A Differential Evolution Algorithm Based on Self-Adapting Mountain-Climbing Operator

2012 ◽  
Vol 263-266 ◽  
pp. 2332-2338
Author(s):  
Sheng Lei ◽  
Wei Liu ◽  
Yao He Cai
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Convergence Speed ◽  
Search Process ◽  
Search Efficiency ◽  
Mountain Climbing ◽  
Evolution Algorithm ◽  
The Individual ◽  
Searching Ability ◽  
Evolution Period

This paper presents a Differential Evolution algorithm based on Self-Adapting Mountain-climbing operator (LCDE) to overcome the problem of low convergence speed and bad local searching ability in the evolution period. The algorithm dynamically adjusts the value of climb radius during using the information of the individual search efficiency in the search process. The experiment results demonstrate that the new differential evolution algorithm has fast convergence speed and high computation precision.

Research and Improvement on K-Means Clustering Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 3231-3235
Author(s):  
Xue Mei Wang ◽  
Jin Bo Wang
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Clustering Center ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed.

scholarly journals Multiscale Cooperative Differential Evolution Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-17 ◽  
Author(s):  
Yongzhao Du ◽  
Yuling Fan ◽  
Xiaofang Liu ◽  
Yanmin Luo ◽  
Jianeng Tang ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Early Stage ◽  
Differential Evolution Algorithm ◽  
Search Range ◽  
Adaptive Parameter ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Evolution Algorithms ◽  
The Individual

A multiscale cooperative differential evolution algorithm is proposed to solve the problems of narrow search range at the early stage and slow convergence at the later stage in the performance of the traditional differential evolution algorithms. Firstly, the population structure of multipopulation mechanism is adopted so that each subpopulation is combined with a corresponding mutation strategy to ensure the individual diversity during evolution. Then, the covariance learning among populations is developed to establish a suitable rotating coordinate system for cross operation. Meanwhile, an adaptive parameter adjustment strategy is introduced to balance the population survey and convergence. Finally, the proposed algorithm is tested on the CEC 2005 benchmark function and compared with other state-of-the-art evolutionary algorithms. The experiment results showed that the proposed algorithm has better performance in solving global optimization problems than other compared algorithms.

Research of Differential Evolution Algorithm for Motor Control

2014 ◽  
Vol 596 ◽  
pp. 216-221
Author(s):  
Zong Fu Wu
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
High Efficiency ◽  
Differential Evolution Algorithm ◽  
Optimization Analysis ◽  
Searching Strategy ◽  
Evolution Algorithm ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm ◽  
Chaos Searching

An improved differential evolution algorithm for solving nonlinear equations of electrical motor system is explored. The algorithm is to convert equations into an optimization problem and, by keeping consideration of the evolution process and adopting dynamic parameters adjusting mechanism, the algorithm can improve searching efficiency and implement real-time surveillance for population overlapping. The Chaos searching strategy is used for overlapping individual to further improve the ability of global optimization. Analysis results of induction motor motion parameters show that the improved differential evolution algorithm proposed in this paper has high efficiency and powerful global optimization searching ability.

Research on the Optimal Design of Soccer Robot Based on the Mechanical Analysis

2013 ◽  
Vol 380-384 ◽  
pp. 3854-3857
Author(s):  
Jian Wen Han ◽  
Lei Hong
Keyword(s):  
Differential Evolution ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
Mechanical Analysis ◽  
Clustering Quality ◽  
Local Optimal Solution ◽  
Evolution Algorithm ◽  
Searching Ability ◽  
Improved Differential Evolution Algorithm

According to the defects of classical k-means clustering algorithm such as sensitive to the initial clustering center selection, the poor global search ability, falling into the local optimal solution. A differential evolution algorithm which was a kind of a heuristic global optimization algorithm based on population was introduced in this article, then put forward an improved differential evolution algorithm combined with k-means clustering algorithm at the same time. The experiments showed that the method has solved initial centers optimization problem of k-means clustering algorithm well, had a better searching ability,and more effectively improved clustering quality and convergence speed

scholarly journals Application of Improved Differential Evolution Algorithm in Solving Equations

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Guiying Ning ◽  
Yongquan Zhou
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Convergence Speed ◽  
Evolutionary Strategy ◽  
Adaptive Mutation ◽  
Local Optimum ◽  
Algebraic Equations ◽  
Evolution Algorithm ◽  
Roots Of Equations ◽  
Improved Differential Evolution Algorithm

AbstractThe problem of finding roots of equations has always been an important research problem in the fields of scientific and engineering calculations. For the standard differential evolution algorithm cannot balance the convergence speed and the accuracy of the solution, an improved differential evolution algorithm is proposed. First, the one-half rule is introduced in the mutation process, that is, half of the individuals perform differential evolutionary mutation, and the other half perform evolutionary strategy reorganization, which increases the diversity of the population and avoids premature convergence of the algorithm; Second, set up an adaptive mutation operator and a crossover operator to prevent the algorithm from falling into the local optimum and improve the accuracy of the solution. Finally, classical high-order algebraic equations and nonlinear equations are selected for testing, and compared with other algorithms. The results show that the improved algorithm has higher solution accuracy and robustness, and has a faster convergence speed. It has outstanding effects in finding roots of equations, and provides an effective method for engineering and scientific calculations.

scholarly journals Optimising Pedestrian Flow Around Large Stadiums

2021 ◽  
Vol 6 ◽  
Author(s):  
Yuming Dong ◽  
Xiaolu Jia ◽  
Daichi Yanagisawa ◽  
Katsuhiro Nishinari
Keyword(s):  
Differential Evolution ◽  
Cellular Automaton ◽  
Differential Evolution Algorithm ◽  
Cellular Automaton Model ◽  
Pedestrian Flow ◽  
Evacuation Time ◽  
Specific Strategy ◽  
Evolution Algorithm ◽  
Surrounding Areas ◽  
The Individual

This study proposes a method that combines the cellular automaton model and the differential evolution algorithm for optimising pedestrian flow around large stadiums. A miniature version of a large stadium and its surrounding areas is constructed via the cellular automaton model. Special mechanisms are applied to influence the behaviour of an agent that leaves from a certain stadium gate. The agent may be attracted to a nearby business facility and/or guided to uncongested areas. The differential evolution algorithm is then used to determine the optimal probabilities of the influencing agents for each stadium gate. The main goal is to reduce the evacuation time, and other goals such as reducing the costs for the influencing agents’ behaviours and the individual evacuation time are also considered. We found that, although they worked differently in different scenarios, the attraction and guidance of agents significantly reduced the evacuation time. The optimal evacuation time was achieved with moderate attraction to the business facilities and strong guidance to the detouring route. The results demonstrate that the proposed method can provide a goal-dependent, exit-specific strategy that is otherwise hard to acquire for optimising pedestrian flow.

Multi-Population Coevolutionary Differential Evolution Algorithm

2010 ◽  
Vol 40-41 ◽  
pp. 235-241
Author(s):  
Yi Zhang ◽  
Xiu Xia Yang
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Population Evolution ◽  
Estimation Of Distribution ◽  
Evolution Algorithm ◽  
Simulation Results ◽  
The Difference ◽  
The Individual ◽  
Global Population ◽  
Chaotic Initialization

The multi-population coevolutionary differential evolution (DE) based on estimation of distribution algorithm (EDA) is proposed. DE completes optimum search using the difference information between the individuals in the population, but the global population evolution information can not be used sufficiently. In this paper, the multi-population co-evolutionary is introduced, which incorporate the merits of the DE and EDA. The elite mutation is proposed in DE. To overcome the greed characteristic, the chaotic initialization and replacement are introduced in DE and the individual diversity in EDA is adjusted based on the individual density. Simulation results show the good global search ability of the proposed algorithm.

An Adaptive Differential Evolution Algorithm for Flow Shop Scheduling to Minimize Makespan

2013 ◽  
Vol 411-414 ◽  
pp. 2089-2092
Author(s):  
Yan Ping Zhou
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Flow Shop Scheduling ◽  
Evolution Process ◽  
Adaptive Mutation ◽  
Shop Scheduling ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm ◽  
Searching Ability

This paper proposed an adaptive differential evolution algorithm. The algorithm has an adaptive mutation factor which can be nonlinear reduced along with evolution process. Mutation factor is declined slowly in the beginning of evolution process in order to improve the global searching ability of the algorithm, and declined rapidly in the later of evolution process. The proposed algorithm is applied to solve flow shop scheduling to minimize makespan, computational experiments on a typical scheduling benchmark shows that the algorithm has a good performance.

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