Chaotic Simulated Annealing Research Articles

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

A Chaotic Simulated Annealing Genetic Algorithm with Asymmetric Time for Offshore Wind Farm Inspection Path Planning

Quantitative Identification Method for Glass Panel Defects Using Microwave Detection Based on the CSAPSO-BP Neural Network.

To address the problem of the quantitative identification of glass panel surface defects, a new method combining the chaotic simulated annealing particle swarm algorithm (CSAPSO) and the BP neural network is proposed for the quantitative evaluation of microwave detection signals of glass panel defects. First, the parameters of the particle swarm optimization (PSO) algorithm are dynamically assigned using chaos theory to improve the global search capability of the PSO. Then, the CSAPSO-BP neural network model is constructed, and the return loss and phase of the microwave detection echo signal of glass panel defects are extracted as the input feature quantity of the network, from which the intrinsic connection between input and output is found through network training and testing to achieve the prediction of the depth and width of glass panel surface defects. The results show that the CSAPSO-BP network model can more accurately characterize the defect geometry of glass panels than the PSO-BP network model.

Chaotic simulated annealing multi-verse optimization enhanced kernel extreme learning machine for medical diagnosis

Classification models such as Multi-Verse Optimization (MVO) play a vital role in disease diagnosis. To improve the efficiency and accuracy of MVO, in this paper, the defects of MVO are mitigated and the improved MVO is combined with kernel extreme learning machine (KELM) for effective disease diagnosis. Although MVO obtains some relatively good results on some problems of interest, it suffers from slow convergence speed and local optima entrapment for some many-sided basins, especially multi-modal problems with high dimensions. To solve these shortcomings, in this study, a new chaotic simulated annealing overhaul of MVO (CSAMVO) is proposed. Based on MVO, two approaches are adopted to offer a relatively stable and efficient convergence speed. Specifically, a chaotic intensification mechanism (CIP) is applied to the optimal universe evaluation stage to increase the depth of the universe search. After obtaining relatively satisfactory results, the simulated annealing algorithm (SA) is employed to reinforce the capability of MVO to avoid local optima. To evaluate its performance, the proposed CSAMVO approach was compared with a wide range of classical algorithms on thirty-nine benchmark functions. The results show that the improved MVO outperforms the other algorithms in terms of solution quality and convergence speed. Furthermore, based on CSAMVO, a hybrid KELM model termed CSAMVO-KELM is established for disease diagnosis. To evaluate its effectiveness, the new hybrid system was compared with a multitude of competitive classifiers on two disease diagnosis problems. The results demonstrate that the proposed CSAMVO-assisted classifier can find solutions with better learning potential and higher predictive performance.

Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.

Transiently chaotic simulated annealing based on intrinsic nonlinearity of memristors for efficient solution of optimization problems.

Optimization problems are ubiquitous in scientific research, engineering, and daily lives. However, solving a complex optimization problem often requires excessive computing resource and time and faces challenges in easily getting trapped into local optima. Here, we propose a memristive optimizer hardware based on a Hopfield network, which introduces transient chaos to simulated annealing in aid of jumping out of the local optima while ensuring convergence. A single memristor crossbar is used to store the weight parameters of a fully connected Hopfield network and adjust the network dynamics in situ. Furthermore, we harness the intrinsic nonlinearity of memristors within the crossbar to implement an efficient and simplified annealing process for the optimization. Solutions of continuous function optimizations on sphere function and Matyas function as well as combinatorial optimization on Max-cut problem are experimentally demonstrated, indicating great potential of the transiently chaotic memristive network in solving optimization problems in general.

Study about Optimization Scheduling Method of Economic Benefit for Grid‐Connected Household Photovoltaic Systems

In order to improve the economic benefit of household photovoltaic (PV) system users under time‐of‐use (TOU) price and enhance their ability to cut peaks and fill valleys for main grid, an optimization method based on dynamic tie‐line transmission power is proposed in this paper, which can be applied to different weather conditions. First, an optimization scheduling model of economic benefit for a grid‐connected PV system is put forward, with the final revenue of user as the objective function. Under the TOU price policy, this model fully considers economic factors such as energy income, energy cost, government subsidy, system maintenance cost, etc. Moreover, the dynamic tie‐line transmission power is taken as the optimization variable, and the improved chaotic simulated annealing particle swarm optimization algorithm is used to solve the optimization problem. Finally, a typical household grid‐connected PV system is taken as study case, and convincing results are obtained to verify the proposed model and optimal strategy. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Efficiency analysis for stochastic dynamic facility layout problem using meta‐heuristic, data envelopment analysis and machine learning

AbstractThe facility layout problem (FLP) is a combinatorial optimization problem. The performance of the layout design is significantly impacted by diverse, multiple factors. The use of algorithmic or procedural design methodology in ranking and identification of efficient layout is ineffective. In this context, this study proposes a three‐stage methodology where data envelopment analysis (DEA) is augmented with unsupervised and supervised machine learning (ML). In stage 1, unsupervised ML is used for the clustering of the criteria in which the layouts need to be evaluated using homogeneity. Layouts are generated using simulated annealing, chaotic simulated annealing, and hybrid firefly algorithm/chaotic simulated annealing meta‐heuristics. In stage 2, the nonparametric DEA approach is used to identify efficient and inefficient layouts. Finally, supervised ML utilizes the performance frontiers from DEA (efficiency scores) to generate a trained model for getting the unique rankings and predicted efficiency scores of layouts. The proposed methodology overcomes the limitations associated with large datasets that contain many inputs / outputs from the conventional DEA and improves the prediction accuracy of layouts. A Gaussian distribution product demand dataset for time period T = 5 and facility size N = 12 is used to prove the effectiveness of the methodology.

Design of Safe Operation Scheme of Charging Station Based on AI Particle Swarm Optimization

In view of the safe and optimized operation of urban EV charging stations, this paper considers satisfying travel needs of users as the premise, improving social and economic benefits as the means, and aimed at ensuring the safe and optimal operation of EVs, puts forward the design scheme of EV charging station that considers user satisfaction, cost of investment and operation, and cost of failure operation and maintenance. The scheme establishes a charging station design model with the goal of minimizing the total social cost. The AI particle swarm optimization algorithm based on chaotic simulated annealing is used to solve the problem, and the chaos theory is introduced for dynamic assignment. Combined with the probability of sudden jump of the simulated annealing algorithm, the algorithm has a higher global optimization ability. The validity and practicability of the design scheme are verified by an example.

FANCFIS: Fast adaptive neuro-complex fuzzy inference system

Large-scale multivariate time series forecasting is an increasingly important problem, with sensor networks pouring out sampled data at unprecedented rates, trillions of dollar's worth of stock trades made every data, and zettabytes of traffic transmitted over the Internet every year. While they are only examples of the much larger domain of general data streams, the uniformly-sampled time series still remains a very large and important subdomain. Extensive research has shown that machine-learning algorithms can often be very effective forecasting models, but many of these algorithms do not scale well. The Adaptive Neuro-Complex-Fuzzy Inferential System is one such approach; built to leverage complex fuzzy sets, it is both an accurate and parsimonious forecasting algorithm. However, its scaling is poor due to a relatively slow training algorithm (gradient descent hybridized with chaotic simulated annealing). Before the algorithm can be used for large-scale forecasting, a fast training algorithm that preserves the system's accuracy and compactness must be developed.We propose the Fast Adaptive Neuro-Complex Fuzzy Inference System, which is designed for fast training of a compact, accurate forecasting model. We use the Fast Fourier Transform algorithm to identify the dominant frequencies in a time series, and then create complex fuzzy sets to match them as the antecedents of complex fuzzy rules. Consequent linear functions are then learned via recursive least-squares. We evaluate this algorithm on both univariate and multivariate time series, finding that this incremental-learning algorithm is as accurate and compact as its slower predecessor, and can be trained much more quickly.

A chaotic simulated annealing and particle swarm improved artificial immune algorithm for flexible job shop scheduling problem

Reasonable scheduling of flexible job shop is key to improve production efficiency and economic benefits; in order to solve the problem in flexible job shop scheduling problem, a novel flexible job shop scheduling method based on improved artificial immune algorithm is proposed. Firstly, a mathematical model of the flexible job shop scheduling is established, and the total shortest processing time is taken as the objective function. Secondly, artificial immune algorithm is used to solve the problem, and particle swarm optimization algorithm is taken as the operator to embed into manual immune algorithm for maintaining the diversity of population and prevent obtaining local optimal solution. Finally, the performance of the algorithm is tested by simulation experiments on standard set. The results show that the proposed algorithm can obtain better flexible job shop scheduling scheme and especially has more significant advantages in solving large-scale problems in comparison with other algorithms.

Formulating multi-objective stochastic dynamic facility layout problem for disaster relief

Facility layout problem is an NP-hard problem where two basic approaches have commonly been used to optimize layouts i.e. qualitative and quantitative. Qualitative aspect provide layout based on maximizing of closeness rating while quantitative involve minimization of material handling cost. Previous work undertaken by scholars in which they have studied or focussed on optimizing product flow to optimize both objectives either considering constant or varying demand across all period. Here, the facility layout problem is addressed from the point of demand based disaster for disaster relief operations. Therefore, the paper first presents mathematical formulation for multi objective stochastic dynamic facility layout problem (MO-SDFLP) where the product demand is varying over multiple periods for multiple products. Huge demand variations in multiple products across multiple periods may cause disaster as it can impact heavily the supply chain. Hence, the layout problem considered here mainly focus on considering product demand variations to capture disaster for disaster relief operations. MO-SDFLP being NH-hard is solved using simulated annealing (SA) and chaotic simulated annealing (CSA) meta-heuristics. The paper reports results of SA and CSA algorithms using data sets available in literature for facility size N = 12, period T = 5 and Gaussian distribution product demand. In addition, to show the applicability of MO-SDFLP for bigger demand based disaster, SA and CSA is also tested on bigger problem i.e. N = 30 for T = 5. It is observed that CSA performs better than SA.

Application of Adaptive Chaotic Simulated Annealing Network to Uplink Power Control in Asynchronous CDMA System

A kind of adaptive Chaotic Stimulated Annealing (CSA) network approach used in the uplink power control of asynchronous Code Division Multiple Access (CDMA) mobile communication system is proposed in this study. The particular influence of near-far effect can be overcome by utilizing the global optimal searching function of CSA method, and the Multiple Access Interference (MAI) in certain cell can be conquered by introducing adaptive multiuser interference cancellation network in corresponding close loop control strategy. Computer simulations give the comparison results between proposed approach and conventional method with heterogeneous Signal-to- Interference Ratio (SIR) threshold decision. It shows that this new approach can achieve higher channel capability and lower average outrage probability with some small increase of iteration steps, which can improve the performance of CDMA mobile communication system effectively.

Designing Flexible Stochastic Dynamic Layout: An Integrated Firefly and Chaotic Simulated Annealing-Based Approach

Facility layout problem (FLP) is non-deterministic and a polynomial time problem (NP-hard). In practice, the FLP is affected due to the presence of fluctuating products demand and thus results in increased material handling cost. Such layout problems are known as stochastic dynamic facility layout problem (SDFLP). It is well known that the computational time to solve SDFLP is exponential; therefore, it is hard to solve using exact approaches. Various meta-heuristic algorithms are used for solving such problems. The aim of the paper is to formulate a novel meta-heuristic for solving SDFLP, which minimizes the material handling cost better than the prevailing meta-heuristic. A hybridized methodology of combining firefly algorithm (FA) and chaotic simulated annealing (CSA), i.e. hybrid FA/CSA is proposed and evaluated to find an optimal solution for SDFLP. The performance of the algorithm, in terms of total material handling cost, is compared with simulated annealing, chaotic simulated annealing and hybrid ant colony optimization/simulated annealing using data from the literature.

Integrating big data analytic and hybrid firefly-chaotic simulated annealing approach for facility layout problem

Manufacturing industries have become larger, diverse and the factors affecting a facility layout design have grown rapidly. Handling and evaluating these large set of criteria (factors) is difficult in designing and solving facility layout problems. These factors and uncertainties have a large impact on manufacturing time, manufacturing cost, product quality and delivery performance. In order to operate efficiently, these facilities should adapt to these variations over multiple time periods and this must be addressed while designing an optimal layout. This paper proposes a novel integrated framework by combining Big Data Analtics and Hybrid meta-heuristic approach to design an optimal facility layout under stochastic demand over multiple periods. Firstly, factors affecting a facility layout design are identified. The survey is conducted to generate data reflecting 3V’s of Big Data. Secondly, a reduced set of factors are obtained using Big Data Analytics. These reduced set of factors are considered to mathematically model a weighted aggregate objective for Multi-objective Stochastic Dynamic Facility Layout Problem (MO-SDFLP). Hybrid Meta-heuristic based on Firefly (FA) and Chaotic simulated annealing is used to solve the MO-SDFLP. To show the working methodology of proposed integrated framework an exemplary case is presented.

Functional Testing Using OCL Predicates to Improve Software Quality

Formal specification-based testing is one of the major criterions to detect faults in the requirement specification or design of a system. However, due to the non-availability of implementation details, test case generation from formal specifications become a challenging task. This research work has proposed an automated fault-based testing framework to test the specification of a system written in OCL (Object Constraint Language). Accordingly, the possible faults in OCL specification of a system have been anticipated by mutating its method based on predicate-based fault classes. Then, test cases are generated by applying Chaotic Simulated Annealing (CSA) algorithm. In this paper, a novel OCL predicate based fitness function is defined to evaluate the generated test data. The experiment results indicate that the OCL specification based testing adopting CSA form an excellent combination for performing functional testing at the earliest to improve software quality.

Hybridization of seasonal chaotic cloud simulated annealing algorithm in a SVR-based load forecasting model

Support vector regression with chaotic sequence and simulated annealing algorithm in previous forecasting research paper has shown its superiority to effectively avoid trapping into a local optimum. However, the proposed chaotic simulated annealing (CSA) algorithm in previous published literature as well as the original SA algorithm could not realize the mechanism of temperature decreasing continuously. In addition, lots of chaotic sequences adopt Logistic mapping function which is distributed at both ends in the interval [0,1], thus, it could not excellently strengthen the chaotic distribution characteristics. To continue exploring any possible improvements of the proposed CSA and chaotic sequence, this paper employs the innovative cloud theory to be hybridized with CSA to overcome the discrete temperature annealing process, and applies the Cat mapping function to ensure the chaotic distribution characteristics. Furthermore, seasonal mechanism is also proposed to well arrange with the cyclic tendency of electric load, caused by economic activities or climate cyclic nature. This investigation eventually presents a load forecasting model which hybridizes the seasonal support vector regression model and chaotic cloud simulated annealing algorithm (namely SSVRCCSA) to receive more accurate forecasting performance. Experimental results indicate that the proposed SSVRCCSA model yields more accurate forecasting results than other alternatives.

Fuzzy C-Means Image Segmentation Algorithm Based on Chaotic Simulated Annealing

Considering the problem that the traditional fuzzy c-means (FCM) image segmentation algorithm is often caught in a specific range in local search and fails to get the globally optimal solution, this paper proposed a modified FCM algorithm based on chaotic simulated annealing (CSA). It traverse all the states without repetition within a certain range to calculate the optimal solution. Experimental results show that our method converges more quickly and accurately to the global optimal and proves a promise global optimization method of high adaptability and feasibility.

Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm

Port throughput forecasting is a very complex nonlinear dynamic process, prediction accuracy is influenced by uncertainty of socio-economic factors, especially by the mixed noise (singular point) produced in the collection, transfer and calculation of statistical data; consequently, it is difficult to obtain a satisfactory port throughput forecasting result. Thus, establishing an effective port throughput forecasting scheme is still a significant research issue. Since the robust v-support vector regression model (RSVR) has the ability to solve the nonlinear and mixed noise in the port throughput history data and its related socio-economic factors, this paper introduces the RSVR model to forecast port throughput. In order to search the more appropriate parameters combination for the RSVR model, considering the proposed simulated annealing particle swarm optimization (SAPSO) algorithm and the original PSO algorithm still have the drawbacks of immature convergence and is time consuming, this study presents chaotic simulated annealing particle swarm optimization(CSAPSO) algorithm to determine the parameter combination. Aiming to identify the final input vectors for RSVR model, the multivariable adaptive regression splines (MARS) is adopted to select the final input vectors from the candidate input variables. This study eventually proposes a port throughput forecasting scheme that hybridizes the RSVR, CSAPSO and MARS to obtain a more accurate forecasting result. Subsequently, this study compiles the port throughput data and the corresponding socio-economic indicators data of Shanghai as the illustrative example to evaluate the feasibility and performance of the proposed scheme. The experimental results indicate that the proposed port throughput forecasting scheme obtains better forecasting result than the six competing models in terms of forecasting error.

Hydrothermal coordination by bi-level optimization and composite constraint handling method

This paper presents a new adaptive bi-level optimization approach to solve short-term hydrothermal coordination (STHTC) problem with AC network constraints. The proposed bi-level approach is composed of an integer coded genetic algorithm (ICGA) to solve unit commitment (UC) in the upper level and a new modified chaotic simulated annealing (MCSA) technique to determine economic dispatch (ED) for each time interval in the lower level. Moreover, a new composite constraint handling method is also presented, which enhances the efficiency of the proposed approach to solve STHTC problem with AC constraints. The proposed methodology is tested on the 9-bus and IEEE 118-bus test systems. The obtained results confirm the validity of the developed approach.

CHAOTIC SIMULATED ANNEALING FOR SOLVING STOCHASTIC DYNAMIC FACILITY LAYOUT PROBLEM

ABSTRACT Stochastic dynamic facility layout (SDFLP) is a Combinatorial Optimization facility layout problem. SDFLP is modeled as QAP where the product demand is random over multiple periods. This paper proposes a novel approach of solving SDFLP using Chaotic Simulated Annealing (CSA). Unlike conventional Simulated Annealing (SA) which uses Gaussian distribution, CSA uses chaotic initialization and chaotic sequences to find the optimal solution. To evaluate the performance of CSA on SDFLP two cooling schedules have been considered - Exponential and Cauchy. The paper reports results of the CSA algorithm used on data sets available in literature for facility size N=12 and time period T=5. To prove the efficacy of the proposed SDFLP solution, CSA is applied on facility size for N=15, 20, 25, 30 and time period T=5 and its results are discussed. Keywords Facility layout; stochastic dynamic facility layout; simulated annealing; cooling schedule; meta-heuristic; chaotic simulated annealing