulated Annealing with Previous Solutions). This algorithm is based on the classical Simulated Annealing (SA). SAPS is implemented in order to obtain results of pair and multiple sequence alignment. SA is a simulation of heating and cooling of a metal to solve an optimization problem. In order to select randomly a current solution, SAPS algorithm chooses a solution from solutions that have been previously generated within the Metropolis Cycle. This simple change has led to increase the quality of the solution to the problem of aligning genomic sequences with respect to the classical Simulated Annealing algorithm. The parameters of SAPS, for certain instances, are tuned by an analytical method, and some parameters have experimentally been tuned. SAPS has generated high-quality results in comparison with the classical SA. The instances used are specific genes of the AIDS virus.

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 Uma Heurística ILS para Resolução do Problema de Carregamento e Descarregamento de Contêineres em Navios Porta-contêineres

2011 ◽  
Author(s):  
Amaro José De S. Neto ◽  
Dalessandro S. Vianna ◽  
Marcilene De Fátima D. Vianna
Keyword(s):  
Local Search ◽  
Search Method ◽  
Greedy Heuristic ◽  
Local Search Method ◽  
Loading And Unloading ◽  
Loading Sequence

When docking at a port terminal it may be necessary to perform various operations of loading and unloading containers. Sometimes, when unloading, the target container which needs to be unloaded may be positioned below other containers that will not be unloaded at this time. These ones need to be removed to unload the target container. The goal is to find the best loading sequence minimizing thus the number of "rearrangements". The proposed heuristic was compared with a greedy heuristic and a local search method. The results show the adequacy of ILS heuristic to the problem addressed.

Seismic DNA – a novel non-local search method for multi-attribute datasets

First Break ◽  
2013 ◽  
Vol 31 (1963) ◽  
Author(s):  
J. Oystein Haavig Bakke ◽  
O. Gramstad ◽  
L. Sonneland
Keyword(s):  
Local Search ◽  
Search Method ◽  
Local Search Method ◽  
Non Local
Sign in / Sign up

Export Citation Format

Share Document