Genetic Simulated Annealing Research Articles

A two-stage decision-support system for floating debris collection in reservoir areas

The accumulation of floating debris is one of the main challenges of water conservancy projects, which may pollute the vulnerable ecosystem of the reservoir area and impose significant risks on waterway transportation and dam operations. Due to the dynamicity and uncertainty caused by water flow, the collection of floating debris is much more complicated than on-land waste collection. In this paper, we propose a two-stage decision-support system to optimize the task allocation and routing decisions for floating debris collection in the reservoir area, where the first stage is proactive planning based on historical/observed data and the second stage is reactive planning based on real-time data. The primary objective is to minimize the total collection cost while simultaneously ensuring the accumulation areas with high risks are prioritized in the daily collection plan, and both genetic algorithm and simulated annealing algorithm are used to solve the optimization problems. The proposed method is validated with a real-world case study at Wushan County in the Three Gorges Reservoir area. The computational results show that the level of time-dependent penalty cost on service priority, the types of collection ships, and the number and locations of unloading points are important influencing factors to the cost and responsiveness. Furthermore, the proposed two-stage decision-support system can help effectively optimize the operational planning of floating debris collection in reservoir areas.

A memetic-based technical indicator portfolio and parameters optimization approach for finding trading signals to construct transaction robot in smart city era

With the development of smart cities, the demand for personal financial services is becoming more and more importance, and personal investment suggestion is one of them. A common way to reach the goal is using a technical indicator to form trading strategy to find trading signals as trading suggestion. However, using only a technical indicator has its limitations, a technical indicator portfolio is further utilized to generate trading signals for achieving risk aversion. To provide a more reliable trading signals, in this paper, we propose an optimization algorithm for obtaining a technical indicator portfolio and its parameters for predicting trends of target stock by using the memetic algorithm. In the proposed approach, the genetic algorithm (GA) and simulated annealing (SA) algorithm are utilized for global and local search. In global search, a technical indicator portfolio and its parameters are first encoded into a chromosome using a bit string and real numbers. Then, the initial population is generated based on the encoding scheme. Fitness value of a chromosome is evaluated by the return and risk according to the generated trading signals. In local search, SA is employed to tune parameters of indicators in chromosomes. After that, the genetic operators are continue employed to generate new offspring. Finally, the chromosome with the highest fitness value could be provided to construct transaction robot for making investment plans in smart city environment. Experiments on three real datasets with different trends were made to show the effectiveness of the proposed approach, including uptrend, consolidation, and downtrend. The total returns of them on testing datasets are 26.53% 33.48%, and 9.7% that indicate the proposed approach can not only reach risk aversion in downtrends but also have good returns in others.

A multi-objective complex product assembly scheduling problem considering transport time and worker competencies

The hybrid flow shop scheduling problem (HFSP) is a research hotspot in the shop scheduling problem. However, few studies have investigated HFSP involving parallel machine scheduling and worker competencies. Therefore, this study investigates a complex product assembly line scheduling problem considering parallel team scheduling, worker competencies, and transport time. The makespan, transport time, imbalance degree of team workload, and imbalance degree of cycle time are taken as optimization objectives. First, a mathematical model considering transport time and worker competencies is developed, and then a mixed encoding and decoding method is proposed. Second, an algorithm that hybridizes the genetic algorithm and simulated annealing algorithm is proposed, involving an improved Nawaz-Enscore-Ham heuristic method and a strengthened elite retention strategy to improve the quality of the solution. Finally, based on nine examples generated from real production statuses, the proposed algorithm is compared with the other four algorithms. The results show that the proposed algorithm is superior in terms of the quality and efficiency of solutions.

Analysis of metaheuristic optimisation techniques for simulated matrix production systems

Increasing demand for individualised products has led to the concept of mass customisation, combining high product variety with production efficiency coming along with mass production. Companies are moving to matrix production systems with complex product flows for mass customisation. One challenge in such systems is the determination of optimal system configurations to fulfil future demands while minimising production costs. An approach to determine the ideal configuration is to use metaheuristics like genetic algorithms or simulated annealing to optimise simulation models. However, it is unclear which methods are ideally suited to finding the best solutions. This contribution compares the performance of genetic algorithms and simulated annealing when optimising the configuration of a company-specific matrix production system using discrete event simulation. The methods are evaluated using different objective functions. For the genetic algorithm, different observation strategies are also tested. Overall, the simulated annealing approach delivers better results with shorter solution times. The contributing factors leading to the different results are discussed, and areas for future research are pointed out.

Core flow distribution optimization of a natural circulation reactor using genetic algorithm, simulated annealing and characteristic statistic algorithm

Optimization of core flow distribution is a critical issue in nuclear engineering, particularly for natural circulation reactors with limited driving force. By appropriately arranging the resistance at fuel assembly inlets, core flow distribution could be well matched with core power distribution, thereby reducing the non-uniformity of core outlet temperature distribution. This, in turn, could enhance the performance and safety of the reactor. However, the flow distribution optimization throughout the fuel life cycle is a complicated global optimization problem that entails a vast solution space. In this paper, the flow distribution optimization with minimizing the maximum-to-minimum outlet temperature difference as the primary optimization objective, has been carried out incorporating various meta-heuristic algorithms with the core thermal-hydraulic analysis code COBRA, for a 200 MW natural circulation light water reactor. Minimizing the maximum-to-average outlet temperature difference is also chosen as an optimization objective since a smaller maximum outlet temperature is desirable. After the parametric sensitivity analysis of the genetic algorithm, the simulated annealing, and the characteristic algorithm, a set of parameters of each algorithm for the current flow distribution optimization problem has been determined. The results show that different algorithms yield similar resistance schemes under both optimization objectives, and all exhibit satisfying optimization effects, among which simulated annealing converges to the global optimum with significantly less computational costs.

MATHEMATICAL MODELLING AND OPTIMIZATION OF POLY-CRYSTALLINE PHOTOVOLTAIC MODULE

This work proposes modeling and optimization of poly-crystalline photovoltaic (PV) modules, validated with experiment, using single diode (SDM) and double diode model(DDM). The PV cell is treated as an equivalent electrical circuit with series and shunt resistance. The weather data like temperature and irradiance are used as input variables. The operating current and maximum power point are obtained using three variables: current, voltage, and power. An experiment was set up in Nagpur city, on the Deccan plateau situated in central India. This place is suited for PV installation due to the high average solar insolation period throughout the year. The outputs of the PV model are optimized using a genetic algorithm (GA) and simulated annealing (SA) algorithm and validated against the experiments with variable load conditions. The metaheuristics optimization techniques worked well for this model and improved the accuracy and precision of the current-voltage (I-V) and power voltage (P-V) curves. The present work compares the errors of the SA and GA algorithms used for extracting the five key points of (I -V) curves using the normalized sum of squared errors (NSSE). The proposed model optimization results are in close agreement with experimental results.

Sliding Mode Controller Based on Genetic Algorithm and Simulated Annealing for Assured Crew Reentry Vehicle

A sliding mode (SM) re-entry control system is proposed for an assured crew re-entry vehicle (ACRV). The vehicle has a low lift/drag ratio (0.3). The trajectory controller drives the ACRV in a predefined track. The reference vectors for the attitude controller are generated by the trajectory controller and navigation system. The feedback SM controller is designed to accommodate the structural parametric uncertainties and environmental disturbances. The SM controller is optimized by both the genetic algorithm (GA) and the simulated annealing (SA) methods. A comparative study is done between both optimization methods and the efficiency of the SA method is proved. The system is subjected to a sensitivity study in terms of various thrust torque profiles. A robustness check is done by changing the system parameters. A pulse width pulse frequency (PWPF) modulator modulates the output torque dictated by the attitude controller. The simulation results demonstrate the proposed method’s effectiveness in achieving the various required parameters.

Analysis of the application of path finding system based on efficiency improvement in smart tourism

With the proposal and development of intelligent tourism destination, the problem of tourism route planning has become a hot topic in tourism research. This study proposes an intelligent tourism route planning model based on efficiency improvement for Guangxi tourism route planning. In order to improve the efficiency of the model, this paper combines ant colony algorithm with genetic algorithm and introduces smoothing factor and guiding factor, and obtains genetic algorithm (genetic algorithm-path smoothing ant colony optimization) with changing probability. Experimental results show that the convergence speed of the algorithm is fast, and it can converge in 78 iterations, and the operation efficiency is higher. The result error of the proposed algorithm is 1.39, which is smaller than that of other comparative algorithms. In the actual application effect evaluation, the average satisfaction of the planned route of the intelligent tourism route planning model based on efficiency improvement is 15.38, which is higher than that of the traditional genetic algorithm and simulated annealing algorithm. It shows that the model can not only efficiently plan the best travel route, but also comprehensively consider the whole trip of tourists, so that tourists can have a better travel experience. The research provides an intelligent route planning method that is more efficient and more in line with tourists' needs for tourism development.

Identification and Calibration Method of Deviation of Main Transformer Online Monitoring Date Groups

Dissolved gas in transformer oil (DGA) online monitoring data can reflect equipment insulation performance, which provides an important basis for transformer condition assessment. The accuracy of online monitoring data directly affects the correctness of transformer condition assessment, and the health condition of the online monitoring data device also affects the effectiveness and reasonableness of online monitoring data. With the accumulation of operation time, the DGA online monitoring device faces many problems, resulting in the overall deviation of this indicator data from the actual value, and the transformer online monitoring device cannot play its monitoring effect. To solve these problems, this paper firstly linearizes the data of the DGA online monitoring device by segmentation, extracts the characteristics of the line segment curve, constructs a segmented correlation mining model, and mines the correlation of different indicators of DGA. Then, according to the change of correlation strength and weakness between online monitoring data in time sequence, the data deviation is tracked. Finally, a multi‐indicator back propagation (BP) neural network algorithm optimized by the genetic simulated annealing algorithm is constructed to calibrate the deviated data. The analysis shows that the method can identify the population deviation of online monitoring data and calibrate the deviated data effectively. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Axial-piston pump optimal multivariable PID controller tunning

The aim of the present work is the synthesis and optimal tunning of a PID controller designed to control the displacement volume of an axial-piston pump intended for hydraulic systems with open circulation. The pump is controlled in real-time through a system developed by the authors based on an electro-hydraulic proportional valve (replacing the conventional hydro-mechanical controller) and an industrial controller. The experimental system was initially used to obtain a data set that is used in a system identification procedure to obtain a model. The obtained state space model was used for the synthesis of the PID controller. The optimal tunning of controller parameters is performed using three modern methods: genetic algorithm, simulated annealing and pattern search. The resulting three PID controllers with different settings depending on the used methods are compared in simulation and experimental conditions. Based on the results, an analysis of the control performance was carried out.

Short-term PV Power Prediction Based on SAGA-FCM and XGBoost

The stable and economic operation of the power grid cannot be achieved without accurate prediction of PV plant power. To address the problem of unstable clustering quality of the fuzzy c-mean clustering algorithm, this paper proposes to combine the genetic algorithm and simulated annealing algorithm to optimize the FCM clustering algorithm, and then combine it with XGBoost to construct a short-term PV power generation prediction model. Firstly, the robustness of the clustering algorithm is improved. Secondly, the differences in power generation characteristics under different weather types are considered, and the prediction models under three typical weather types are separately trained. Finally, to verify the improvement of the model on prediction accuracy, different clustering methods are used to compare the prediction results, and the results show that the algorithm proposed in this paper has higher accuracy compared with the other three methods.

Construction, Spectral Modeling, Parameter Inversion-Based Calibration, and Application of an Echelle Spectrometer.

We have developed a compact, asymmetric three-channel echelle spectrometer with remarkable high-spectral resolution capabilities. In order to achieve the desired spectral resolution, we initially establish a theoretical spectral model based on the two-dimensional coordinates of spot positions corresponding to each wavelength. Next, we present an innovative and refined method for precisely calibrating echelle spectrometers through parameter inversion. Our analysis delves into the complexities of the nonlinear two-dimensional echelle spectrogram. We employ a variety of optimization techniques, such as grid exploration, simulated annealing, genetic algorithms, and genetic simulated annealing (GSA) algorithms, to accurately invert spectrogram parameters. Our proposed GSA algorithm synergistically integrates the strengths of global and local searches, thereby enhancing calibration accuracy. Compared to the conventional grid exploration method, GSA reduces the error function by 22.8%, convergence time by 2.16 times, and calibration accuracy by 7.05 times. Experimental validation involves calibrating a low-pressure mercury lamp, resulting in an average spectral accuracy error of 0.0257 nm after performing crucial parameter inversion. Furthermore, the echelle spectrometer undergoes a laser-induced breakdown spectroscopy experiment, demonstrating exceptional spectral resolution and sub-10 ns time-resolved capability. Overall, our research offers a comprehensive and efficient solution for constructing, modeling, calibrating, and applying echelle spectrometers, significantly enhancing calibration accuracy and efficiency. This work contributes to the advancement of spectrometry and opens up new possibilities for high-resolution spectral analysis across various research and industry domains.

Exploring the design of ecological and energy-saving residential buildings adapted to climate change based on a simulated annealing algorithm

Abstract 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.

Data-driven approach to fuzzy inference system tuning for static security assessment of multi-area power systems with renewable energy

Energy market deregulation and increased integration of intermittent renewable energy resources (RE) have pushed modern power systems to operate close to their security limits, which if violated could be catastrophic. To prevent power system failure and damage, a fast and accurate security assessment tool is required. In this paper, a data-driven fuzzy inference system tuned by hybrid genetic-simulated annealing (HGSA-FIS) is proposed for quick static security assessment (SSA) of a power system under a single component failure. An existing composite security index is modified to make a reliable SSA by distinguishing between severe and non-severe operating scenarios and contingencies. The prediction performance of HGSA-FIS was evaluated on an RTS-GMLC test system with operating scenarios varying with RE generation, load demand, and tie-line flows. The findings demonstrate the robust performance of HGSA-FIS in making static security assessments under varying operating scenarios, exceeding ANFIS in most and ANN in a few scenarios.

Performance improvement of the solid oxide fuel cell using optimal parameters identification through fuzzy logic based-modeling and different optimization algorithms

Fuel Cells are novel devices that have been proposed as new power generation systems. The advantages of solid oxide fuel cells are higher efficiency, higher stability, fuel flexibility, lower emissions, and generally lower cost. In the present study, the fuzzy model is employed to build the model of the solid oxide fuel cell considering various sputtering power, thickness of electrolyte, and temperatures of cell. The maximum iterations for the adaptive neuro-fuzzy inference model was considered 50 iterations. About 3500 samples were applied for the training process, and almost 900 samples were utilized for the testing. After modeling process, the genetic algorithm, particle swarm, simulated annealing, and hybrid firefly-particle swarm optimizers are applied to achieve the optimum value of current and power densities. The results showed that proposed fuzzy model could approximate the model the system with a good agreement with experimental data. Additionally, the obtained data confirm the accuracy, high convergence speed, and robustness of the proposed hybrid optimizer compared to three efficient optimization algorithms. Accordingly, the correlation factor for the proposed fuzzy model for the training and testing dataset was obtained to be 0.9298 and 0.9289, correspondingly.

Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm

Logistics distribution is a collection of interrelated organizations and facilities. There is a waste of cost and time in many links. Therefore, it is particularly important to use information technology to improve distribution efficiency. Under the constraints of delivery vehicle cost and time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines the global search ability of the genetic algorithm (GA) and the simulated annealing algorithm (SA) with strong local search ability to solve the vehicle routing problem with time windows (VRPTW). The perturbation factor is introduced to improve the local search, and the crossover method is optimized to obtain more efficient genetic operators by using population information. In this paper, combined with the actual application case, the simulation experiment is carried out in MATLAB. The experimental results show that, compared with the traditional genetic algorithm and simulated annealing algorithm, the total cost of the improved genetic simulated annealing algorithm is reduced by about 15%, which provides a more suitable vehicle route planning scheme.

Genetic-simulated annealing optimization for surface wave inversion of shear-wave velocity profiles of geotechnical sites

A new hybrid genetic-simulated-annealing (GSA) optimization algorithm is introduced to solve the multivariable minimization problem for surface wave inversion. The algorithm is effective for both global and local searches due to its combination of the reproduction and selective generation schemes from genetic algorithms (GA) with the nonlinear scaling fitness function and perturbation scheme from simulated annealing (SA). The hybrid GSA algorithm can reduce the risk of a solution becoming trapped in a local minimum and improve global searching efficiency. A mathematical test function as well as surface wave examples are used to examine the advantages and performance of the GSA algorithm. Comparisons of GA, SA, and GSA inversion results demonstrates that GSA can yield the smallest uncertainty and greatest efficiency, and improve the statistical confidence of using surface wave testing for shear-wave velocity profiling.

A sustainable and resilient supply chain (RS-SCM) by using synchronisation and load-sharing approach: application in the oil and gas refinery

Nowadays sustainable and resilient supply chain management (R&S-SCM) is an interesting and in the meantime vital problem that has grabbed the attention of many researchers. This study is an effort to investigate the simultaneous design of sustainable and resilient flow in the supply chain. To achieve a sustainable and resilient supply chain, there is a wide variety of strategies depending on their impacts on different industries and the features of their supply chain. The fuzzy QFD method is designed to select the most appropriate strategy in the study. Two approaches are used for this purpose. Early planning in sustainable mode is formulated assuming that all members of the network are healthy and final planning in the reactive approach to destruction which formulates the return to the original state in the shortest possible time, cost and scenario-based status. A hybrid priority-based Genetic Algorithm (pb-GA) and simulated annealing algorithm (SA) is developed in two phases to find the optimal solutions. Response Surface Methodology (RSM) method is used to adjust significant parameters for the algorithm. Several test problems are generated showing that the proposed metaheuristic algorithm can find good solutions in reasonable time spans.

Bayesian optimization of ESG (Environmental Social Governance) financial investments

Financial experts seek to predict the variability of financial markets to ensure investors’ successful investments. However, there has been a big trend in finance in the last few years, which are the ESG (Economic, Social and Governance) criteria, due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should consider not only its financial performance but compliance with ESG criteria. Portfolio optimization (PO) techniques previously applied to ESG portfolios, are all closed-form analytical ones. But the real world is rather a black box with unknown analytical expressions. Thus, in this paper we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to-compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated into the objective function. And we compare it to two other black-box techniques widely applied for the optimization of ‘conventional portfolios’ (non-ESG ones): the metaheuristics Genetic algorithm (GA) and Simulated Annealing (SA). Although BO has many theoretical advantages over GA and SA, it has never been applied to PO. Thus, this paper investigates whether BO can be used in the ESG PO framework as an alternative and compares it with GA and SA. This is the research gap to which this paper responds. To show the empirical performance of BO, we carry out four illustrative experiments and find evidence of BO outperforming the baselines. Thus we add another different optimization approach to the world of ESG investing: a black-box non-heuristic optimization approach through BO. Our study is the first paper that leverages BO and ESG scores into a PO technique. This paper opens the door to many new research lines in (ESG) portfolio optimization.

Evaluation of combinatorial algorithms for optimizing highly nonlinear structural problems

Optimizing highly nonlinear structural problems can be very challenging due to the large number of parameters. Classical compliance minimization does not work for such problems. Common optimization algorithms also do not find good solutions. This work evaluates both commonly used optimization algorithms and algorithms not yet used in topology optimization. The algorithms are evaluated using a simple nonlinear problem: minimizing the end displacement of a cantilever beam fixed on one side and loaded by gravity. The global optimum for a coarse mesh grid is computed by simulating nearly 60 million possible topology designs using a Brute-Force search. We use this benchmark to evaluate the computational cost and objective values of known and newly developed optimization methods. The known methods are binary-coded Genetic Algorithm, Simulated Annealing, and Free Shape Optimization. The Reduced Variable Neighborhood Search (RVNS) has not yet been applied to topology optimization. We provide two implementations of RVNS: Breadth-First Search with a limited search depth (BFSL) and with an optional restriction for the size of the simultaneously modified area (TBFSL). According to the benchmark, TBFSL is the most efficient approach. For the optimization on a finer mesh grid, TBFSL is combined with a multi-grid approach to further increase efficiency.