Hybrid Genetic Simulated Annealing Algorithm Research Articles

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain.

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

Multi-Satellite Scheduling for Moving Targets Using Enhanced Hybrid Genetic Simulated Annealing Algorithm and Observation Strip Selection

Multi-Satellite Scheduling for Moving Targets Using Enhanced Hybrid Genetic Simulated Annealing Algorithm and Observation Strip Selection

A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

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.

Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm.

It is necessary to ensure the ship's stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.

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%.

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

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.

Fault Localization Based on Hybrid Genetic Simulated Annealing Algorithm

Software testing is an important stage in the software development process, which is the key to ensure software quality and improve software reliability. Software fault localization is the most important part of software testing. In this paper, the fault localization problem is modeled as a combinatorial optimization problem, using the function call path as a starting point. A heuristic search algorithm based on hybrid genetic simulated annealing algorithm is used to locate software defects. Experimental results show that the fault localization method, which combines genetic algorithm, simulated annealing algorithm and function correlation analysis method, has a good effect on single fault localization and multi-fault localization. It greatly reduces the requirement of test case coverage and the burden of the testers, and improves the effect of fault localization.

Research on the Optimization of Staff Allocation under the Strategy of “Strong Leads the Weak”

Based on the operator’s competency index matrix, a two-layer mathematical model with the goal of maximizing the total competency of employees’ workstations is designed. The upper layer uses the Hungarian Algorithm to achieve the configuration of “strong employees”, and the lower layer uses the Hybrid Genetic Simulated Annealing Algorithm to achieve the configuration of “weak employees”. The results show that: compared with the traditional undifferentiated overall configuration. The double-level configuration based on the double-level mathematical model increases the employee competency difference ratio by 5.71%, which effectively embodies the idea of “strong leads the weak”.

Hybrid Genetic Simulated Annealing Algorithm for Job Shop Scheduling Problem

The job shop scheduling problem (JSSP) is a well-known difficult combinatorial optimization problem, as it is classified as NP-hard problem and therefore no deterministic algorithms can solve them in a reasonable amount of time. The main objective of solving this problem is to find suitable job sequences on machines to optimize the performance criteria. In this paper, a meta-heuristic approach for solving the job-shop scheduling problem (JSSP) is presented. This approach uses a hybrid genetic algorithm that is suggested by previous stuody, to generate the best solutions and then a simulated annealing algorithm to improve the quality and performance of the best solutions to produce the optimal/near-optimal solution. Ten benchmark problems adopted from the previous study are used to evaluate the performance of the proposed algorithm. The computational results validate the quality of the proposed algorithm; this is done by calculating the completion time Cmax

Optimization Dubins Path of Multiple UAVs for Post-Earthquake Rapid-Assessment

In the last decade, with the wide application of UAVs in post-earthquake relief operations, the images and videos of affected areas obtained by UAVs immediately after a seismic event have become an important source of information for post-earthquake rapid assessment, which is crucial for initiating effective emergency response operations. In this study, we first consider the kinematic constraints of UAV and the Dubins curve is introduced to fit the shortest flyable path for each UAV that meets the maximum curvature constraint. Second, based on the actual requirements of post-earthquake rapid assessment, heterogeneous UAVs, multi-depot launching, and targets allowed access to multiple times, the paper proposes a multi-UAV rapid-assessment routing problem (MURARP). The MURARP is modeled as the multi-depot revisit-allowed Dubins TOP with variable profit (MD-RDTOP-VP) which is a variant of the team orienteering problem (TOP). Third, a hybrid genetic simulated annealing (HGSA) algorithm is developed to solve the problem. The result of numerical experiments shows that the HGSA algorithm can quickly plan flyable paths for heterogeneous UAVs to maximize the expected profit. Finally, a case study based on real data of the 2017 Jiuzhaigou earthquake in China shows how the method can be applied in a post-earthquake scenario.

Free Parameter Optimization of DTMDs Based on Improved Hybrid Genetic-Simulated Annealing Algorithm

The optimum design of distributed tuned mass dampers (DTMDs) is normally based on predefined restrictions, such as the location and/or mass ratio of the tuned mass dampers (TMDs). To further improve the control performance, a free parameter optimization method (FPOM) is proposed. This method only restricts the total mass of the DTMDs system and takes the installation position, mass ratio, stiffness and damping of each TMD as parameters to be optimized. An improved hybrid genetic-simulated annealing algorithm (IHGSA) is adopted to find the optimum values of the design parameters. This algorithm can solve the non-convexity and multimodality problems of the objective function and is quite effective in dealing with the large amount of computations in the free parameter optimization. A numerical benchmark model is adopted to compare the control efficiency of FPOM with conventional control scenarios, such as single TMD, multiple TMDs and DTMDs optimized through conventional methods. The results show that the DTMDs system optimized by using FPOM is superior to the other control scenarios for the same value of mass ratio.

Bi-Level Optimization Model of Boundary Signal Control for the Network Based on Macroscopic Fundamental Diagrams

This paper proposes a bi-level optimization model about boundary signal control for the network using macroscopic fundamental diagrams. Firstly, the road network is simplified as a directed graph, in which vertices and arcs respectively correspond to traffic zones and road segments connecting zones. Then a bi-level optimization model is proposed and the control objective is maximizing total output vehicles as well as keeping the existing number of vehicles optimal for each zone. The upper layer model optimizes transfer traffic flow among zones and the lower layer model optimizes the signal control scheme about boundary intersections. And a hybrid genetic simulated annealing algorithm is devised to solve the model. Finally, micro-simulation is used to test and verify the validity of proposed model. The results show that when the network congestion occurs, especially when traffic congestion is not uniform, the proposed model can improve the output vehicles for the whole network. Simultaneously, the existing number of vehicles in each zone can maintain at nearly optimal level. Thus verifying the effectiveness and feasibility of the boundary signal control model.

Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

Optimization of Automobile Suspension System Using Hybrid GSA Algorithm

In this paper Hybrid Genetic- Simulated Annealing Algorithm (GSA) is studied and applied to the suspension system of an automobile with two different case studies. Mathematical modelling of an automobile is done for passive suspension system. GA, SA and GSA were simulated using programming in MATLAB. First case study is based on the data of researcher in the field where GA was applied for optimization of automobile suspension system. Here SA, as well as Hybrid GSA are applied and compared with the results of GA. Convergences of the three methods are also plotted. In second case study an automobile model MARUTI 800 is considered. All the necessary parameters are calculated through measurements. The results shows required seat acceleration for current available springs of the automobile in field. For both the case studies the Hybrid GSA shows best results among the three. This can be performed before actual manufacturing of springs and automobile to check whether desired seat acceleration and ride comfort will achieve or not. If it is satisfied with the value of seat acceleration, decision would be made regarding manufacturing of springs with those values of stiffness and damping constants.

Multipass Turning Operation Process Optimization Using Hybrid Genetic Simulated Annealing Algorithm

For years, there has been increasing attention placed on the metal removal processes such as turning and milling operations; researchers from different areas focused on cutting conditions optimization. Cutting conditions optimization is a crucial step in Computer Aided Process Planning (CAPP); it aims to select optimal cutting parameters (such as cutting speed, feed rate, depth of cut, and number of passes) since these parameters affect production cost as well as production deadline. This paper deals with multipass turning operation optimization using a proposed Hybrid Genetic Simulated Annealing Algorithm (HSAGA). The SA-based local search is properly embedded into a GA search mechanism in order to move the GA away from being closed within local optima. The unit production cost is considered in this work as objective function to minimize under different practical and operational constraints. Taguchi method is then used to calibrate the parameters of proposed optimization approach. Finally, different results obtained by various optimization algorithms are compared to the obtained solution and the proposed hybrid evolutionary technique optimization has proved its effectiveness over other algorithms.

Estimation of shallow S-wave velocity structure and site response characteristics by microtremor array measurements in Tekirdag region, NW Turkey

In this study, we aimed to explore the S-wave velocity structure of shallow soils using microtremors in order to estimate site responses in Tekirdag and surrounding areas (NW Turkey). We collected microtremor array data at 44 sites in Tekirdag, Marmara Ereglisi, Corlu, and Muratlı. The phase velocities of Rayleigh waves were estimated from the microtremor data using a Spatial Autocorrelation method. Then, we applied a hybrid genetic simulated annealing algorithm to obtain a 1D S-wave velocity structure at each site. Comparison between the horizontal-to-vertical ratio of microtremors and computed ellipticities of the fundamental mode Rayleigh waves showed good agreement with validation models. The depth of the engineering bedrock changed from 20 to 50 m in the Tekirdag city center and along the coastline with a velocity range of 700–930 m/s, and it ranged between 10 and 65 m in Marmara Ereglisi. The average S-wave velocity of the engineering bedrock was 780 m/s in the region. We obtained average S-wave velocities in the upper 30 m to compare site amplifications. Empirical relationships between the AVs30, the site amplifications, and also average topographic slopes were established for use in future site effects microzonation studies in the region.

An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming.

In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

Zone Design of Tandem Loop AGVs Path with Hybrid Algorithm

AGV has been widely used in flexible manufacturing system. In order to prevent AGV collision for a workshop with complicated and crossover AGV paths, a dynamic tandem loop AGV path design method is proposed. A mathematical model for tandem loop AGV path optimization is established with the goal to minimize material flow between different loops of AGV path. AGV load balancing and material flow-within-loop balancing between different loops are taken as constraints. A hybrid genetic simulated annealing algorithm is proposed for optimization. The optimal scheme of allocating workstations to different tandem loops can be obtained finally. Case studies results, including an air conditioner assembly workshop AGV path design, have shown that the method is feasible and effective.