Genetic Simulated Annealing Algorithm Research Articles

Rapid determination of the main components of corn based on near-infrared spectroscopy and a BiPLS-PCA-ELM model.

To evaluate corn quality quickly, the feasibility of near-infrared spectroscopy (NIRS) coupled with chemometrics was analyzed to detect the moisture, oil, protein, and starch content in corn. A backward interval partial least squares (BiPLS)-principal component analysis (PCA)-extreme learning machine (ELM) quantitative analysis model was constructed based on BiPLS in conjunction with PCA and the ELM. The selection of characteristic spectral intervals was accomplished by BiPLS. The best principal components were determined by the prediction residual error sum of squares of Monte Carlo cross validation. In addition, a genetic simulated annealing algorithm was utilized to optimize the parameters of the ELM regression model. The established regression models for moisture, oil, protein, and starch can meet the demand for corn component detection with the prediction determination coefficients of 0.996, 0.990, 0.974, and 0.976; the prediction root means square errors of 0.018, 0.016, 0.067, and 0.109; and the residual prediction deviations of 15.704, 9.741, 6.330, and 6.236, respectively. The results show that the NIRS rapid detection model has higher robustness and accuracy based on the selection of characteristic spectral intervals in conjunction with spectral data dimensionality reduction and nonlinear modeling and can be used as an alternative strategy to detect multiple components in corn rapidly.

A genetic-simulated annealing algorithm for stochastic seru scheduling problem with deterioration and learning effect

ABSTRACT As a flexible and effective production mode, seru production has been adopted successfully in electronic industry. In practice, the processing time may be affected by many stochastic factors, such as worker absence, shortage of resources, and so on. This paper focuses on seru scheduling problems with stochastic processing time, and considers the influence of dynamic resource allocation, job deterioration, learning effecteffect, and setup time simultaneously to minimize the makespan. A genetic-simulated annealing algorithm is proposed, in which a simulated annealing procedure is constructed to re-optimize the optimal individual obtained by geneticthe genetic algorithm. Experiment results validate the effectiveness of proposedthe proposed seru scheduling model and genetic-simulated annealing algorithm for solving large-scale cases, and indicate that the stochastic processing time has a great influence on the makespan whichmakespan that can help production manager to makeproduce more consistent results according to the actual situation.

Comparison of SAGA and GA in Wind Turbine Layout Optimization Based on Modified Two-dimensional Wake Model or Jensen Model

In this paper, Simulated Annealing Genetic Algorithm (SAGA) and a new two-dimensional wake model called 2D_k Jensen model are adopted for optimal wind turbine layout (WTL) in the wind farms and are compared with Genetic Algorithm (GA) and Jensen model, respectively, aiming to minimize the investment cost and maximize the wind power generation as much as possible. The influence of the radial distribution of wake on the equivalent wind speed in the wake superposition region is considered. In the case of single wind direction and single speed, total output power and energy extraction efficiency are both improved when SAGA is applied to the two model conditions respectively, especially for the WTL using the 2D_k Jensen model, these two aspects are significantly improved by 13.75% and 24.10%, respectively, and the objective function is reduced by 19.05%. The results demonstrate that SAGA is more conducive to solving the practical configuration optimization of wind turbines, compared with the original GA.

Optimization Design of Pressure Hull for Long-Range Underwater Glider Based on Energy Consumption Constraints

Underwater gliders are a class of ocean observation equipment driven by buoyancy, and their energy consumption source is mainly generated by the active regulation of buoyancy. The periodic elastic deformation of the pressure hull during the upward and downward movement of the underwater glider can have a large impact on its driving buoyancy. This paper relates the optimization problem of the pressure hull with the energy consumption of underwater glider, and the energy improvement factor is taken as the optimization objective. Based on the mechanical theory, the theoretical optimization model and constraint model are derived. A hybrid genetic simulated annealing algorithm (HGSAA) is adopted to optimize the pressure hull of the underwater glider developed by Huazhong University of Science and Technology (HUST). Additionally, the effectiveness of the optimized mathematical model and optimization results were verified by the tests. The sea trial results show that after the pressure hull optimization, the energy consumption of the buoyancy regulation unit decreased by 21.9%, and the total energy carried increased by 12.4%.

Multi-User Dynamic Computation Offloading and Resource Allocation in 5G MEC Heterogeneous Networks with Static and Dynamic Subchannels

With the rapid development of Mobile Edge Computing (MEC) technology, the computationally intensive requests of end devices can be offloaded to MEC servers directly, which equipped at the edge of wireless networks. Through offloading, the performances such as the execution delay as well as the energy consumption can be effectively improved, which can significantly enhance the quality of user experience. Given the dynamics and randomness of computation requests arrival, the energy in the battery, the radio network environment, and the computation resource in the MEC server, it is a challenge to perform efficient offloading. Based on these problems, this paper proposes dynamic optimization schemes with queuing theory for the cases of the static subchannel and dynamic subchannel during a time slot separately in 5G MEC heterogeneous networks with multiple MDs equipped with the function of energy harvesting. In the schemes, offloading decisions and radio allocation strategies will be dynamically coordinated. They are also jointly allocated along with changing wireless communication resources and computation demands aiming to minimize the system average execution delay. Specifically, it is assumed that the offloading requests can be transmitted through either macro base stations or small base stations. In the case of the static subchannel, a joint resource allocation and computation offloading scheme based on Lyapunov optimization and Simulated Annealing Genetic Algorithm (SAGA) is put forward. As for the dynamic subchannel, the master-and-slave model is adopted and solved by SAGA and Sequential Quadratic Programming (SQP) method. At last, the effectiveness of the proposed schemes is verified through several simulations.

Reverse logistics planning research of building waste from perspective of closed supply chain in urban sustainable environment governance.

The resource utilization of building waste in urban sustainable environment governance is significant, in view of the status of construction waste recycling; this article built a reverse logistics planning model in urban building waste from the perspective of a closed supply chain by means of a hybrid nonlinear programming model. Combining the simulated annealing algorithm with memory function and the genetic algorithm with global convergence performance, a new genetic simulated annealing algorithm is presented to optimize the model. The empirical study was then made for the system to verify the feasibility of the model. The proposed new planning method and its detailed steps can provide a meaningful reference value for resource utilization of building waste in urban sustainable environment governance.

A Transcoding Task Offloading and Routing Decision-Making Scheme in Live Transmission Architecture Based on Computing Power Network

In recent years, there has been a significant increase in the demand for high-bit-rate live broadcast services, which has led to the widespread use of edge transcoding technology. Edge transcoding can effectively reduce the throughput of streaming media transmission, making it a popular and extensively researched technology. However, due to the real-time requirements of live broadcasting, the edge server needs to have the sufficient computing power to ensure low-latency calculations, which makes computing power allocation and traffic distribution become quite difficult. Inspired by the real-time and flexible computing power scheduling ability of the Computing Power Network, this paper explores reasonable edge task offloading and efficient traffic routing path planning to ensure overall low latency. This paper proposes a live stream transmission architecture based on the computing power network to solve the problems mentioned above to some degree. The paper first models the computing power network in the scene and then designs a task offloading algorithm based on Deep Reinforcement Learning (DQN) to determine the device for executing the computing task. Furthermore, a hybrid Simulated Annealing Genetic Algorithm (SAGA) is proposed for routing decisions. The effectiveness and superiority of the scheme are validated through simulation experiments.

Proactive dynamic vehicle routing problems considering cooperation services for the store-depot-integrated retailer.

Distribution costs remain consistently high in crowded city road networks, posing challenges for traditional distribution methods in efficiently handling dynamic online customer orders. To address this issue, this paper introduces the Proactive Dynamic Vehicle Routing Problem considering Cooperation Service (PDVRPCS) model. Based on proactive prediction and order-matching strategies, the model aims to develop a cost-effective and responsive distribution system. A novel solution framework is proposed, incorporating a proactive prediction method, a matching algorithm and a hybrid Genetic Algorithm-Simulated Annealing (GA-SA) algorithm. To validate the effectiveness of the proposed model and algorithm, a case study is conducted. The experimental results demonstrate that the dynamic scheme can significantly reduce the number of vehicles required for distribution, leading to cost reduction and increased efficiency.

A Multi-objective Mathematical Programing Model for the Problem of P-envy Emergency Medical Service Location.

To prevent the great dangers caused by emergency situations, providing rapid and high-quality emergency aid highly depends on the location of emergency medical centers. The purpose of this research is to present a multi-objective mathematical programing model based on the minimum P-envy algorithm to locate and construct emergency medical services (EMS). Maximizing the coverage in order to increase the probability of survival of different categories of patients, minimizing the costs of constructing EMS and optimizing the ratio of regions having the right to emergency medical services is one of the fundamental challenges in the health care system of countries. In this paper, a model for maximum utilization of EMS considering budget limitations is presented. In this study, since the problem is NP-Hard, the Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm were used to solve this problem. The parameters of the metaheuristic algorithms were tuned using the Taguchi method. Several instance problems were solved to compare the performance of 2 algorithms. The results demonstrate that the validity of the proposed model. Also, the mean of the solutions obtained by GA for small, medium, and large-size problems are better than the SA algorithm. Also, the GA algorithm obtained more efficient solutions than the SA algorithm.

Joint Task Offloading and Resource Allocation in Vehicular Edge Computing Networks for Emergency Logistics

As a special form of multiaccess edge computing (MEC), vehicular edge computing (VEC) plays an important role in emergency logistics by providing real‐time and low‐latency services for vehicles. The solution of the joint task offloading and resource allocation problem (JTORA) is the key to improving VEC efficiency. This study formulates a special model according to the multistage characteristics of the computational task in vehicular edge computing networks (VECNs) for emergency logistics. First, the JTORA problem is decomposed into three computational steps, each of which includes a task offload (TO) problem and a resource allocation (RA) problem. Then, a hybrid solution is proposed which uses a simulated annealing process to optimize the genetic algorithm (GA) and cooperate with the particle swarm optimization (PSO) algorithm, called the genetic simulated annealing and particle swarm optimization (GSA‐PSO) algorithm. Furthermore, a simulation experiment is designed and the effectiveness of the GSA‐PSO is verified.

Uncertain Multi-Objective Hazardous Materials Transport Route Planning Considering Resilience and Low–Carbon

Hazardous material transport accidents are events with low probability and high consequence risk. With the increase in the proportion of hazardous materials transported on domestic roads, increasing number of scholars have begun to study this field. In this study, a multi-objective hazardous materials transport route planning model considering road traffic resilience and low carbon, which considers the uncertainty of demand and time and is under the limit of the time window. It transports many types of hazardous materials from multiple suppliers to multiple retails with the three goals (transportation cost, risk and carbon emission). This model fills the gap in the research on hazardous materials transportation in the field of low carbon, and this is the first time that road traffic resilience is considered in the transport of hazardous materials as one of the weight factors of risk calculation. We designed a improved ant colony optimization algorithm (ACO) to obtain the pareto optimal solution set. We compare the improved ACO with genetic algorithm and simulated annealing algorithm. The results show that the improved ACO has better solution quality and solution space, which verifies the validity and reliability of the improved ACO.

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

An Improved Multi-Objective Hybrid Genetic-Simulated Annealing Algorithm for AGV Scheduling under Composite Operation Mode

Ability-Restricted Indoor Reconnaissance Task Planning for Multiple UAVs

For indoor multi-task planning problems of small unmanned aerial vehicles (UAVs) with different abilities, task assignment and path planning play a crucial role. The multi-dimensional requirements of reconnaissance tasks bring great difficulties to the task execution of multi-UAV cooperation. Meanwhile, the complex internal environment of buildings has a great impact on the path planning of UAVs. In this paper, the ability-restricted indoor reconnaissance task-planning (ARIRTP) problem is solved by a bi-level problem-solving framework. In the upper level, an iterative search algorithm is used to solve the task assignment problem. According to the characteristics of the problem, a solution-space compression mechanism (SSCM) is proposed to exclude solutions that do not satisfy the task requirements. In the lower level, based on a topological map, the nearest neighbor (NN) algorithm is used to quickly construct the path sequence of a UAV. Finally, the genetic algorithm (GA) and simulated annealing (SA) algorithm are applied to the upper level of the framework as iterative search algorithms, which produces two hybrid algorithms named the GA-NN and SA-NN, respectively. ARIRTP instances of different scales are designed to verify the effectiveness of the SSCM and the performance of the GA-NN and SA-NN methods. It is demonstrated that the SSCM can significantly compress the solution space and effectively improve the performance of the algorithms. The proposed bi-level problem-solving framework provides a methodology for the cooperation of multi-UAV to perform reconnaissance tasks in indoor environments. The experimental results show that the GA-NN and SA-NN methods can quickly and efficiently solve the ARIRTP problem. The performance of the GA-NN method is similar to that of the SA-NN method. The GA-NN method runs slightly faster. In large-scale instances, the performance of the SA-NN method is slightly better than that of the GA-NN method.

In-vehicle heat-induced impact on the system usability of on-board computer interfaces considering the optimization of scheme presentation order

Extreme heat is now a new normal for many cities due to climate change and urbanisation. The heat challenges have generated significant impacts on people's daily functions, including driving behaviours. In-vehicle temperature condition regulate drivers' thermal comfort, responses, and thereby intervening their cognition of on-board computer interface schemes (OBCIS). However, existing studies on the OBCIS usability evaluation have not well dealt with the impacts of ambient temperature and scheme presentation order on drivers' responses. This study addressed such research gaps by empirically analysing respondents' eye movement, including fixation duration (FD) and fixation times (FT) towards 24 typical interface prototypes under six temperature conditions. Moreover, the genetic algorithm (GA) and simulated annealing algorithm (SAA) were applied to optimise the total distance between the prototypes for optimal OBCIS presentation order. The results indicated that respondents' FD and FT generally increased with temperature. The checkerboard type OBCIS, which got the highest usability score, presented the lowest FT at 23 °C, while it exhibited the highest FT at 26 °C. Moreover, after the two optimisation algorithms had been run for 50 times respectively, paired sample t-test showed that GA performed better than SAA in the optimisation work, and thereby the optimal presentation order was determined. This study provides a reference for understanding heat-induced information interaction and OBCIS design. Compared with traditional usability evaluation methods of human-computer interface, this research properly ensures the reliability of the evaluation results in terms of ambient temperature and order effect bias.

Glass classification and recognition based on genetic simulated annealing algorithm and decision tree algorithm

The chemical composition and ratio of ancient glasses are highly susceptible to change due to the complex burial environment. In order to determine the classification rules of lead-barium glass and potassium glass, and to subclassify lead-barium glass and potassium glass according to the appropriate chemical composition index, this paper mines the linear combination of high potassium glass and lead-barium glass by linear discriminant analysis, and calculates the clustering centers of lead-barium glass and high potassium glass subclassifications and the subclassifications of glass to be classified based on the FCM (Fuzzy-C Mean Clustering) algorithm of genetic simulated annealing algorithm. The sub-clusters of lead-barium glass and high-potassium glass, as well as the affiliation degrees of the glass samples to be classified were calculated based on the genetic simulated annealing algorithm. The model results were then examined by using the decision tree algorithm, and the results of glass artifact type identification were obtained.

An extended version of adaptive large neighborhood search for a relief commodities distribution network design under uncertainty

Natural and technology-induced disasters have posed significant threats to human life all around the world and caused many damages and losses so far. The current study addresses a location-routing problem to make an efficient and timely distribution plan in response to a possible earthquake. This problem considers uncertainty in such parameters as demand, access to routes, travel time, and the number of available vehicles. To deal with these uncertainties, stochastic programming (SP) is performed while the objective function is to minimize the time of carrying relief commodities (RCs) to affected areas. The problem is coded in the CPLEX solver to obtain optimal solutions to small-scale problems, and an adaptive large neighborhood search (ALNS) is proposed to solve mixed-integer linear formulas for large-scale problems. To validate the formulation and evaluate the performance of the proposed ALNS, several types of tests are devised. To shows the efficiency of the proposed ALNS, two other metaheuristic algorithms, the Genetic algorithm (GA) and simulated annealing algorithm (SA), are used as well. The results of the calculations suggest the satisfactory performance of the suggested algorithm and the effectiveness of the model for the desirable delivery of humanitarian aids to affected areas.

Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm

The disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithm’s capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithm’s computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm.

Differential Evolution Algorithm-Aided Time-Varying Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Orthogonal frequency division multiplexing (OFDM) is the preferred scheme for high-speed communication in the field of underwater acoustic communication. However, it is very sensitive to the carrier frequency offset (CFO). This study used a time-varying CFO estimation method aided by the differential evolution (DE) algorithm to accurately estimate the CFO of an OFDM system. This method was based on the principle that the received OFDM signal with inter-carrier interference could be considered by a Multi Carrier-code division multiple access (MC-CDMA) system on the receiver side because MC-CDMA is a technology that combines OFDM and code division multiple access (CMDA). Because it is suitable for solving problems where there are dependencies between adjacent variables, the DE algorithm was used to capture the varying CFO values on the adjacent blocks. The spreading code of the MC-CDMA was obtained based on the estimated CFO values, which were elements in the DE solutions. Then the received signal was reconstructed. The Root-Mean-Square Error between the reconstructed and actual received signals was used as the cost function, and the CFO was estimated using the DE algorithm because of its powerful parallel search capability. The simulation results showed that the proposed method had a high estimation accuracy. Compared with other intelligent optimization algorithms such as the genetic algorithm and simulated annealing mutated-genetic algorithm, the time-varying CFO estimation performance of the DE algorithm was better because of its unique ability to solve problems with dependencies between adjacent variables. Specifically, under the condition of a high signal-to-noise ratio, the improvement of estimation accuracy reaches 36.13%, and the Bit Error Rate of demodulation is thus reduced by 75%, compared with the reference algorithms. In addition, the proposed method also has good applicability to modulation methods. For phase-shift keying and quadrature amplitude modulation, in particular, the proposed method not only achieved high-precision time-varying CFO estimation values, but also reduced the demodulation deterioration caused by noise.

Robotic disassembly line balancing and sequencing problem considering energy-saving and high-profit for waste household appliances

Waste household appliances contain hazardous substances and valuable components. The timely disposal of these waste products can avoid environmental pollution and improve the recycling rate of resources. Robotic disassembly can realize efficient and precise disassembly to achieve sustainable and clean production. Reasonable allocation of robots and disassembly tasks is the key to achieving efficient production. Reducing energy consumption and improving profits are the focus of the government and enterprises. The tool replacement of the robot needs to be considered in the actual disassembly process, which increases the difficulty of the problem. This study establishes a multi-objective robotic disassembly line balancing and sequencing model to quantify the problem. The model aims to shorten the disassembly completion time and perform disassembly operations with energy-saving and high-profit. An improved genetic simulated annealing algorithm based on problem characteristics is designed. The superiority of the proposed algorithm is verified by solving 21 benchmarks with the number of tasks ranging from 7 to 148 and comparing with five algorithms. In a disassembly case of waste household appliances represented by a microwave oven, 66 disassembly schemes are obtained for decision makers to choose from different emphases. The results show that these schemes can not only achieve efficient and green disassembling but also obtain higher disassembly profits.

Micro-dimensional oscillation-based optimization for a dielectric metalens in the mid-infrared.

In the past few decades, there has been significant progress made in metasurfaces and integrated and miniaturized optical devices. As one of the most prominent applications of metasurfaces, the metalens is the subject of significant research. In this paper, for achieving better focusing performance of the initial metalens designed by the Pancharatnam-Berry (PB) phase, a concept of micro-dimensional oscillation is proposed to optimize the geometric parameters of nanopillars. A strategy of grouping iteration is proposed to reduce the loss rate and computational effort in a holistic way. Its essence is to divide an extremely large-scale optimization space into many overlapping groups. Meanwhile, an improved genetic-simulated annealing (IGSA) algorithm is presented for the optimal solution of each group. By introducing the adaptive crossover and mutation probabilities in traditional genetic algorithms, the IGSA algorithm has both strong global searching capability and excellent local searching capability. After optimization, the maximum field intensity of the central hot spot can be increased by about 8% compared to the initial metalens. Moreover, the field intensity of the side lobes around the hot spot is almost constant, and the central hot spot increases, which provides a potential for the realization of high imaging contrast.