Sensor placement optimization of civil engineering structures using GA–SA algorithm

Assembly sequence planning plays an important role in the product development process. It is an important factor that determines quality and cost of the product assembly. Cost in assembly can be reduced by the implementation of generating automatic product assembly sequences, and selecting the optimum sequence in product assembly process. Assembly sequence planning (ASP) is combinatorial problem. Graph-based algorithms are adopted for traditional ASP method. In recent years, some genetic algorithms and simulated annealing algorithms have been used to solve ASP problems, and some achievements are arrived at. However, the two kinds of algorithms have limitations for ASP. GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non-optimization of final result for global variable. For simulated annealing algorithms, the principle of generating new sequence is exchanging position of the randomly selected two parts. Obviously, for complex products, a number of non-feasible solutions may appear, and the efficiency is low. In view of these limitations, the approach of combining GA and SA is proposed to build genetic simulated annealing algorithm for the optimization of ASP. In this paper, the following contents are included. Firstly, the relevant researches on assembly sequence planning and the application of GA and SA are summarized. Next, the idea of combining the two algorithms into genetic simulated annealing algorithm is put forward, which aims at improving the efficiency of problem solving. Thirdly, the genetic simulated annealing algorithm for assembly sequence planning is implemented, the method, procedure as well as key techniques of the genetic simulated annealing algorithm are addressed in detail and the principles for selecting parameters are studied to achieve better performance of the algorithm. Fourthly, a case study is presented to validate the proposed method. In the case, GA, SA and genetic simulated annealing algorithm are applied to ASP respectively, and the results verify the advantages of the genetic simulated annealing algorithm in solving the ASP problem. At last, the work of this paper is summarized and the future researches are given.

PurposeThe purpose of this paper is to propose a novel method under the name of genetic simulated annealing algorithm (GSAA) and ant colony optimization (ACO) algorithm for assembly sequence planning (ASP) which is possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.Design/methodology/approachBased on the genetic algorithm (GA), simulated annealing, and ACO algorithm, the GSAA are put forward. A case study is presented to validate the proposed method.FindingsThis GSAA has better optimization performance and robustness. The degree of dependence on the initial assembly sequence about GSAA is decreased. The optimization assembly sequence still can be obtained even if the assembly sequences of initial population are infeasible. By combining GA and simulated annealing (SA), the efficiency of searching and the quality of solution of GSAA is improved. As for the presented ACO algorithm, the searching speed is further increased.Originality/valueTraditionally, GA heavily depends on the choosing original sequence, which can result in early convergence in iterative operation, lower searching efficiency in evolutionary process, and non‐optimization of final result for global variable. Similarly, SA algorithms may generate a great deal of infeasible solutions in the evolution process by generating new sequences through exchanging position of the randomly selected two parts, which results in inefficiency of the solution‐searching process. In this paper, the proposed GSAA and ACO algorithm for ASP are possessed of the competence for assisting the planner in generating a satisfied and effective assembly sequence with respect to large constraint assembly perplexity.

A type of genetic simulated annealing algorithms (GSAAs) is presented, which is used to optimize the parameters of proportional-integral-derivative (PID) controllers. This approach combines the merits of genetic algorithms (GAs) and simulated annealing algorithms (SAAs). By integrating the global search ability of GA with the local search ability of SAA, the search ability of GSAA is much stronger than GA's and SAA's search ability. So, GSAA could find the global optimal solution of the given problem. Furthermore, the adaptive probability for crossover operator and nonuniform mutation operator is used in the GSAA, which can eliminate the phenomena of premature converge. Computer simulation on the speed control system of a kind of mobile robots is relized by Matlab. The results of computer simulation demonstrate that, comparing with the GA and SAA, the response speed of the PID controller can be improved due to the parameters produced from GSAA.

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

To solve the hardware/software partitioning problem in embedded system, this paper proposed a new genetic simulated annealing algorithm (NGSA) which based on analysis of genetic algorithms and simulated annealing algorithm the main advantages and disadvantages. The genetic algorithm integrates the simulated annealing idea; niche technology is introduced to maintain population diversity; and the Metropolis criterion with the formation of new groups to improve the quality of group. Experimental results show that the algorithm has strong climbing ability and global search capability, and the fitness value is significantly improved than genetic algorithm and simulated annealing algorithm.

The generalization error of Support Vector Machine usually depends on its kernel parameters, but there is no analytic method to choose kernel parameters for SVM. In order to choose the kernel parameters for SVM, the Simulated Annealing Algorithm and Genetic Algorithm are combined, which is called Simulated Annealing Genetic Algorithm (SA-GA), to choose the SVM kernel parameters. SA-GA makes use of encoding method, reproduction, crossover and mutation in the SA when generate new solution. In this way, the characteristic of SA that can accept a worse solution in a certain extent of probability can solve premature convergence of GA, and the heuristic search method of GA can make SA robust to the parameters of cooling schedule. So the combined algorithm has better performance than SA or GA, and it can get a better solution for optimization problem. At last, SA-GA has been used to choosing the kernel parameters of SVM. The results of simulation show that the performance of the method that proposed in this paper was more efficient than SA and GA for choosing kernel parameters of SVM.

In radio astronomy observation, using bandwidth synthesis technique, the observation precision can be improved under limited record bandwidth. The optimal band selection is one of the key problems in bandwidth synthesis. In this paper, the mathematical model of the band selection is established, and the essence of optimal band selection is pointed out, and the optimal band selection algorithm is studied. Aiming at the shortcoming of the conventional method, the genetic algorithm and simulated annealing algorithm are respectively used, and finally a new band selection method based on the genetic simulated annealing algorithm is proposed, combining the two algorithms. Simulating experiments results show that the genetic simulated annealing algorithm is obviously better than the above three method, and has better robustness.

A method for least-cost QoS multicast routing based on genetic simulated annealing algorithm

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.

Test optimization selection is a set cover problem, and heuristic algorithm for set covering problem is effective method. A genetic simulated annealing neural network fused algorithm was proposed by fusing the genetic algorithm, BP neural network and the simulated annealing algorithm, the genetic algorithm global search ability, strong ability of BP neural network training algorithm and fast search ability of simulated annealing algorithm were made full use of in this algorithm, the phenomenon falling into local optimum was avoided, and also the search efficiency and accuracy wad improved, the algorithm is applied to solve the test optimization selection problem. Example proves that this algorithm can effectively and quickly obtain test the optimal solution of optimization problems. Introduction In order to optimize the selection, the optimal combination test is chose in all possible combination of tests in the system, and the testability index can be met, while the minimum cost, include test time and test expense[1][2]. Perspective of mathematics, optimization selection is a test set covering problem, but we know set cover problem is a NP problem, when the system is on a big scale, to obtain the optimal solution is very difficult[3][4]. At present there are many more effective to solve the set covering problem of heuristic algorithm, such as genetic algorithm (GA) and simulated annealing algorithm (SA), neural network (ANN), tabu search (TS), ant colony system (ACO), etc., but these algorithms have their own advantages and defects, the effect is not ideal to solve alone, to complement each other mutual fusion has become the focus of future research[5][6][7]. This article is based on this idea, BP neural network and simulated annealing algorithm and introduced on the basis of traditional genetic algorithm, and a genetic simulated annealing neural network fusion algorithm has been formed, optimization selection can be solved faster and more accurate. Fusion algorithm Operation process of fusion algorithm The operation process of fusion algorithm is shown in Figure 1. Extreme value and dead zone analysis of algorithm In the process of solving the traditional heuristic algorithm, the maximum and minimum values appear easily. These extreme values are very close to the optimal value of algorithm, but the fusion algorithm is based on genetic algorithm, and the advantages of genetic algorithm is strong global search capability, which can avoid the occurrence of extreme value in the whole search space[8][9]. Therefore, the occurrence of extreme value can be avoided in the fusion algorithm can the occurrence of extreme value. Similarly, the genetic algorithm is good at global search, which can search to every corner of the space in the process of search, and the dead zone can be avoided in the fusion algorithm. Because of strong global search ability of the genetic algorithm, extreme value and dead zone can be avoided, but the disadvantage of slow speed is also caused in the genetic algorithm. Moreover, International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015) © 2015. The authors Published by Atlantis Press 402 The BP neural network and simulated annealing algorithm are introduced in the fusion algorithm, the search speed can be improved, and the optimal solution can solved quickly and effectively[10][11].

In the context of the digital development of big data and information in recent years, performance-based residential building design and its optimization methods have gradually become a research hotspot of domestic and international attention. The simulated annealing algorithm is a major component of residential buildings’ optimal energy efficiency design. Although the crossover operator under the computational traditional simulated annealing algorithm incorporates the respective advantages of the genetic algorithm and simulated annealing algorithm, the purely optimal genetic simulated annealing algorithm is more effective for the evaluation of index analysis and better reflects the superiority of the algorithm. In the Energy Plus model, the three algorithms were used to analyze the design indexes of ecological energy-efficient residential buildings, and the SS values of the MOSA algorithm fluctuated between 10% and 24%, and the performance of the recommended genetic simulated annealing algorithm was significantly better than the other two algorithms. This study effectively solves the difficulties of low efficiency and high failure rate common in design practice when using this technique and is of historical importance to the development of energy-efficient residential buildings in China.

Development of a parallel optimization method based on genetic simulated annealing algorithm

Introduction. The Grid Connected Photovoltaic System comprises two fundamental control loops: an external loop responsible for overseeing the DC link voltage, and an internal control loop that regulates the inverter current. The primary element of any control loop is the proportional-integral controller and determining the appropriate gains for this controller is a difficult issue.Aim of the Study. The study aimed to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage by novel hybrid optimization method named Genetic Algorithm- Simulated Annealing and Genetic Algorithm- Pattern search.Material and Methods. In this paper we use two hybrid optimizations techniques called Genetic Algorithm- simulated Annealing and Genetic Algorithm- Pattern Search to adjust the gains of the PI controllers in both static and dynamic irradiance scenarios for improving DC-link voltage.Results. Finally, this study presents comparison of DC-link voltage with six cases with manual tuning of PI controller, as well as PI controller by Genetic Algorithm- simulated Annealing, Genetic Algorithm- Pattern Search, Genetic Algorithm, Simulated Annealing and Pattern Search. The comparison showed by using Genetic Algorithm-Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.Discussion and Conclusion. The results achieved to strengthen the DC-link voltage under both static and dynamic irradiance conditions enable the sustaining of a constant DC-link voltage, which is essential for grid-connected photovoltaic systems. The comparison showed by using Genetic Algorithm- Simulated Annealing, peak overshoot in DC-link voltage is 829.3 V while peak overshoot in DC-link voltage is 1 052 V when DC-link voltage is controlled by manual tuning of PI as well as significant reduction in peak time and settling time in DC-link voltage.

According to the characteristics of self-anchored suspension bridge, a new method to detect damage is introduced in this paper.It works in two stages.First, a BP neural network model is built to predict damaged position. Next, based on the characteristics of genetic algorithm and simulated annealing algorithm, a new approach, genetic-simulated annealing algorithm, is put forward to identify damage extent of detected positions. Compared with the traditional genetic algorithm, the global convergence effect of this algorithm is enhanced by using of the Metropolis acceptance rule of the simulated annealing algorithm in the searching process.

In order to solve the complex illuminance modeling and calculation problem in indoor lighting environment, and improve the energy-saving effect of lighting optimization control algorithm. In this paper, a radial basis function neural network (RBFNN) illuminance model is proposed to simplify the calculation process of illuminance and the calculated illuminance value can provide data support for the optimization control calculation as the feedback link of optimization control. In this paper, a genetic simulated annealing algorithm is designed by combining genetic algorithm and simulated annealing algorithm to avoid the problem of traditional control algorithm falling into local optimal solution. Through the simulation verification of three lighting scenes with different personnel distribution, the traditional particle swarm optimization algorithm saves 46.00%, 38.00% and 37.11% energy respectively, while the genetic simulated annealing algorithm saves 47.22%, 46.67% and 41.78 energy respectively. It can be seen that the latter has a better energy-saving effect in the three scenes.