An improved search space resizing method for model identification by Standard Genetic Algorithm

In the multimodal transport network, due to various uncertain factors such as weather and traffic conditions, the transport time will become uncertain accordingly, besides, railway transport and water transport are usually limited by timetable. These factors will inevitably affect the path selection. The purpose of this study is to seek an optimal transport scheme that considers both the uncertainty of the multimodal transport network and the timetable limit. In view of the uncertainties of the multimodal transport network, interval data are used to represent the uncertainty of network weights, and robust optimization method is then adopted to process the interval data. An optimal model of robust shortest path considering timetable limit is established and genetic algorithm (GA) is designed to solve the problem. The GA designed provides an encoding method for variable-length chromosomes applicable to shortest path problem solving in the multimodal transport. And the handling methods for loops and inaccessible paths due to chromosome crossover and mutation are also suggested. And finally, numerical examples are provided to verify the validity of the model and algorithm.

Section: Genetic Algorithm Designmentioning

confidence: 99%

The robust shortest path problem for multimodal transportation considering timetable with interval data

Liu

Peng

Song

et al. 2018

“…First, we should pick up some 'interest points' at distinctive locations in an image, such as blobs, corners, and T-junctions. Then, we can obtain the descriptor of the neighbourhoods of every interest point by utilizing a feature vector (Nepomuceno, Martins, Amaral, & Riveret, 2017;Rajarathinam, Gomm, Yu, & Abdelhadi, 2017). Such a descriptor should be distinctive and, at the same time, robust to noise, detection errors, and geometric and photometric deformations.…”

Section: Introductionmentioning

confidence: 99%

An improved GMS-PROSAC algorithm for image mismatch elimination

Zhao

Ding

Wang

et al. 2018

Image matching usually plays a critical role for visual simultaneous localization and mapping (VSLAM). However, the resultant mismatches and low calculation efficiency of current matching algorithms reduces the performance of VSLAM. In order to overcome above two drawbacks, an improved GMS-PROSAC algorithm for image mismatch elimination is developed in this paper by introducing a new epipolar geometric constraint (EGC) model with a projection error function. This improved algorithm, named as GMS-EGCPROSAC, is made up of the traditional GMS algorithm and the improved PROSAC algorithm. First, the GMS algorithm is employed to obtain a rough matching set and then all matching pairs in this set are sorted according to their similarity degree. By selecting some smatching pairs with the highest similarity degree, the parameter of the EGC model is obtained. By resort to the calculated parameter, the improved PROSAC algorithm can be carried out to eliminate false matches. Finally, the real-time and the effectiveness are adequately verified by executing some contrast experiments. Our approach can not only quickly eliminate mismatches but also get more high-quality matching pairs.

“…Metaheuristic optimization algorithms can solve the complex optimization problems and search for a set of relevant parameter values by minimizing or maximizing the objective functions (Faris, Sheta, & Öznergiz, 2016). There are many famous meta-heuristic algorithms which include genetic algorithm (GA) (Mousavi-Avval, Rafiee, Sharifi, Hosseinpour, & Notarnicola, 2017;Rajarathinam, Gomm, Yu, & Abdelhadi, 2017), particle swarm optimization (PSO) algorithm (Chen et al, 2014;Satpati, Koley, & Datta, 2014), differential evolution (DE) algorithm (Long, Liang, Huang, & Chen, 2013;Piotrowski, 2016), ant colony optimization (ACO) (Chen, Zhou, & Luo, 2017;Samà, Pellegrini, D'Ariano, Rodriguez, & Pacciarelli, 2016), artificial bee colony (ABC) algorithm (Li, Gong, & Yang, 2014;Xue, Jiang, Zhao, & Ma, 2018), and gravitational search CONTACT Jing Zhang zjing133@sdust.edu.cn algorithm (GSA) (Mirjalili & Gandomi, 2017;Rashedi, Nezamabadi-Pour, & Saryazdi, 2011). Grey wolf optimization (GWO) algorithm, which was a new swarm intelligence algorithm based on the behaviour of grey wolves for global optimization, was proposed by Mirjalili (2014).…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid meta-heuristic algorithm for optimization problems

Gai

Liu

et al. 2018

This paper presents a novel hybrid meta-heuristic algorithm called HMGSG to solve the optimization problems. In the proposed HMGSG algorithm, a spiral-shaped path for grey wolf optimization (GWO) is used to ensure both the faster convergence rate and diversity. The mutualism phase of symbiotic organisms search (SOS) is introduced and modified with the adaptive benefit factors to optimize the ability of exploitation. The stud genetic algorithm (GA) is introduced into the HMGSG to promote convergence. The numerical experiment results show that the performance of HMGSG is superior to that of the GWO, SOS and GA. In addition, the HMGSG algorithm is used to optimize the fractionalorder PID controller parameters for roll attitude control of UAV. And the simulation results show the effectiveness of this algorithm.