A novel character segmentation method for serial number on banknotes with complex background

Zhou, Jingling; Wang, Feng; Xu, Jianrong; Yan, Yun; Zhu, Huiqing

doi:10.1007/s12652-018-0707-5

Cited by 14 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This algorithm proposed 4 kinds of calculation methods on particle speed and position update, which effectively improved the effectiveness of each particle search. 3)A method of adding user preferences (Wang et al, 2019;Zhou et al, 2018). The preference of such methods in multi-objective optimization means that the decision maker assigns different values to the goal to indicate its importance or priority order.…”

Section: Introductionmentioning

confidence: 99%

Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

Zhang

Xie

2021

International Journal of Cognitive Informatics and Natural Intelligence

View full text Add to dashboard Cite

Many-objective optimization problems (MaOPs) refer to those multi-objective problems (MOPs) withmore than three objectives. In order to solve MaOPs, a multi-objective particle swarm optimization algorithm based on new fitness assignment and multi cooperation strategy(FAMSHMPSO) is proposed. Firstly, this paper proposes a new fitness allocation method based on fuzzy information theory to enhance the convergence of the algorithm. Then a new multi criteria mutation strategy is introduced to disturb the population and improve the diversity of the algorithm. Finally, the external files are maintained by the three-point shortest path method, which improves the quality of the solution. The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation and IGD+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms. The experimental results show that FAMSHMPSO algorithm has obvious performance advantages in convergence, diversity and robustness.

show abstract

Section: Introductionmentioning

confidence: 99%

Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

Zhang

Xie

2021

International Journal of Cognitive Informatics and Natural Intelligence

View full text Add to dashboard Cite

show abstract

“…The system of these phenomena possesses the property of self-feedback. Through the iteration of initial values, the distribution of ergodicity and randomness is formed (Zhou et al 2018). Self-feedback search is carried out in the iterative process.…”

Section: Introductionmentioning

confidence: 99%

A self-feedback strategy differential evolution with fitness landscape analysis

Huang

Li²,

Ouyang³

et al. 2018

Soft Comput

View full text Add to dashboard Cite

Differential evolution (DE) has been widely applied to complex global optimization problems. Different search strategies have been designed to find the optimum conditions in a fitness landscape. However, none of these strategies works well over all possible fitness landscapes. Since the fitness landscape associated with a complex global optimization problem usually consists of various local landscapes, each search strategy is efficient in a particular type of fitness landscape. A reasonable approach is to combine several search strategies and integrate their advantages to solve global optimization problems. This paper presents a new self-feedback strategy differential evolution (SFSDE) algorithm based on fitness landscape analysis of single-objective optimization problem. In the SFSDE algorithm, in the analysis of the fitness landscape features of fitnessdistance correlation, a self-feedback operation is used to iteratively select and evaluate the mutation operators of the new SFSDE algorithm. Moreover, mixed strategies and self-feedback transfer are combined to design a more efficient DE algorithm and enhance the search range, convergence rate and solution accuracy. Finally, the proposed SFSDE algorithm is implemented to optimize soil water textures, and the experimental results show that the proposed SFSDE algorithm reduces the difficulty in estimating parameters, simplifies the solution process and provides a novel approach to calculate the parameters of the Van Genuchten equation. In addition, the proposed algorithm exhibits high accuracy and rapid convergence and can be widely used in the parameter estimation of such nonlinear optimization models.

show abstract

A New High-Dimensional Particle Swarm Evolution Algorithm Based on New Fitness Allocation and Multi-criteria Strategy

Zhang

Xie

2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

A novel character segmentation method for serial number on banknotes with complex background

Cited by 14 publications

References 28 publications

Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

A self-feedback strategy differential evolution with fitness landscape analysis

A New High-Dimensional Particle Swarm Evolution Algorithm Based on New Fitness Allocation and Multi-criteria Strategy

Contact Info

Product

Resources

About