Hybrid Search Algorithm Research Articles

Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

Integration of Train Regulation and Speed Profile Optimization Based on Feature Learning and Hybrid Search Algorithm

Two-Phase Hybrid Search Algorithm for Time-Dependent Cold Chain Logistics Route Considering Carbon Emission and Traffic Congestion

Two-Phase Hybrid Search Algorithm for Time-Dependent Cold Chain Logistics Route Considering Carbon Emission and Traffic Congestion

Innovative Path of Civic Education Management Mode in Colleges and Universities Based on Bayesian Networks

Abstract This paper constructs a Bayesian network structure learning model using both the Bayesian network and the Notears algorithm. Combined with acyclic graph and conditional probability table, the structure and parameters of BN are described respectively. Focusing on the basic methods of Bayesian network structure, the hybrid algorithm based on Notears is chosen as the basic method of structure learning by analyzing the application of the evaluation search method, constraint method and hybrid search algorithm. The results show that the ideal education management method should focus on the integration with real life, so much so that it has a low rate of detachment from reality as well as a low rate of theory, accounting for 0.35 and 0.4, respectively. The traditional way of management of ideology and political education in colleges and universities is more theoretical and detached from the actual situation of education and management, with theoretical management accounting for 0.75, and management is inefficient, with a percentage of 0.6. The research in this paper promotes, to a certain extent, the innovation of the management of the ideological education in colleges and universities, for the management of ideological education in colleges and universities.

Advanced framework for enhancing ultrasound images through an optimized hybrid search algorithm and a novel motion compounding processing chain

Ultrasound imaging is a fast, widespread, and essential diagnostic technique for examining the body’s internal anatomy to find abnormal tissues or diseases. However, speckle noise in ultrasound imaging corrupts fine details and edges and degrades the image’s resolution and contrast, making diagnosing more difficult. In this work, a speckle reduction method using motion compounding is proposed. The objective of this work is ultrasound image enhancement keeping all the diagnostics details and edges. The proposed method uses the pre-locations frames that the sonographer generates before locating the required diagnostic frame. These frames are applied to the proposed optimized Modified Three-Step Search (O-MTSS) algorithm to enhance the final processed frame. The O-MTSS algorithm is a hybrid between the Three Step Search algorithm (TSS) and the New Diamond Search Algorithm (NDS). The method requires a scoring layer for storing additional frames to select optimal frames that will be used for enhancement. The proposed methodology is tested on synthetic and real ultrasound images. The outcomes produce considerable speckle reduction while maintaining the image edges with good computational time for real-time scanning. The result is evaluated by subjective physicians, radiologists, and image evaluation metrics. According to the percentages of the subjective analysis, there is a remarkable improvement in the processed image. The proposed algorithm result is compared with existing algorithms; It is observed that the improvement in terms of signal to noise ratio, peak signal to noise ratio, mean square error, root mean square error and structural similarity index values of the proposed method are 4.6%, 3.32%, 12.02%, 6.2% and 1.57% respectively over Non-Local Low-Rank (NLLR) method. According to qualitative and quantitative analysis, the suggested method outperforms existing speckle reduction techniques regarding edge and fine detail preservation.

A framework for optimising flight efficiency of a crossing waypoint by balancing flight conflict frequency and flight-level usage benefits

AbstractWith the increase of air transportation, some crossing waypoints (CWPs) are becoming bottlenecks in the operation of air traffic networks. This paper presents a CWP operation optimisation framework based on a two-stage optimisation method. First, we considered the interests of airlines and air traffic controllers and established a flight-level dynamic allocation model for the CWP to minimise the flight-level deviation and the number of flight conflicts. A multi-objective, self-adaptive differential evolution-local search hybrid algorithm was used to solve the model in a parallel computing manner. Subsequently, a flight conflict resolution algorithm based on the Monte-Carlo tree search was designed for flight conflicts that existed after the optimisation. Finally, based on real operation data, four experimental scenarios were constructed, and the air traffic operation simulation system was used for experimental validation. For daily traffic and 1.2 times peak traffic scenarios, the average flight-level deviation reduction rates after optimisation were 53% and 39%, and the successful flight conflict resolution rates reached 89% and 75%, respectively. The experimental results showed that this optimisation framework can effectively balance the number of flight conflicts with the efficiency of flight-level usage and directly improve the capacity of the CWP, which can be used as a reference for air traffic control auxiliary decision support systems.

Flow Ordering Problem for Time-Triggered Traffic in the Scheduling of Time-Sensitive Networking

Time-Sensitive Networking (TSN) can ensure deterministic communications for time-critical traffic, which plays a crucial role in various real-time scenarios. In this letter, we propose and study a neglected problem in TSN, named flow ordering problem, which provides a new perspective on improving the scheduling of large-scale TSN. Specifically, we formulate the flow ordering problem, look into its theoretical basis, and prove this problem is NP-hard. Furthermore, we propose a hybrid search algorithm to provide an optimized scheduling order. Simulation results verify the significant impact of the flow ordering problem on TSN scheduling and the effectiveness of our algorithm.

Design and Parameter Optimization of Trajectory Correction Control Strategy for Air Duct Structure Projectile

In order to make the new air duct structure trajectory correction projectile have good dynamic correction control effect, the control strategy of the projectile’s correction mechanism is studied in this paper. A design method of trajectory correction control strategy based on particle swarm optimization-cuckoo search (PSO-CS) hybrid algorithm is proposed to obtain the optimal control parameters that can make the projectile flight stable and correct accurately. Firstly, the mathematical model of the air duct structure projectile is established. Secondly, the multiobjective optimization problem is analyzed. The projectile’s correction control strategy optimization model is established by taking the start control time, the number of corrections, the correction working time, and the interval time as the control variables. The optimization model innovatively considers the influence of the correction action on the flight stability of the projectile and the influence of the start control time on the correction range. Finally, the PSO-CS hybrid algorithm is used to design the calculation method of the optimization model and solve the optimal correction working parameters. The simulation results indicate that the control strategy optimization model can be solved by the proposed calculation method. Moreover, optimal correction working parameters of the correction mechanism in the current state can be obtained. Compared with the results of using single PSO algorithm and CS algorithm, the correction scheme calculated by PSO-CS hybrid optimization algorithm is better. This correction control scheme can effectively reduce the impact point deviation and make the projectile flight stable. At the same time, the circular error probable (CEP) of the projectile after correction is reduced from 42.3 m to 4.6 m while the impact point dispersion is lowered. The research results show that the design method of correction control strategy proposed in this paper is effective for trajectory correction of the new air duct structure projectile.

A Study of Hybridized Smell Agent Symbiotic Organism Search in Congress on Evolutionary Computation Functions

This paper presents a study of the Smell Agent Symbiotic Organism Search (SASOS) hybrid algorithm. SASOS is developed from bioinspired Smell Agent-Based Optimization(SAO) and Symbiosis Organism Search (SOS) algorithms. Bioinspired algorithms often lack a balance between speed and accuracy to achieve optimal performance efficiency and a global search for the best solution. To address these challenges, the algorithm reduces the imbalance between diversification and intensification in bioinspired algorithms to improve the search for global optima. SASOS performance was evaluated in sixteen selected Congress on Evolutionary Computation (CEC) functions using Aggregative Best Counts (ABC) compared to the regular SAO and SOS algorithms. For an advanced performance comparison, the convergence study was carried out on each CEC function to assess the fitness of the algorithms based on the Desirable Convergence Goal (DCG). Evaluation results using 50 iterations have shown that SASOS performed better withABCof56.25%than the SAO and SOS algorithms with ABC of 28.12% and 15.63%, respectively, in standard benchmark functions. Furthermore, in the convergence study, 1000 iterations were superimposed for each algorithm on the CEC functions. The convergence results showed that SASOS obtained the best DCG of 58.83%compared to SOS and SAO with DCG of 25.00% and 16.67%, respectively. These results made the performance of the hybrid SASOS uniquely different from other similar approaches.This is because the hybrid SASOS satisfactorily balanced the diversification and intensification phases in the bioinspired SAO and SOS algorithms. The eligible characteristics of the hybrid SASOS with respect to ABC and DCG showed its compatibilityand significance forvarious engineering optimizationapplications

Stochastic Multi-Objective Vehicle Routing Model in Green Environment With Customer Satisfaction

The Vehicle Routing Problem (VRP) is one of the most studied combinatorial optimization problems in operations research that are classified as NP-hard. Introducing uncertainty to the problem increases the complexity of solving such problems. Sources of uncertainty in a VRP can be travel times, service times, and unpredictable demands of customers. Ignoring these sources may lead to inaccurate modeling of the VRP. Moreover, the area of green logistics and the environmental issues associated received significant attention. This paper aims to study the stochastic multi-objective Vehicle Routing Problem in a green environment. The stochastic Green VRP (GVRP) presented deals with three objectives simultaneously that consider economic, environmental, and social aspects. First, a new hybrid search algorithm to solve the VRP is presented and validated. The algorithm is then employed to solve the stochastic multi-objective GVRP. Pareto fronts were obtained, and trade-offs between the three objectives are presented. Furthermore, an analysis of the effect of customers’ time window relaxation is presented.

A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS–ABC Algorithm

This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms.

Clustering Driven Iterated Hybrid Search for Vertex Bisection Minimization

The Vertex Bisection Minimization Problem (VBMP) is a relevant graph partitioning model with a variety of practical applications. This work introduces a clustering driven iterated hybrid search algorithm (CLUHS), which is the first approach that applies clustering to reinforce iterated local search for solving VBMP. The proposed CLUHS uses hierarchical clustering to build an initial solution, guide local search process and perform search diversification. Experimental studies on 137 benchmark instances show the high competitiveness of the proposed approach compared to the state-of-the-art methods. In particular, CLUHS finds new record-breaking solutions for 18 instances.

Mayfly Sparrow Search Hybrid Algorithm for RFID Network Planning

The radio frequency identification (RFID) technology is frequently used for large-scale RFID network planning, which brings thought-provoking RFID network planning (RNP) problems such as the uncertain number of readers, high interference rate of readers, poor economic benefits, and weak robustness. In this paper, we propose a mayfly sparrow search hybrid algorithm with Circle chaotic map and Lévy flight, which is named MASSA. MASSA maps out the position of the readers by adding the idea of the mayfly algorithm (MA) into the optimization process of the sparrow search algorithm (SSA). Circle chaos mapping and Lévy flight are added in the SSA optimization process and the nonlinear inertia coefficients to balance the relationship between global search and local search. The MASSA is compared with SSA, Improved sparrow search algorithm (ISSA), MA, particle swarm optimization algorithm (PSO), and gray wolf algorithm (GWO). The first part of the experiment uses the CEC2017 test suite to evaluate algorithm performance and analysis strategy effectiveness. The results encourage the application of MASSA to solve RNP problems. In the second part, experiments were conducted with four objective functions of coverage, interference, power, and load balance. Two different experiments were carried out with fixed number of readers and flexible number of readers under random label conditions. Experimental results show that MASSA has good planning performance, better economy, and robustness.

An effective hybrid search algorithm for the multiple traveling repairman problem with profits

The multiple traveling repairman problem with profits consists of multiple repairmen serving a subset of all customers to maximize the revenues collected through the visited customers. To address this problem, an effective hybrid search algorithm based on the memetic framework is proposed. In the proposed method, three features are integrated: a dedicated arc-based crossover to generate high-quality offspring solutions, a fast evaluation technique to reduce the complexity of navigating classical neighborhoods as well as a correcting step to ensure accurate evaluation of neighboring solutions. The performance of the algorithm on 470 benchmark instances were compared with those of the leading reference algorithms. The results show that the proposed algorithm outperforms the state-of-the-art algorithms by setting new records for 137 instances and matching the best-known results for 330 instances. The importance of the key search components of the algorithm was investigated.

Multi-objective design optimization using hybrid search algorithms with interval uncertainty for thin-walled structures

Thin-walled structures have been widely used in vehicle components due to their lightweight characteristics. To design structural parameters that are more in line with actual requirements, it is often necessary to consider the influence of uncertainty in design parameters. An effective multi-objective design optimization method using hybrid search algorithms under interval uncertainty (MDOHSA) is proposed in this study. The MDOHSA mainly consists of the modified gray​ wolf optimization (mGWO) and the pattern search (PS) algorithms. The mGWO algorithm is applied to derive a new solution set and the PS algorithm is used to quickly search the lower and upper bounds of interval responses in MDOHSA. The non-dominated strategy considering uncertainty is designed by the possibility degree of interval value. The mGWO is designed by improving the initialization population and the updating mechanism. Some standard test functions are implemented to analyze the proposed method, and the result shows that the mGWO algorithm is superior to the other analogous algorithms. In this study, the car subframe, the hexagonal fractal structure, and the bracket of an intelligent sanitation vehicle are optimized by MDOHSA. The above results show that the optimization method proposed in this study is effective. The feasible solution of the multi-objective design optimization method can be selected according to the interval deviation degree of design variables and performance requirements. This study develops an effective multi-objective design optimization method for thin-walled structures under interval uncertainty and can continue to be used for the uncertain optimization of other engineering problems.

RESEARCH ON FUZZY SCHEDULING OF CLOUD COMPUTING TASKS BASED ON HYBRID SEARCH ALGORITHMS AND DIFFERENTIAL EVOLUTION

In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.

Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

Hybrid and adaptive harmony search algorithm for optimizing energy efficiency in VMP problem in cloud environment

Data Center energy usage has risen dramatically because of the rapid growth and demand for cloud computing. This excessive energy usage is a challenge from an economic and environmental point. Virtual Machine Placement (VMP) along with virtualization technologies is widely used to manage power utilization in data centers. The assignment of virtual machines to physical machines affects energy consumption. VMP is a process of mapping VMs onto a set of PMs in a data center to minimize total power consumption and maximize resource utilization. The VMP is an NP-hard problem due to its constraints and huge combinations. In this paper, we formulated the problem as a single objective optimization problem in which the objective is to minimize the energy consumption in cloud data centers. The main contribution of this paper is hybrid and adaptive harmony search algorithm for optimal placements of VMs to PMs. HSA with adaptive PAR settings, simulated annealing and local search strategy aims at minimizing energy consumption in cloud data centers with satisfying given constraints. Experiments are conducted to validate the performance of these variations. Results show that these hybrid HSA variations produce better results than basic HSA and adaptive HSA. Hybrid HS with simulated annealing, and local search strategy gives better results than other variants for 80 percent datasets.

Research on the development and path exploration of autonomous underwater robots

With the increasing development of marine science and technology, autonomous underwater robots have become a hot spot in the field of underwater research and applications, and have been widely used in deep-sea scientific research, marine resources research, and deep-sea security. This paper focuses on analyzing the current research and development status and application prospects of autonomous underwater robots and predicts their future development trend. Aiming at the difficulties in engineering practice of path search in underwater unknown space, the traditional space path exploration method is analyzed, and a hybrid search algorithm is proposed to plan a reasonable and efficient dynamic path for target search in unknown underwater space.

Classification of Lung Sounds Using Ant Colony Optimization and Monte Carlo Tree Search Hybrid Algorithm

Classification of Lung Sounds Using Ant Colony Optimization and Monte Carlo Tree Search Hybrid Algorithm

Drivable Path Planning Using Hybrid Search Algorithm Based on E* and Bernstein–Bézier Motion Primitives

This article proposes a hybrid path-planning algorithm, the HE* algorithm, which combines the discrete grid-based E* search and continuous Bernstein–Bézier (BB) motion primitives. Several researchers have addressed the smooth path planning problem and the sample-based integrated path planning techniques. We believe that the main benefits of the proposed approach are: directly drivable path, no additional post-optimization tasks, reduced search branching, low computational complexity, and completeness guarantee. Several examples and comparisons with the state-of-the-art planners are provided to illustrate and evaluate the main advantages of the HE* algorithm. HE* yields a collision-safe and smooth path that is close to spatially optimal (the Euclidean shortest path) with a guaranteed continuity of curvature. Therefore, the path is easily drivable for a wheeled robot without any additional post-optimization and smoothing required. HE* is a two-stage algorithm which uses a direction-guiding heuristics computed by the E* search in the first stage, which improves the quality and reduces the complexity of the hybrid search in the second stage. In each iteration, the search is expanded by a set of BBs, the parameters of which adapt continuously according to the guiding heuristics. Completeness is guaranteed by relying on a complete node mechanism, which also provides an upper bound for the calculated path cost. A remarkable feature of HE* is that it produces good results even at coarse resolutions.