scholarly journals Multi-Objective Shark Smell Optimization Algorithm Using Incorporated Composite Angle Cosine for Automatic Train Operation

Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 714
Author(s):  
Longda Wang ◽  
Xingcheng Wang ◽  
Zhao Sheng ◽  
Senkui Lu
Keyword(s):  
Optimization Algorithm ◽  
Trajectory Optimization ◽  
Evaluation Index ◽  
Local Optimum ◽  
Matlab Simulation ◽  
Multi Objective ◽  
Train Operation ◽  
Searching Ability ◽  
Automatic Train Operation ◽  
Aggregation Phenomenon

In this paper, an improved multi-objective shark smell optimization algorithm using composite angle cosine is proposed for automatic train operation (ATO). Specifically, when solving the problem that the automatic train operation velocity trajectory optimization easily falls into local optimum, the shark smell optimization algorithm with strong searching ability is adopted, and composite angle cosine is incorporated. In addition, the dual-population evolution mechanism is adopted to restrain the aggregation phenomenon in shark population at the end of the iteration to suppress the local convergence. Correspondingly, the composite angle cosine, considering the numerical difference and preference difference, is used as the evaluation index, which ameliorates the shortcoming that the traditional evaluation index is not objective and reasonable. Finally, the Matlab/simulation and hardware-in-the-loop simulation (HILS) results for automatic train operation show that the improved optimization algorithm proposed in this paper has better optimization performance.

scholarly journals Multi-Objective Hybrid Optimization Algorithm Using a Comprehensive Learning Strategy for Automatic Train Operation

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1882
Author(s):  
Longda Wang ◽  
Xingcheng Wang ◽  
Kaiwei Liu ◽  
Zhao Sheng
Keyword(s):  
Optimization Algorithm ◽  
Learning Strategy ◽  
Optimization Algorithms ◽  
Hybrid Optimization ◽  
Multi Objective ◽  
Chaotic Mapping ◽  
Hybrid Optimization Algorithm ◽  
Whale Optimization ◽  
Train Operation ◽  
Automatic Train Operation

Aiming at the problem of easy-to-fall-into local convergence for automatic train operation (ATO) velocity ideal trajectory profile optimization algorithms, an improved multi-objective hybrid optimization algorithm using a comprehensive learning strategy (ICLHOA) is proposed. Firstly, an improved particle swarm optimization algorithm which adopts multiple particle optimization models is proposed, to avoid the destruction of population diversity caused by single optimization model. Secondly, to avoid the problem of random and blind searching in iterative computation process, the chaotic mapping and the reverse learning mechanism are introduced into the improved whale optimization algorithm. Thirdly, the improved archive mechanism is used to store the non-dominated solutions in the optimization process, and fusion distance is used to maintain the diversity of elite set. Fourthly, a dual-population evolutionary mechanism using archive as an information communication medium is designed to enhance the global convergence improvement of hybrid optimization algorithms. Finally, the optimization results on the benchmark functions show that the ICLHOA can significantly outperform other algorithms for contrast. Furthermore, the ATO Matlab/simulation and hardware-in-the-loop simulation (HILS) results show that the ICLHOA has a better optimization effect than that of the traditional optimization algorithms and improved algorithms.

High-speed automatic train operation optimization algorithm

2013 ◽  
Vol 32 (11) ◽  
pp. 3221-3224
Author(s):  
Yue-zong LI ◽  
Peng-ling WANG ◽  
Xuan LIN ◽  
Qing-yuan WANG
Keyword(s):  
Optimization Algorithm ◽  
High Speed ◽  
Operation Optimization ◽  
Train Operation ◽  
Automatic Train Operation

Optimization of Reactive Power Based on Dynamic Learning Factor Multi-Objective Particle Swarm Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3984-3987
Author(s):  
Ying Ai ◽  
Yi Xin Su ◽  
Yao Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Reactive Power ◽  
Particle Swarm ◽  
Local Optimum ◽  
Dynamic Learning ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Bus System

. Particle swarm optimization algorithm has the defects of easy to fall into local optimum and low convergence accuracy used in reactive power optimization. To solve the problems, this paper proposed an improved particle swarm optimization algorithm based on dynamic learning factors. The two accelerations are changed with searching stage, so as to enhance the early globle search ability and the late local search ability, then to avoid local optimum; minimum particle angle method and crowded distance method are uesd to determine the global extremum in instalments, so as to improve the convergence speed and accuracy of multi-objective pareto solutions. Take the IEEE 30 bus system IEEE 118 bus system as example, the proposed method is compared with adaptive chaos particle swarm optimization (ACPSO) and NSGA-II, simulation results show that the method put forward in this paper has better convergence accuracy.

Multi-objective and discrete Elephants Herding Optimization algorithm for QoS aware web service composition

2019 ◽  
Vol 53 (2) ◽  
pp. 445-459 ◽  
Author(s):  
Samia Chibani Sadouki ◽  
Abdelkamel Tari
Keyword(s):  
Web Service ◽  
Optimization Algorithm ◽  
Service Composition ◽  
Global Optimum ◽  
Web Service Composition ◽  
Local Optimum ◽  
Complex Needs ◽  
Multi Objective ◽  
Local Searches ◽  
Conflicting Objectives

The goal of QoS aware web service composition (QoS-WSC) is to provide new functionalities and find a best combination of services to meet complex needs of users. QoS of the resulting composite service should be optimized. QoS-WSC is a global multi-objective optimization problem belonging to NP-hard class given the number of available services. Most of existing approaches reduce this problem to a single-objective problem by aggregating different objectives, which leads to a loss of information. An alternative issue is to use Pareto-based approaches. The Pareto-optimal set contains solutions that ensure the best trade-off between conflicting objectives. In this paper, a new multi-objective meta-heuristic bio-inspired Pareto-based approach is presented to address the QoS-WSC, it is based on Elephants Herding Optimization (EHO) algorithm. EHO is characterised by a strategy of dividing and combining the population to sub population (clan) which allows exchange of information between local searches to get a global optimum. However, the application of others evolutionary algorithms to this problem cannot avoids the early stagnancy in a local optimum. In this paper a discrete and multi-objective version of EHO will be presented based on a crossover operator. Compared with SPEA2 (Strength Pareto Evolutionary Algorithm 2) and MOPSO (Multi-Objective Particle Swarm Optimization algorithm), the results of experimental evaluation show that our improvements significantly outperform the existing algorithms in term of Hypervolume, Set Coverage and Spacing metrics.

scholarly journals Automatic Train Operation Speed Profile Optimization and Tracking with Multi-Objective in Urban Railway

2019 ◽  
Vol 48 (1) ◽  
pp. 57-64 ◽  
Author(s):  
Xiaomin Zhu ◽  
Qian Pu ◽  
Qian Zhang ◽  
Runtong Zhang
Keyword(s):  
Fuzzy Controller ◽  
Speed Profile ◽  
Control Effect ◽  
Multi Objective ◽  
Operation Speed ◽  
Train Operation ◽  
Objective Model ◽  
Simulation Results ◽  
Profile Optimization ◽  
Automatic Train Operation

Besides energy-efficiency, people also want train operation to be comfortable, punctual and parking precise. In this paper, a multi-objective model for automatic train operation in urban railway is proposed by unifying dimensions of different objectives firstly. This model is built by applying multi-objective decision with the penalty function, based on the analysis of train performance and its operation environment. Then a genetic algorithm is developed to solve this model and obtain the optimal recommended speed profiles. Thirdly, fuzzy controller is designed to achieve track recommended speed profiles. Finally, with the help of Matlab software, control effect is verified based on simulation. From the simulation results, it can be seen this strategy can meet the requirement of multi-objective, which are energy-saving, parking precisely, running punctually and comfort.

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