Finder-MCTS: A Cognitive Spectrum Allocation Based on Traveling State Priority and Scenario Simulation in IoV

With the increasing number of intelligent connected vehicles, the problem of scarcity of communication resources has become increasingly obvious. It is a practical issue with important significance to explore a real-time and reliable dynamic spectrum allocation scheme for the vehicle users, while improving the utilization of the available spectrum. However, previous studies have problems such as local optimum, complex parameter setting, learning speed, and poor convergence. Thus, in this paper, we propose a cognitive spectrum allocation method based on traveling state priority and scenario simulation in IoV, named Finder-MCTS. The proposed method integrates offline learning with online search. This method mainly consists of two stages. Initially, Finder-MCTS gives the allocation priority of different vehicle users based on the vehicle’s local driving status and global communication status. Furthermore, Finder-MCTS can search for the approximate optimal allocation solutions quickly online according to the priority and the scenario simulation, while with the offline deep neural network-based environmental state predictor. In the experiment, we use SUMO to simulate the real traffic flows. Numerical results show that our proposed Finder-MCTS has 36.47%, 18.24%, and 9.00% improvement on average than other popular methods in convergence time, link capacity, and channel utilization, respectively. In addition, we verified the effectiveness and advantages of Finder-MCTS compared with two MCTS algorithms’ variations.

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Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212464 ◽

2022 ◽

pp. 1-12

Author(s):

Yang Li ◽

Simeng Chen ◽

Ke Bai ◽

Hao Wang

Keyword(s):

Path Planning ◽

Electrostatic Discharge ◽

Fire Risk ◽

Convergence Time ◽

Economic Losses ◽

Risk Level ◽

Support Vector ◽

Initial Population ◽

Local Optimum ◽

Planning Problem

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

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Assessing the Performance of a SAR Boat Location-Allocation Plan via Simulation

Operations Research for Military Organizations - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-5225-5513-1.ch004 ◽

2019 ◽

pp. 67-97

Author(s):

Mumtaz Karatas ◽

Nasuh Razi ◽

Hakan Tozan

Keyword(s):

Optimal Allocation ◽

Aegean Sea ◽

Allocation Scheme ◽

Loss Of Life ◽

Location Allocation ◽

Maritime Search And Rescue ◽

Two Stages ◽

Critical Process ◽

Level Process

Maritime search and rescue (SAR) operation is a critical process that aims to minimize the loss of life, injury, and material damage by rendering aid to persons in distress or imminent danger at sea. Optimal allocation of SAR vessels is a strategic level process that is to be carried out with a plan to react rapidly. This chapter seeks to evaluate the performance of a SAR boat location plan using simulation. The proposed methodology in this chapter works in two stages: First, an optimal allocation scheme of SAR resources is determined via a multi-objective mathematical model. Next, simulation is used to test the performance of the analytical solution under stochastic demand. With the heaviest traffic and maritime risk, the methodology is applied to a case study in the Aegean Sea.

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A Biological Immune Mechanism-Based Quantum PSO Algorithm and Its Application in Back Analysis for Seepage Parameters

Mathematical Problems in Engineering ◽

10.1155/2020/2191079 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Jiacheng Tan ◽

Liqun Xu ◽

Kailai Zhang ◽

Chao Yang

Keyword(s):

Back Analysis ◽

Computation Time ◽

Vaccination Strategy ◽

Pso Algorithm ◽

Population Diversity ◽

Local Optimum ◽

Immune Mechanism ◽

Convergence Performance ◽

Poor Convergence ◽

Quantum Pso

Back analysis for seepage parameters is a classic issue in hydraulic engineering seepage calculations. Considering the characteristics of inversion problems, including high dimensionality, numerous local optimal values, poor convergence performance, and excessive calculation time, a biological immune mechanism-based quantum particle swarm optimization (IQPSO) algorithm was proposed to solve the inversion problem. By introducing a concentration regulation strategy to improve the population diversity and a vaccination strategy to accelerate the convergence rate, the modified algorithm overcame the shortcomings of traditional PSO which can easily fall into a local optimum. Furthermore, a simple multicore parallel computation strategy was applied to reduce computation time. The effectiveness and practicability of IQPSO were evaluated by numerical experiments. In this paper, taking one concrete face rock-fill dam (CFRD) as a case, a back analysis for seepage parameters was accomplished by utilizing the proposed optimization algorithm and the steady seepage field of the dam was analysed by the finite element method (FEM). Compared with immune PSO and quantum PSO, the proposed algorithm had better global search ability, convergence performance, and calculation rate. The optimized back analysis could obtain the permeability coefficient of CFRD with high accuracy.

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A Study on Approximation Performances of Improved Bp Neural Networks Based on LM Algorithms

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.411-414.1935 ◽

2013 ◽

Vol 411-414 ◽

pp. 1935-1938 ◽

Cited By ~ 6

Author(s):

Shuo Ding ◽

Xiao Heng Chang ◽

Qing Hui Wu

Keyword(s):

Adaptive Learning ◽

Numerical Optimization ◽

Nonlinear Function ◽

Learning Rate ◽

Bp Algorithm ◽

Convergence Time ◽

Nonlinear Functions ◽

Adaptive Learning Rate ◽

Marquardt Algorithm ◽

Poor Convergence

When approximating nonlinear functions, standard BP algorithms and traditional improved BP algorithms have low convergence rate and tend to be stuck in local minimums. In this paper, standard BP algorithm is improved by numerical optimization algorithm. Firstly, the principle of Levenberg-Marquardt algorithm is introduced. Secondly, to test its approximation performance, LMBP neural network is programmed via MATLAB7.0 taking specific nonlinear function as an example. Thirdly, its approximation result is compared with those of standard BP algorithm and adaptive learning rate algorithm. Simulation results indicate that compared with standard BP algorithm and adaptive learning rate algorithm, LMBP algorithm overcomes deficiencies ranging from poor convergence ability, prolonged convergence time, increasing iteration steps to nonconvergence. Thus with its good approximation ability, LMBP algorithm is the most suitable for medium-sized networks.

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Optimal allocation to treatments in a sequential multiple assignment randomized trial

Statistical Methods in Medical Research ◽

10.1177/09622802211037066 ◽

2021 ◽

pp. 096228022110370

Author(s):

Andrea Morciano ◽

Mirjam Moerbeek

Keyword(s):

Optimal Design ◽

Randomized Trial ◽

Trial Design ◽

Optimal Allocation ◽

Total Sample ◽

Shiny App ◽

Multiple Assignment ◽

Optimal Allocations ◽

Two Stages ◽

Current Stage

One of the main questions in the design of a trial is how many subjects should be assigned to each treatment condition. Previous research has shown that equal randomization is not necessarily the best choice. We study the optimal allocation for a novel trial design, the sequential multiple assignment randomized trial, where subjects receive a sequence of treatments across various stages. A subject's randomization probabilities to treatments in the next stage depend on whether he or she responded to treatment in the current stage. We consider a prototypical sequential multiple assignment randomized trial design with two stages. Within such a design, many pairwise comparisons of treatment sequences can be made, and a multiple-objective optimal design strategy is proposed to consider all such comparisons simultaneously. The optimal design is sought under either a fixed total sample size or a fixed budget. A Shiny App is made available to find the optimal allocations and to evaluate the efficiency of competing designs. As the optimal design depends on the response rates to first-stage treatments, maximin optimal design methodology is used to find robust optimal designs. The proposed methodology is illustrated using a sequential multiple assignment randomized trial example on weight loss management.

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Genetic algorithm method for solving the optimal allocation of response resources problem on the example of polish zone of the Baltic Sea / Metoda algorytmów genetycznych do rozwiązywania problemów optymalnej alokacji środków do zwalczania rozlewów olejowych na przykładzie polskiej strefy Morza Bałtyckiego

Journal of Konbin ◽

10.1515/jok-2016-0028 ◽

2016 ◽

Vol 38 (1) ◽

pp. 291-310

Author(s):

Kinga Łazuga ◽

Lucjan Gucma

Keyword(s):

Genetic Algorithm ◽

Optimal Allocation ◽

Optimization Methods ◽

Genetic Optimization ◽

Location Allocation ◽

Second Stage ◽

Genetic Algorithm Method ◽

Logistical Problem ◽

The Baltic ◽

Two Stages

Abstract The paper presents research related to optimal allocation of response vessels. Research belong to the logistical problem, location-allocation type (LA). Research is focused on vessels belongs to polish Search and Rescue. For the optimal allocation of resources used two-stages method wherein the first stage, using genetic optimization methods and consist in such allocation of response vessels to minimize costs of the spill at sea. In the second stage uses an accurate simulation model of oil spill combat action to verify the solutions obtained by genetic algorithm method.

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Individual Disturbance and Attraction Repulsion Strategy Enhanced Seagull Optimization for Engineering Design

Mathematics ◽

10.3390/math10020276 ◽

2022 ◽

Vol 10 (2) ◽

pp. 276

Author(s):

Helong Yu ◽

Shimeng Qiao ◽

Ali Asghar Heidari ◽

Chunguang Bi ◽

Huiling Chen

Keyword(s):

Optimization Algorithm ◽

Optimization Problems ◽

Solution Space ◽

Search Process ◽

Local Optimum ◽

Engineering Optimization ◽

New Variant ◽

Practical Engineering ◽

Poor Convergence ◽

Swarm Intelligence Algorithm

The seagull optimization algorithm (SOA) is a novel swarm intelligence algorithm proposed in recent years. The algorithm has some defects in the search process. To overcome the problem of poor convergence accuracy and easy to fall into local optimality of seagull optimization algorithm, this paper proposed a new variant SOA based on individual disturbance (ID) and attraction-repulsion (AR) strategy, called IDARSOA, which employed ID to enhance the ability to jump out of local optimum and adopted AR to increase the diversity of population and make the exploration of solution space more efficient. The effectiveness of the IDARSOA has been verified using representative comprehensive benchmark functions and six practical engineering optimization problems. The experimental results show that the proposed IDARSOA has the advantages of better convergence accuracy and a strong optimization ability than the original SOA.

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Multi-objective Water Resources Optimum Allocation Scheme Based on Improved Standard Cuckoo Searching Algorithm (ISCSA)

10.21203/rs.3.rs-192405/v1 ◽

2021 ◽

Author(s):

Ke Zhou

Keyword(s):

Water Resources ◽

Water Supply ◽

Optimal Allocation ◽

Optimal Solution ◽

Local Optimum ◽

Ecological Benefits ◽

Multi Objective ◽

Intelligent Optimization Algorithm ◽

Water Resources Optimal Allocation

Abstract The standard cuckoo searching algorithm(SCSA)is a population intelligent optimization algorithm, which is also a new heuristic searching algorithm. The advantages of SCSA (such as convenient operation, heuristic searching, etc.) make it easy to find the optimal solution and maintain wider searching range. However, SCSA also has some drawbacks, such as long searching time, easy to fall into local optimum. In order to solve the problems existed in SCSA, in this paper, the improved standard cuckoo searching algorithm (ISCSA) was studied, which includes chaotic initialization and Gaussian disturbed algorithm. As a case study, taking economic, social and ecological benefits as the objective function, the multi-objective water resources optimal allocation models were constructed in Xianxiang Region, China. The ISCSA was applied to solve the water allocation models and the multi-objective optimal water supply scheme for Xinxiang region was obtained. The water resources optimal allocation schemes in the planning level year (2025) for 12 water supply sub-regions were predicted. The desirable eco-environment and benefits were achieved using the studied methods. The results show that the ISCSA has obvious advantages in the solution of water resources optimal allocation and planning.

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