Simulated Annealing Mechanism Research Articles

SGOA: annealing-behaved grasshopper optimizer for global tasks

An improved grasshopper optimization algorithm (GOA) is proposed in this paper, termed as SGOA, which combines simulated annealing (SA) mechanism with the original GOA that is a natural optimizer widely used in finance, medical and other fields, and receives more promising results based on grasshopper behavior. To compare performance of the SGOA and other algorithms, an investigation to select CEC2017 benchmark function as the test set was carried out. Also, the Friedman assessment was performed to check the significance of the proposed method against other counterparts. In comparison with ten meta-heuristic algorithms such as differential evolution (DE), the proposed SGOA can rank first in the CEC2017, and also ranks first in comparison with ten advanced algorithms. The simulation results reveal that the SA strategy notably improves the exploration and exploitation capacity of GOA. Moreover, the SGOA is also applied to engineering problems and parameter optimization of the kernel extreme learning machine (KELM). After optimizing the parameters of KELM using SGOA, the model was applied to two datasets, Cleveland Heart Dataset and Japanese Bankruptcy Dataset, and they achieved an accuracy of 79.2% and 83.5%, respectively, which were better than the KELM model obtained other algorithms. In these practical applications, it is indicated that the proposed SGOA can provide effective assistance in settling complex optimization problems with impressive results.

Repetitive Nearest Neighbor Based Simulated Annealing Search Optimization Algorithm for Traveling Salesman Problem

The traveling salesman problem (TSP) is the most popular and most studied non-deterministic polynomial (NP) hard problem that has been used in various fields of science and technology. Due to the NP-hard nature, it is very difficult to solve this problem effectively and efficiently. For this reason, diverse appropriate optimization algorithms have been designed and developed in the last few decades. Among these algorithms, heuristic algorithms are much more suitable to tackle with this complex problem. In this paper, we propose a hybrid heuristic algorithm to solve the symmetric TSP problem by combining the search mechanism of repetitive nearest neighbor (RNN) heuristic and simulated annealing (SA) heuristic algorithms. In fact, a set of better routes are generated step by step by the RNN algorithm and these routes are improved through the iterative improvement process of the SA algorithm. The proposed algorithm is tested on a set of benchmark symmetric TSP datasets and compared with the basic RNN and SA algorithms as well as some other hybrid algorithms existing in the literature. It is demonstrated by the experimental results that the proposed algorithm is more effective than both the basic RNN and SA algorithms, and the obtained optimum results are in good agreement with the corresponding best-known optimum results. In addition, the proposed algorithm outperforms some other hybrid algorithms in terms of solution quality.

Solar Irradiance Prediction Model design by hybrid of ANFIS with Simulated Annealing Mechanism

Solar Irradiance Prediction Model design by hybrid of ANFIS with Simulated Annealing Mechanism

GSAPSO-MQC:medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system

Due to the large amount of image information data, high redundancy and high pixel correlation, the traditional medical image encryption algorithm is easy to be attacked by chosen plaintext. Therefore, a new medical image encryption algorithm combining genetic simulated annealing particle swarm optimization and modified quantum chaos system is proposed to obtain better security performance. Firstly, an improved quantum chaotic system is used to generate the key stream. Then the selection and cross operation of genetic algorithm are used to process the plaintext image. The optimal sequence generated by simulated annealing algorithm is used to scramble the image. Meanwhile, the particle swarm optimization (PSO) algorithm is introduced into the simulated annealing mechanism. The initial temperature is set according to the optimal fitness value of the initial population. Metropolis is used to optimize the generation of individual optimal position and global optimal position, and the inertial weight parameters of PSO algorithm are optimized to avoid particles falling into local optimal in the optimization process and improve the convergence speed of the algorithm. Through these three operations, the histogram of the scrambled image can be equalized to resist statistical attack. Experimental results and performance analysis show that the encryption system proposed in this paper can resist many typical attacks such as histogram analysis, correlation analysis, differential attack and violent attack, and has high security and encryption efficiency. Compared with other encryption methods, the encryption efficiency of our proposed method has improved by approximately 10%.

Transmit Antenna Selection and Beamformer Design for Secure Spatial Modulation With Rough CSI of Eve

The security of spatial modulation (SM) aided networks can always be improved by reducing the desired link's power at the cost of degrading its bit error ratio performance and assuming the power consumed to artificial noise (AN) projection (ANP). We formulate the joint optimization problem of maximizing the secrecy rate (Max-SR) over the transmit antenna selection and ANP in the context of secure SM-aided networks, which is mathematically a non-linear mixed integer programming problem. In order to solve this problem, we provide a pair of solutions, namely joint and separate solutions. Specifically, an accurate approximation of the SR is used for reducing the computational complexity, and the optimal AN covariance matrix (ANCM) is found by convex optimization for any given active antenna group (AAG). Then, given a large set of AAGs, simulated annealing mechanism is invoked for optimizing the choice of AAG, where the corresponding ANCM is recomputed by this optimization method as well when the AAG changes. To further reduce the complexity of the above-mentioned joint optimization, a low-complexity two-stage separate optimization method is also proposed. Furthermore, when the number of transmit antennas tends to infinity, the Max-SR problem becomes equivalent to that of maximizing the ratio of the desired user's signal-to-interference-plus-noise ratio to the eavesdropper's. Thus our original problem reduces to a fractional programming problem, hence a significant computational complexity reduction can be achieved for the optimization problem. Our simulation results show that the proposed algorithms outperform the existing leakage-based null-space projection scheme in terms of the SR performance attained, and drastically reduces the complexity at a slight SR performance reduction.

Service Deployment of C4ISR Based on Genetic Simulated Annealing Algorithm

This study considers a service-deployment problem of C4ISR(Command, Control, Communication, Computer, Intelligence, Surveillance and Reconnaissance) system under service-oriented architecture. In order to make C4ISR sufficiently agile to meet the challenges brought by diverse combat missions and highly adversarial combat processes, this paper adopts a distributed deployment method to deploy the service processes that constitute C4ISR in multiple data centers. Considering the service information requirements and the location of specific data, a deployment constraint for a service is proposed. Further, in order to improve resource utilization and reduce communication overhead, this paper establishes a distributed service deployment model facing the characteristics of C4ISR. For the large search space and complex constraints of the optimal problem, a multi-objective genetic simulated annealing algorithm is proposed by designing the neighboring individual generation rules and combining the simulated annealing mechanism and the genetic algorithm to enhance the ability of both local search and global search. The simulation results show that this method can produce a high-quality C4ISR service-deployment scheme effectively.

Application of simulated annealing genetic algorithm-optimized back propagation (BP) neural network in fault diagnosis

Optimal weights are usually obtained in neural network through a fixed network conformation, which affects the practicality of the network. Aiming at the shortage of conformation design and weight training algorithm in neural network application, the back propagation (BP) neural network learning algorithm combined with simulated annealing genetic algorithm (SAGA) is put forward. The multi-point genetic optimization of neural network topology and network weights is performed using hierarchical coding schemes and genetic operations. The simulated annealing mechanism is incorporated into the Genetic Algorithm (GA) to optimize the design and optimization of neural network conformation and network weights simultaneously. The SAGA takes advantage of GA excellent ability in grasping the overall ability of the search process, also uses the SA algorithm to control the convergence of the algorithm to avoid premature phenomenon. The fault diagnosis of one certain on-board electrical control box of helicopter and one certain flight control box of aircraft autopilot were used as a test platform to simulate the algorithm. The simulation conclusions reveal that the algorithm has good convergence rate and high diagnostic accurateness.

Reconstruction of Complex Networks Under Missing and Spurious Noise Without Prior Knowledge

The problem of network reconstruction under missing and spurious interactions has attracted widespread interests, for it currently appears in a myriad of contexts. However, the existing methods require assuming that prior knowledge is known, which is not always available in realistic situations. The approach of stochastic block models (SBMs) can overcome this limitation, but it is very time-consuming and liable to trap in a local optimum. Specifically, we have to passively wait for several days or longer when it is applied to relatively big networks. In this paper, we study the heuristic mechanism of SBM and reveal that its greedy strategy may lead to the drop of the solution speed and optimality. Accordingly, we propose a novel network reconstruction method, called enhanced SBM (ESBM), with the assistance of the simulated annealing mechanism. By comparing ESBM with SBM in both synthetic and real networks, the experimental results suggest that the reconstructed network's global properties of the ESBM can be closer to those of the true network than those of the SBM. Furthermore, the ESBM can flexibly adjust the convergence time according to the actual speed demands. We are surprised to find that, by slightly sacrificing the prediction accuracy, the calculation speed of ESBM can be more than ten times faster than that of SBM. Our results may also shed light on maximum-likelihood methods for large-scale networks' reconstruction.

A novel optimal method of robotic weld assembly line balancing problems with changeover times: a case study

PurposeThe balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.Design/methodology/approachThis research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.FindingsTheory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.Research limitations/implicationsThe additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.Originality/valueFor the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

A New Supervised Learning Algorithm Based on Genetic Inheritance for Spiking Neural Networks

Spiking neural networks (SNNs) can perform complex spatio-temporal information computations in precise temporal coding. These networks differ from previous models in that spiking neurons convey information by time rather than rate of spikes. Most existing training algorithms are based on gradient descent with inherent defects, such as local optimum and over-fitting. In this paper, we investigate the performance of the Genetic Algorithm Involving Mechanism of Simulated Annealing, as a supervised training algorithm for SNNs. The key idea is to adopt global search, which effectively avoid local optima and over-fitting. According to the experiment results, this approach has higher accuracy than other learning algorithms on well-known classification problems.

Application of dam deformation prediction based on LSSVR optimized by ASA-ABC

In order to improve the prediction accuracy of the dam deformation, aiming at the difficulty to determining the parameters of the least squares support vector regression (LSSVR) and the artificial bee colony algorithm (ABC) is prone to fall into local optimum when optimizing parameters, a self-adaptive simulated annealing mechanism is designed to improve the optimization performance of the ABC algorithm, so that a prediction model based on least squares support vector regression optimized by adaptive simulated annealing artificial bee colony (ASA-ABC-LSSVR) is constructed, and the model is applied to dam deformation prediction. The experimental results show that ASA-ABC effectively solves the difficult to balance the development and exploration capability of ABC. Compared with the prediction model based on the LSSVR and the model based on the LSSVR optimized by ABC (ABC-LSSVR), the ASA-ABC-LSSVR model has higher prediction accuracy and the prediction trend is more practical.

Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm

Scheduling in foundry consist a type of production where, the hot work-in-processes cannot wait between two successive operations and can be modeled as a flow shop scheduling problem with no-wait constraint. With the objective to reduce total flow time, the appropriate sequence of jobs for scheduling is essential, hence the problem can be observed as typical NP-hard combinatorial optimization problem. This paper, proposes hybridization of Particle Swarm Optimization with simulated annealing for planning and scheduling issues which are very complex because of the ever changing needs of customers and existing constraints in foundry. This Proposed Hybrid Particle Swarm Optimization algorithm represents solution by random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed hybrid particle swarm optimization algorithm initializes population efficiently with Nawaz-Enscore-Ham heuristic and uses evolutionary search guided by the mechanism of PSO and local search by mechanism of simulated Annealing by balancing both global exploration and local exploitation. The proposed hybrid particle swarm optimization algorithm try to bridge the gap between theory and practice by considering foundry environment, which will help planner to decide the sequence of production of jobs based against clients’ orders and to develop efficient scheduling procedures for minimizing total flow time with relatively low computational efforts. Extensive computational experiments are carried out based on various casting’s (job’s) characteristics viz. casting type, mould size and type of alloy, where size of job (n) considered as 10,12,20,50 and 100. With respect to performance measure, Average Relative Percent Deviation which is popular in the scheduling literature, the proposed method performs better than Simulated Annealing and Particle Swarm Optimization.

PageRank centrality for performance prediction: the impact of the local optima network model

A local optima network (LON) compresses relevant features of fitness landscapes in a complex network, where nodes are local optima and edges represent transition probabilities between different basins of attraction. Previous work has found that the PageRank centrality of local optima can be used to predict the success rate and average fitness achieved by local search based metaheuristics. Results are available for LONs where edges describe either basin transition probabilities or escape edges. This paper studies the interplay between the type of LON edges and the ability of the PageRank centrality for the resulting LON to predict the performance of local search based metaheuristics. It finds that LONs are stochastic models of the search heuristic. Thus, to achieve an accurate prediction, the definition of the LON edges must properly reflect the type of diversification steps used in the metaheuristic. LONs with edges representing basin transition probabilities capture well the diversification mechanism of simulated annealing which sometimes also accepts worse solutions that allow the search process to pass between basins. In contrast, LONs with escape edges capture well the diversification step of iterated local search, which escapes from local optima by applying a larger perturbation step.

Estimation of Distribution Algorithm with Local Sampling Strategy for Community Detection in Complex Networks

It is important to discover the potential community structure for analyzing complex networks. In this paper, an estimation of distribution algorithm with local sampling strategy for community detection in complex networks is presented to optimize the modularity density function. In the proposed algorithm, the evolution probability model is built according to eminent individuals selected by simulated annealing mechanism and a local sampling strategy based on a local similarity model is adopted to improve both the speed and the accuracy for detecting community structure in complex networks. At the same time, a more general version of the criterion function with a tunable parameter λ is used to avoid the resolution limit. Experiments on synthetic and real-life networks demonstrate the performance and the comparison of experimental results with those of several state-of-the-art methods, the proposed algorithm is considerably efficient and competitive.

A Phoenix++ Based New Genetic Algorithm Involving Mechanism of Simulated Annealing

Genetic algorithm is easy to fall into local optimal solution. Simulated annealing algorithm may accept nonoptimal solution at a certain probability to jump out of local optimal solution. On the other hand, lack of communication among genes in MapReduce platform based genetic algorithm, the high-performance distributed computing technologies or platforms can further increase the execution efficiency of these traditional genetic algorithms. To this end, we propose a novel Phoenix++ based new genetic algorithm involving mechanism of simulated annealing. Simulated annealing genetic algorithm has two distinctive characteristics. First, it is the synthesis of the conventional genetic algorithm and the simulated annealing algorithm. This characteristic guarantees our proposed algorithm has a higher probability of getting the global optimal solution than traditional genetic algorithms. The other is that our algorithm is a parallel algorithm running on the high-performance parallel platform Phoenix++ instead of a conventional serial genetic algorithm. Phoenix++ implements the MapReduce programming model that processes and generates large data sets with our parallel, distributed algorithm on a cluster. The experiments indicate that the convergence speed of GA algorithm is significantly faster after adding the simulated annealing algorithm on Phoenix++ platform.

A hybrid backtracking search algorithm for permutation flow-shop scheduling problem

The Permutation Flow-shop Scheduling Problem (PFSP) which is an NP-complete problem widely exists in many industrial manufacturing systems, such as motor industry, semiconductor industry, and appliance industry. Therefore, how to obtain the optimal schedule for PFSP is very important for these manufacturing systems. Many attentions from researchers and engineers have been paid to solve this problem, but the developments of more effective and efficient scheduling technologies and methods are never end. In this paper, based on a new evolutionary algorithm – Backtracking Search Algorithm (BSA), a hybrid BSA (HBSA) is proposed for PFSP with the objective to minimize the makespan. To make original BSA suitable for discrete problems, some improvements and relative techniques about original BSA, such as crossover and mutation strategies, simulated annealing (SA) mechanism used to avoid premature and random insertion local search, are presented. 29 famous benchmark problems have been used to evaluate the performance of the proposed HBSA. And several comparisons between HBSA and some other classical algorithms are conducted. The results show the effectiveness of proposed HBSA.

Efficient simultaneous synthesis for heat exchanger network with simulated annealing algorithm

The simultaneous synthesis of heat exchanger network is primarily regarded as mixed integer nonlinear programming models and its combinatorial nature can easily lead to a suboptimal network design and unmanageable computational effort. Based on simulated annealing algorithm, this paper presents an efficient simultaneous synthesis method that provides the optimal networks in a two-level procedure. Two probability models are proposed to generate candidate structures by random perturbations in the upper level. The minimum total annualized cost of each candidate structure is solved in the lower level and then sent to the upper level where different structures are evaluated by simulated annealing mechanism. This two-level method is applied to solve four benchmark cases of heat exchanger network synthesis and the computational results show that satisfactory heat exchanger network designs can be guaranteed with reasonable cooling schedules and two most economical networks with no splits are solved for case 1 and case 3.

Target detection approach for UAVs via improved Pigeon-inspired Optimization and Edge Potential Function

In this paper, the hybrid model of Edge Potential Function (EPF) and Simulated Annealing Pigeon-inspired Optimization (SAPIO) algorithm is proposed to accomplish the target detection task for Unmanned Aerial Vehicles (UAVs) at low altitude. EPF can be calculated from the edge map of the original image and provide a kind of attractive pattern for the given target, which is conventionally exploited by the optimization algorithms. Pigeon-inspired Optimization (PIO) is a novel bio-inspired computation algorithm, which was inspired from the homing characteristics of pigeons. In this paper, the simulated annealing mechanism is adopted in our SAPIO algorithm for maximizing the value of EPF. A series of comparative experiments with standard Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony Optimization (ABC) and PIO algorithms demonstrate the robustness and effectiveness of our SAPIO algorithm. Meanwhile, the proposed approach can guarantee accurate target matching.

A Particle Swarm Optimization Algorithm Based on Simulated Annealing

The passage aims at solving the problems resulted from the optimized process of Particle Swarm Optimization (PSO), which might reduce the population diversity, cause the algorithm to convergence too early, etc. A whole new mutable simulated annealing particle swarm optimization is proposed based on the combine of the simulated annealing mechanism and mutation. This new algorithm substitutes the Metropolis criterion in the simulated annealing mechanism for mutagenic factors in the process of mutation, which both ensures the diversity of the particle swarm, and ameliorates the quality of the swarm, so that this algorithm would convergence to the global optimum. According to the result of simulated analysis, this hybrid algorithm maintains the simplicity of the particle swarm optimization, improves its capability of global optimization, and finally accelerates the convergence and enhances the precision of this algorithm.

Study on Hybrid Genetic Algorithm for Capacitated Vehicle Routing Problem

Capacitated vehicle routing problem is logistics optimization indispensable part. The hybrid genetic algorithm is used to optimize the solution. Firstly, use sequence of real numbers coding so as to simplify the problem; Construct the initial solution to improve the feasibility; adopt some arithmetic crossover operator to enhance whole search ability of the chromosome. Secondly, use Boltzmann simulated annealing mechanism to improve the convergence speed and search efficiency. Finally, comparing to other algorithms, the results demonstrate the effectiveness and good quality.