Simulated Annealing Algorithm Based Single Robot Odor Source Localization Strategy

2014 ◽  
Vol 494-495 ◽  
pp. 1286-1289
Author(s):  
Shi Gang Cui ◽  
Guang Ming Zeng ◽  
Fan Liang ◽  
Jiang Lei Dong

This paper presents a search strategy for single mobile robots to realize the active olfaction (also called odor/gas source localization or plume tracing). The odor source localization is regarded as a kind of dynamic function optimization problem in this article, using the simulated annealing algorithm to calculate the optimal solution of density distribution function, namely the odor source location. The simulation experiments results in indoor ventilated environment show that the robot can track in plume and locate the odor source under the area of the 10m*10m, and it can effectively jump out of local maximum values in the process of search.

Symmetry ◽  
2018 ◽  
Vol 11 (1) ◽  
pp. 15 ◽  
Author(s):  
Jing Lu ◽  
Xinqiang Qin

The S-λ Curves have become an important research subject in computer aided geometric design (CAGD), which owes to its good geometric properties (such as affine invariance, symmetry, and locality). This paper presents a new method to approximate an S-λ curve of degree n by using an S-λ curve of degree n-1. We transform this degree reduction problem into the function optimization problem first, and then using a new genetic simulated annealing algorithm to determine the global optimal solution of the optimization problem. The method can be used to approximate S-λ curves with fixed or unconstrained endpoints. Examples are given to verify the effectiveness of the presented algorithm; and these numeric examples show that the algorithm is not only easy to implement, but also offers high precision, which makes it valuable in practical applications.


Author(s):  
Ha Thi Mai Phan

As the construction activity has been growing, the companies that supply fresh concrete expand their production scale to meet their customers’ needs. The more customers, the longer queue tank trucks have to wait to pick up the fresh concrete. The customers are construction companies that have different construction works at the same time while the transportation time is only at night. They have to schedule efficiently the fleet of fresh concrete tank trucks during the night (turning the tank trucks a few turns) with constraints on the time window for the transfer of fresh concrete from the concrete company to the construction site as well as constraints on the waiting time for loading fresh concrete in the company. The scheduling for the fleet of construction company’s tank trucks will be modeled to minimize total transportation costs (fixed, variable) with estimated waiting times and tank truck’s turns several times during the night. The model of logistics problem is NP hard; Therefore, two algorithms are proposed to find the nearly optimal solution: heuristics and simulated annealing algorithm. The results will be compared and analyzed.


2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
He Tian ◽  
Guoqiang Wang ◽  
Kangkang Sun ◽  
Zeren Chen ◽  
Chuliang Yan ◽  
...  

Dynamic unbalance force is an important factor affecting the service life of scrap metal shredders (SMSs) as the product of mass error. Due to the complexity of hammerheads arrangement, it is difficult to take all the parts of the hammerhead into account in the traditional methods. A novel optimization algorithm combining genetic algorithm and simulated annealing algorithm is proposed to improve the dynamic balance of scrap metal shredders. The optimization of hammerheads and fenders on SMS in this paper is considered as a multiple traveling salesman problem (MTSP), which is a kind of NP-hard problem. To solve this problem, an improved genetic algorithm (IGA) combined with the global optimization characteristics of genetic algorithm (GA) and the local optimal solution of simulated annealing algorithm (SA) is proposed in this paper, which adopts SA in the process of selecting subpopulations. The optimization results show that the resultant force of the shredder central shaft by using IGA is less than the traditional metaheuristic algorithm, which greatly improves the dynamic balance of the SMS. Validated via ADAMS simulation, the results are in good agreement with the theoretical optimization analysis.


2011 ◽  
Vol 58-60 ◽  
pp. 1031-1036
Author(s):  
Dong Mei Yan ◽  
Cheng Hua Lu

This paper analyzed the principle and insufficient of traditional simulated annealing algorithm, and on the basis of the traditional simulated annealing algorithm, this paper used improved simulated annealing algorithm to solve vehicle routing problems. The new algorithm increases memory function, and keeps the current best state to avoid losing current optimal solution while reducing the computation times and accelerating the algorithm speed. The experimental results show that, the algorithm can significantly improve the optimization efficiency, and has faster convergence speed than traditional simulated annealing algorithm.


Transport ◽  
2011 ◽  
Vol 26 (2) ◽  
pp. 133-140 ◽  
Author(s):  
Arash Jahangiri ◽  
Shahriar Afandizadeh ◽  
Navid Kalantari

In recent years, natural and man-made disasters have increased and consequently put people's lives in danger more than before. Some of the crises are predictable. In these cases, there is a limited time for effective respond minimizing fatalities when people should be evacuated in a short time. Therefore, a transportation network plays a key role in evacuation. Hence, the outbound paths of urban networks are not sufficient from the viewpoint of number and capacity to encounter a huge amount of people; furthermore, it is costly to construct new routes or increase the capacity of the existing ones. Thus, a better utilization of the existing infrastructure should be considered. The article presents a model that determines optimum signal timing and increases the outbound capacity of the network. Moreover, in regard for the magnitude of the problem, an optimal solution could not be reached employing ordinary methods; therefore, the simulated annealing algorithm which is a meta-heuristic technique is used. The results of this study demonstrated that the objective function of the problem was greatly improved. Santrauka Pastaruoju metu gamtos ir žmonijos sukeltų nelaimių padaugėjo, todėl susiduriama su daugiau pavojų nei anksčiau. Kai kurių krizių ir nelaimių negalima numatyti. Tokiais atvejais veiksmingas reagavimo laikas yra ribotas, bet padeda sumažinti mirties atvejų, greitai evakuojant žmones. Šiuo atveju ypač svarbus yra transporto tinklas ir transportavimas. Iš miesto į užmiestį vedančios gatvės nėra pakankamai efektyvios, norint pervežti didelį žmonių skaičių. Be to, brangu pradėti rengti naujus maršrutus arba didinti jau esančių gatvių pralaidumą. Todėl turėtų būti apsvarstytas geresnis esančios transporto infrastruktūros panaudojimas. Straipsnyje nagrinėjamas modelis, nulemiantis optimalų šviesoforo signalo laiko nustatymą ir padidinantis išvykstančiųjų skaičių. Atsižvelgiant į problemos svarbą, optimalus sprendimas negali būti priimtas, naudojant įprastus metodus. Todėl naudojamas metaeuristinis metodas – modeliuojamasis atkaitinimo algoritmas (simulated annealing algorithm). Šio darbo rezultatai rodo, kad problemos tikslo funkcija labai pagerėjo. Резюме В настоящее время в мире увеличилось число стихийных бедствий и бедствий, связанных с неосторожной деятельностью людей. Значительную часть бедствий предсказать невозможно. В таких случаях эффективное время реагирования ограничено, что в свою очередь помогает уменьшить количество смертельных исходов и несчастных случаев при эвакуации населения. В этом случае транспортная сеть и сам процесс транспортирования играют решающую роль. Улицы, ведущие из города, становятся неэффективными из-за огромного количества людей. Кроме того, подготовка новых маршрутов или увеличение пропускной способности имеющихся улиц являются дорогостоящими мероприятиями. Поэтому следует проанализировать возможности более эффективного использования уже имеющейся транспортной инфраструктуры. В статье представлена модель, позволяющая определить выбор и установить оптимальное время сигнала светофора во время аварийной эвакуации с целью увеличить число эвакуируемых. Учитывая важность проблемы, оптимальное решение не может быть принято с использованием обычных методов. Поэтому используется метаэвристический метод – алгоритм имитации отжига (simulated annealing algorithm). Результаты, представленные в исследовании, показали, что целевая функция исследуемой проблемы значительно улучшилась


2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xibin Zhao ◽  
Hehua Zhang ◽  
Yu Jiang ◽  
Songzheng Song ◽  
Xun Jiao ◽  
...  

As being one of the most crucial steps in the design of embedded systems, hardware/software partitioning has received more concern than ever. The performance of a system design will strongly depend on the efficiency of the partitioning. In this paper, we construct a communication graph for embedded system and describe the delay-related constraints and the cost-related objective based on the graph structure. Then, we propose a heuristic based on genetic algorithm and simulated annealing to solve the problem near optimally. We note that the genetic algorithm has a strong global search capability, while the simulated annealing algorithm will fail in a local optimal solution easily. Hence, we can incorporate simulated annealing algorithm in genetic algorithm. The combined algorithm will provide more accurate near-optimal solution with faster speed. Experiment results show that the proposed algorithm produce more accurate partitions than the original genetic algorithm.


2010 ◽  
Vol 37-38 ◽  
pp. 203-206
Author(s):  
Rong Jiang

Modern management is a science of technology that adopts analysis, test and quantification methods to make a comprehensive arrangement of the limited resources to realize an efficient operation of a practical system. Simulated annealing algorithm has become one of the important tools for solving complex optimization problems, because of its intelligence, widely used and global search ability. Genetic algorithm may prevent effectively searching process from restraining in local optimum, thus it is more possible to obtains the global optimal solution.This paper solves unconstrained programming by simulated annealing algorithm and calculates constrained nonlinear programming by genetic algorithm in modern management. So that optimization process was simplified and the global optimal solution is ensured reliably.


2020 ◽  
Vol 12 (4) ◽  
pp. 1406
Author(s):  
Bing Han ◽  
Shuang Ren ◽  
Jingjing Bao

In recent years, with the development of high-speed railway in China, the railway operating mileages and passenger transport capacity have increased rapidly. Due to the high density of trains and the limited capacity of railways, it is necessary to solve market shares of different railway traffic modes in order to adjust the operation plans appropriately and run railway passenger transport products in line with passenger demand. Therefore, the purpose of this paper is to calculate market shares by formulating a mixed logit model based on improved nonlinear utility functions taking different factors into consideration, such as seat grades, fares, running time, passenger income levels and so on. Firstly according to maximum likelihood estimation, the likelihood function of this mixed logit model is proposed to maximize utility of all passenger groups. After that, we propose two improved algorithms based on the simulated annealing algorithm (ISAA-CC and ISAA-SS) to estimate the unknown parameters and solve the optimal solution of this model in order to enhance the computational efficiency. Finally, a real-world instance with related data of Beijing–Tianjin corridor, is implemented to demonstrate the performance and effectiveness of the proposed approaches. In addition, by performing this numerical experiment and comparing these two improved algorithms with the traditional Newton method, the ant colony algorithm and the simulated annealing algorithm, we prove that the improved algorithms we developed are superior to others in the optimal solution.


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