A Parallel Hybrid Genetic Simulated Annealing Algorithm for Large-Scale Constrained Optimization

2002 ◽  
Author(s):  
Udayan Misra ◽  
Christina Bloebaum
Keyword(s):  
Simulated Annealing ◽  
Constrained Optimization ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Genetic Simulated Annealing Algorithm ◽  
Parallel Hybrid ◽  
Annealing Algorithm

Study on Hybrid Genetic Simulated Annealing Algorithm for Multi-Vehicle and Multi-Cargo Loading Problem

2010 ◽  
Vol 171-172 ◽  
pp. 167-170 ◽  
Author(s):  
Xiao Bo Wang ◽  
Jin Ying Sun ◽  
Chun Yu Ren
Keyword(s):  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Solution Quality ◽  
Genetic Simulated Annealing Algorithm ◽  
Algorithm Construct ◽  
Loading Problem ◽  
Annealing Algorithm ◽  
Crossover And Mutation

This paper studies multi-vehicle and multi-cargo loading problem under the limited loading capacity. Hybrid genetic simulated annealing algorithm is used to get the optimization solution. Firstly, adopt hybrid coding so as to make the problem more succinctly. On the basis of cubage-weight balance algorithm, construct initial solution to improve the feasibility. Adopt the improved non-uniform mutation so as to enhance local search ability of chromosomes. Secondly, through utilizing Boltzmann mechanism of simulated annealing algorithm, control crossover and mutation operation of genetic algorithm, search efficiency so as to improve the solution quality of algorithm. Finally, the example can be shown that the above model and algorithm is effective and can provide for large-scale ideas to solve practical problems.

scholarly journals Water Saving Control of Turfgrass Irrigation Robot Using Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Bei Han
Keyword(s):  
Simulated Annealing ◽  
High Performance ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Labor Costs ◽  
Genetic Simulated Annealing Algorithm ◽  
Annealing Algorithm ◽  
Adjustment System ◽  
Electromechanical Equipment ◽  
Cutter Head

Artificial intelligence through the robotic system offers a solution to the quest for an autonomous system with high cutting efficiency for lawn mowing. Because of the current trimming and maintenance operations on grasslands and gardens, it is essential to develop autonomous and efficient lawn pruning electromechanical equipment. This paper describes the design and construction of a high-performance automated grass trimming and irrigating robot. This device cuts and irrigates grass automatically with little human intervention. A genetic simulated annealing algorithm was employed to optimize motor parameters, specifically design a set of mowing mechanisms and mowing height adjustment system. The prototype was tested, which mainly includes the running status evaluation of the walking module, the mowing module, the cutter head lifting module, and the collision detection module. This robot can save water while watering the lawns, reduce labor costs, and improve mowing efficiency. We note that the proposed system can be implemented on a large scale under natural conditions in the future, which will be helpful in robotics applications and cutting grass on lawns and playing grounds.

scholarly journals Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

2021 ◽  
Vol 28 (2) ◽  
pp. 101-109
Keyword(s):  
Simulated Annealing ◽  
Software Testing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Fault Localization ◽  
Good Effect ◽  
Test Case ◽  
Genetic Simulated Annealing Algorithm ◽  
Starting Point ◽  
Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

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