Improved Simulated Annealing Algorithm Research Articles

Performance Analysis of Different Optimization Algorithms for Multi-Class Object Detection

Object recognition is a significant approach employed for recognizing suitable objects from the image. Various improvements, particularly in computer vision, are probable to diagnose highly difficult tasks with the assistance of local feature detection methodologies. Detecting multi-class objects is quite challenging, and many existing researches have worked to enhance the overall accuracy. But because of certain limitations like higher network loss, degraded training ability, improper consideration of features, less convergent and so on. The proposed research introduced a hybrid convolutional neural network (H-CNN) approach to overcome these drawbacks. The collected input images are pre-processed initially through Gaussian filtering to eradicate the noise and enhance the image quality. Followed by image pre-processing, the objects present in the images are localized using Grid Guided Localization (GGL). The effective features are extracted from the localized objects using the AlexNet model. Different objects are classified by replacing the concluding softmax layer of AlexNet with Support Vector Regression (SVR) model. The losses present in the network model are optimized using the Improved Grey Wolf (IGW) optimization procedure. The performances of the proposed model are analyzed using PYTHON. Various datasets are employed, including MIT-67, PASCAL VOC2010, Microsoft (MS)-COCO and MSRC. The performances are analyzed by varying the loss optimization algorithms like improved Particle Swarm Optimization (IPSO), improved Genetic Algorithm (IGA), and improved dragon fly algorithm (IDFA), improved simulated annealing algorithm (ISAA) and improved bacterial foraging algorithm (IBFA), to choose the best algorithm. The proposed accuracy outcomes are attained as PASCAL VOC2010 (95.04%), MIT-67 dataset (96.02%), MSRC (97.37%), and MS COCO (94.53%), respectively.

Dynamic path planning of mobile robot based on improved simulated annealing algorithm

Dynamic path planning for mobile robots is an urgent issue that needs to be solved because of the growing use of mobile robots in daily life and industrial operations. This work focuses on avoiding moving obstacles in dynamic situations. The computational effort required by some current algorithms makes them difficult to utilize for path planning in dynamic situations whilst the computational effort required by other methods makes them simple yet prone to local minima. In this paper, an improved simulated annealing (SA) algorithm for dynamic path planning is proposed. To reduce its computational effort, the initial path selection method and deletion operation are introduced. Simulation results show the improved SA algorithm outperforms other algorithms and provides optimal solutions in static and dynamic environments.

Imbalanced Data Classification Using SVM Based on Improved Simulated Annealing Featuring Synthetic Data Generation and Reduction

Imbalanced data classification is one of the major problems in machine learning. This imbalanced dataset typically has significant differences in the number of data samples between its classes. In most cases, the performance of the machine learning algorithm such as Support Vector Machine (SVM) is affected when dealing with an imbalanced dataset. The classification accuracy is mostly skewed toward the majority class and poor results are exhibited in the prediction of minority-class samples. In this paper, a hybrid approach combining data pre-processing technique and SVM algorithm based on improved Simulated Annealing (SA) was proposed. Firstly, the data pre-processing technique which primarily aims at solving the resampling strategy of handling imbalanced datasets was proposed. In this technique, the data were first synthetically generated to equalize the number of samples between classes and followed by a reduction step to remove redundancy and duplicated data. Next is the training of a balanced dataset using SVM. Since this algorithm requires an iterative process to search for the best penalty parameter during training, an improved SA algorithm was proposed for this task. In this proposed improvement, a new acceptance criterion for the solution to be accepted in the SA algorithm was introduced to enhance the accuracy of the optimization process. Experimental works based on ten publicly available imbalanced datasets have demonstrated higher accuracy in the classification tasks using the proposed approach in comparison with the conventional implementation of SVM. Registering at an average of 89.65% of accuracy for the binary class classification has demonstrated the good performance of the proposed works.

Road Recognition Based on Vehicle Vibration Signal and Comfortable Speed Strategy Formulation Using ISA Algorithm.

When a vehicle is being driven, it is excited by the road roughness and generates its own vibration. In order to improve the vehicle’s riding comfort and the physical–mental health of passengers in the vehicle, this paper proposes a formulation method for a comfortable speed strategy and the technical route of its application. According to international standard ISO 2631-1, the relationship between the weighted root-mean-square acceleration value and comfortable vehicle speed is analyzed. The simulation test platform of the road roughness signal and vehicle vibration signal is built by using the filtering white noise method and the second Lagrange equation through Matlab/Simulink. Combined with the simulation platform, this paper extracts seven characteristics with statistical properties from the time-domain signal and obtains 500 sample data. Random forest (RF), extreme learning machine (ELM), and radial basis function neural network (RBF-NN) are applied to identify roads. Two comfortable speed strategy formulation methods based on the improved simulated annealing (ISA) algorithm are proposed and compared according to the solution effect of each grade of comfortable speed. The results show that the simulated signals of each grade road roughness are accurate. Road recognition can be effectively carried out using the statistical characteristics of vehicle vibration acceleration signals. ELM has high recognition accuracy and fast execution speed. The ISA-II algorithm has a low solution error of comfortable speed and a low computation time. The comfortable speed of the research vehicle on different road grades showed a great difference.

Improving robustness in interdependent networks under intentional attacks by optimizing intra-link allocation.

The interdependent network is particularly vulnerable to attacks on high degree nodes; therefore, the improvement of its robustness under intentional attacks has become an important topic. In this paper, we put forward a new metric to quantify the robustness of interdependent networks against intentional attacks and develop an improved simulated annealing algorithm (ISAA) to maximize this metric by optimizing the allocation of intra-links in subnetworks. Based on the comparison between the ISAA and existing algorithms, it is found that the algorithm presented in this paper is more effective to enhance the robustness of an interdependent scale-free network (ISFN). By applying the ISAA to ISFNs with different coupling preferences, there is a key finding that the robustness of the optimized ISFN is significantly stronger than that of the original ISFN. In particular, for cases of disassortative and random couplings, no sudden collapse occurs in optimized ISFNs. According to the analysis of the degree and the clustering coefficient, we find that the subnetwork of the optimized ISFN exhibits an onion-like structure. In addition, the ISFN whose robustness is enhanced to resist the attacks on high degree nodes is still robust to the intentional attacks concerning the betweenness and PageRank.

Distribution reconstruction of non-uniform combustion field based on improved simulated annealing algorithm

In this paper, a method of selecting the optimal transition lines’ combination is analyzed to measure the absorption spectrum of the non-uniform combustion flow field, which is used to solve the basic two-region distribution, and an improved simulated annealing algorithm (ISA) is proposed for reconstructing the field distribution of the combustion flow field, in order to solve the problems of slow convergence speed and low efficiency of the traditional simulated annealing algorithm. By modifying the model perturbation mode and annealing strategy, the efficiency of the algorithm and the chance to jump out of the local optimal space are further improved. According to the numerical simulation results, more transitions are helpful in improving the accuracy of combustion field reconstruction and making the reconstruction less sensitive to noise. It is worth noting that the optimal transitions’ combination is better than the non-optimal transitions’ combination with more transitions included. In this paper, three different combustion models are constructed to verify the effectiveness of the improved algorithm. A comparison between the reconstruction results of the traditional simulated annealing algorithm and the improved simulated annealing algorithm shows that both algorithms have the same precision but the latter algorithm has a higher operating efficiency, and a faster running time (nearly 40 times faster than the former algorithm). At the same time, the simulation results also show that the reconstruction accuracy will decrease slightly with the complication of combustion flow field. By building the TDLAS-HT measurement system in the laboratory and using 8 × 8 orthogonal optical path arrangement, the two different combustion states formed before and after placing the steel rod in the flat flame furnace are reconstructed, the results show that the reconstruction distribution is basically consistent with the original distribution, and the reconstructed distribution well shows the combustion characteristics of the original distribution of the flame field. The effectiveness of the proposed method is verified by numerical simulation and verification tests. Under the condition of the same reconstruction accuracy as the reconstruction accuracy of the traditional simulated annealing algorithm, the higher operating efficiency is helpful in reconstructing the rapidly changing turbulent field, which has some guiding significance for the hyperspectral reconstruction of temperature and concentration distribution in the combustion flow field.

Solving Bid-Based Dynamic Economic Dispatch in Competitive Electricity Market Using Improved Simulated Annealing Algorithm

Electricity markets are open after the deregulation of power systems due to competition. An optimization problem based on dynamic economic dispatch has recently come up in the new context of deregulated power systems known as bid-based dynamic economic dispatch (BBDED). It is one of the major operations and control functions in the electricity markets used to determine the optimal operations of market participants with scheduled load demands during a specified period. BBDED involves power generation companies (GENCOs) and customers to submit energy and price bids to the independent system operator (ISO) in a day-ahead market. The ISO clears the market with the objective of social profit maximization. In this paper, a BBDED problem is solved using an improved simulated annealing algorithm (ISA), including system constraints with different periods under bidding strategies. The proposed ISA technique is implemented in MATLAB and applied on a 3-unit system, a 6-unit system, and a 40-unit large-scale system. The proposed ISA is evaluated by comparison with relevant methods available in the literature, to demonstrate and confirm its potential in terms of convergence, robustness, and effectiveness for solving the BBDED problem.

Aerodynamic optimization of a SCO2 radial-inflow turbine based on an improved simulated annealing algorithm

Supercritical carbon dioxide plays a vital role in the development of power generation applications. It owns the characteristics of high density and low viscosity, which can ensure a compact structure for turbomachinery. With the blossom of optimization algorithm, an interdisciplinary research which applies optimization method to a traditional design process of turbomachinery can accelerate the course and promote the validity by leaps and bounds. We improve the traditional simulated annealing algorithm and establish an optimization process containing the optimization of rotor meridian plane and nozzle profile. This process can effectively reduce the computation time by establishing a surrogate model of coarse mesh simulation. The effects of traditional simulated annealing algorithm (SAA), genetic algorithm (GA) and improve simulated annealing algorithm (ISAA) are compared. As a result, we realize a maximum of 4.94% promotion for isentropic efficiency in ISAA computation. Also, ISAA method saves the computation time by 59.6% compared to GA and by 41.5% compared to SAA. Applying ISAA optimization method to the turbine in a kW-scale solar-driven Brayton cycle power system, we realize a 1.17% increase for the system efficiency.

Parameter identification of Bouc–Wen dynamic model for magnetorheological shimmy damper based on improved simulated annealing algorithm

Magnetorheological (MR) shimmy damper has a good application prospect in aircraft landing gear shimmy control as a semi-active vibration control device; however, its non-linear and hysteretic characteristics bring difficulties to the control and restrict performance. It is necessary to develop a dynamic model of the damper that can effectively show these characteristics. This study is based on the experimental data of the damping force characteristics of MR shimmy damper with different control currents. Bouc–Wen model, apply to describe non-linear and hysteretic characteristics, was selected to establish the dynamic model of damping force, displacement and velocity. This study proposed an improved simulated annealing (SA) algorithm, which can improve the efficiency of identification, to identify the parameters of the model. Comparing with the original algorithm, the improved SA algorithm has the same solution quality and better performance in computational efficiency. The relationships between the identified parameters and the control current were obtained by curve fitting, and the experimental data with different amplitudes and frequencies are used to verify the result. It is shown that the established model can accurately show the dynamic characteristics of the damper under different excitation.

An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

Research on HMCVT Efficiency Model Based on the Improved SA Algorithm

Research on the efficiency characteristic of the hydromechanical continuous variable transmission (HMCVT) in tractors is key to obtaining optimal transmission, developing control strategies, and assessing efficiency. To ease and improve the accuracy of obtaining the efficiency completely based on test measurements or theoretical calculation, this study proposes a method for building the HMCVT efficiency model. The method is based on an improved simulated annealing (SA) algorithm according to a small amount of test data. The study uses 8 groups of transmission efficiency values under different operating conditions obtained from bench tests. By theoretical analysis of the HMCVT, this study divides the total transmission efficiency into (i) the transmission efficiency from the output power of the power source to the confluence mechanism, (ii) the transmission efficiency of the confluence mechanism, and (iii) the transmission efficiency of the output part after confluence. The formulas for the three parts of transmission efficiencies are then derived. This study improves the SA algorithm and uses it to identify the three key parameters of hydraulic systems of the transmission efficiency calculation model. Research results indicate that the efficiency model built using the proposed method exhibits high accuracy with an error of about 1.90%. The improved SA algorithm can rapidly complete key parameter identification with an error of about 2.16%; when the displacement ratio is 0, the efficiency values at the same stage are approximately equal under different operating conditions. The HMCVT efficiency model can be built rapidly and effectively with only five groups of efficiency measurement tests.

Vehicle Routing Problems with Fuel Consumption and Stochastic Travel Speeds

Conventional vehicle routing problems (VRP) always assume that the vehicle travel speed is fixed or time-dependent on arcs. However, due to the uncertainty of weather, traffic conditions, and other random factors, it is not appropriate to set travel speeds to fixed constants in advance. Consequently, we propose a mathematic model for calculating expected fuel consumption and fixed vehicle cost where average speed is assumed to obey normal distribution on each arc which is more realistic than the existing model. For small-scaled problems, we make a linear transformation and solve them by existing solver CPLEX, while, for large-scaled problems, an improved simulated annealing (ISA) algorithm is constructed. Finally, instances from real road networks of England are performed with the ISA algorithm. Computational results show that our ISA algorithm performs well in a reasonable amount of time. We also find that when taking stochastic speeds into consideration, the fuel consumption is always larger than that with fixed speed model.

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.

Research on Application of a Type of Improved Simulated Annealing Algorithm for the Transit Vehicle Scheduling

Plan of transit vehicle scheduling is a complicated system engineering that is inuenced by multitudinous factors. The optimization model of transit vehicle scheduling has been built to minimize the total cost of passengers waiting for the transit vehicle and maximum the total benet of the bus company. Considering that the algorithm depended heavily on selected parameters, hybrid algorithm of the parameter setting of simulated annealing algorithm is proposed as a stochastic combinatorial optimization problem based on order and Nested Partitions (NP), on that basis, Improved Simulated Annealing algorithm (ISA) is designed to solve the model. Finally, a case study has been carried out taking Lanzhou as an example in order to testify validity of this model and its algorithm by using calculating and comparing analysis, and further solved practical problems under is analyzed. The results show that the results obtained by ISA are better than those obtained by SA and GA applied alone, The best solutions found so far of the ISA have less tness value than that of the other two.

An Improved Heuristic Algorithm for a Hybrid Flow-Shop Scheduling

This paper presents an improved metaheuristic algorithm to minimize the makespan in a hybrid flow-shop scheduling (HFS) with non-identical parallel machines. First, a mathematical model for an HFS problem is introduced. Second, an improved simulated annealing algorithm (ISAA) which is inspired from a hormone modulation mechanism is presented to retrofit speed and accuracy of the algorithm. Finally, the computer simulation demonstrates the good quality of the proposed procedure, and it outperforms several other algorithms.

Protein Folding Simulation Based on Improved Simulated Annealing Algorithm

Protein folding problem is one of the most important problems in bioinformatics. By combining simulated annealing method with pull moves which is a local move set and conformation update mechanism, we put forward an improved simulated annealing (ISA) algorithm for the two-dimensional hydrophobic- polar (2D HP) protein folding problem. Numerical results show that the ISA algorithm can find the known lowest-energy ground state more rapidly and efficiently than the genetic algorithm (GA) for the several given HP sequences. For the sequence with length 20, we obtain the lower-energy conformation than GA. The performance of the algorithm show ISA is an effective method for protein folding simulation

An Improved Simulated Annealing Algorithm for Real-Time Dynamic Job-Shop Scheduling

Job-shop scheduling is one of the core research aspects of Manufacturing Execution System (MES). It is significant for improving the utilization of enterprise resources, enhancing product quality, shortening delivery periods, reducing product cost, and raising enterprise competitive power in market economy. In order to solve this problem, Simulated Annealing (SA) algorithm is improved to solve large-scale combinatorial problem of job-shop scheduling. To make the SA algorithm more effective to solve job-shop scheduling problems, a solution encoding mode, scheduling scheme generation, initial temperature selection, temperature updating function, Markov chain length, end rule, and so on of the improved SA algorithm are discussed that affect the computation speed and convergence of the SA algorithm. Finally, the improved SA algorithm is validated by a job–shop scheduling problem of 10 workpieces and 10 machines.

Improved global–local simulated annealing formulation for solving non-smooth engineering optimization problems

This paper is concerned with a novel optimization algorithm that implements an enhanced formulation of simulated annealing (SA). The new algorithm is denoted as ISA (improved simulated annealing) in the rest of the paper. ISA includes a two-level random search: “global annealing” where all design variables are perturbed simultaneously and “local annealing” where design variables are perturbed one at a time. The improvement with respect to classical SA is in the fact that trial designs are generated always taking care to choose directions along which the cost function may improve. To this purpose, cost function sensitivities are computed in order to properly choose the size of each random perturbation. In addition, the optimization problem is linearized about the current design point if the optimizer ends up in an infeasible region or there is no significant reduction in cost even though the cost function gradient is not close to zero. The linearization is controlled by a trust region model. The optimization algorithm continuously shifts from global to local annealing based on the current best record at the beginning of each cooling cycle. Finally, the cooling schedule is automatically adjusted within ISA based on the convergence behavior. In this work, the ISA algorithm is successfully utilized to solve complicated optimization problems which exhibit non-smooth/non-convex behavior: (i) the large-scale (200 design variables and 3500 constraints) weight minimization of a 200 bar truss under five independent loading conditions; (ii) the configuration optimization of a cantilevered bar truss with 45 elements and 81 design variables; (iii) an example of reverse engineering where in-plane elastic properties of an eight-ply woven composite laminate are to be determined. The performance of ISA is compared to that of classical SA, gradient based optimization codes recently published in literature and commercial software. The results obtained in this study indicate that ISA is a very efficient optimization code. In fact, ISA was much faster than classical SA. The present code allowed about 300 kg weight saving in the 200 bar truss case and about 80 kg in the cantilevered bar truss case. In addition, the residual error on elastic constants in the material identification problem was less than 3%.