Enhance Differential Evolution Algorithm Based on Novel Mutation Strategy and Parameter Control Method

2015 ◽  
pp. 634-643
Author(s):  
Laizhong Cui ◽  
Genghui Li ◽  
Li Li ◽  
Qiuzhen Lin ◽  
Jianyong Chen ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Novel Mutation ◽  
Differential Evolution Algorithm ◽  
Parameter Control ◽  
Mutation Strategy ◽  
Evolution Algorithm

A Hybrid Differential Evolution Algorithm for Solving Function Optimization

2010 ◽  
Vol 439-440 ◽  
pp. 315-320
Author(s):  
Zhi Gang Zhou
Keyword(s):  
Differential Evolution ◽  
Novel Mutation ◽  
Experimental Studies ◽  
Differential Evolution Algorithm ◽  
Function Optimization ◽  
Test Cases ◽  
Key Points ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Mutation Strategies

One of the key points resulting in the success of differential evolution (DE) is its mechanism of different mutation strategies for generating mutant vectors. In this paper, we also present a novel mutation strategy inspired by the velocity updating scheme of particle swarm optimization (PSO). The proposed approach is called HDE, which conducts the mutation strategy on the global best vector for each generation. Experimental studies on 8 well-known benchmark functions show that HDE outperforms other three compared DE algorithms in most test cases.

A novel differential evolution algorithm with a self-adaptation parameter control method by differential evolution

2017 ◽  
Vol 22 (18) ◽  
pp. 6171-6190 ◽  
Author(s):  
Laizhong Cui ◽  
Genghui Li ◽  
Zexuan Zhu ◽  
Zhenkun Wen ◽  
Nan Lu ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Control Method ◽  
Differential Evolution Algorithm ◽  
Parameter Control ◽  
Self Adaptation ◽  
Evolution Algorithm

scholarly journals Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-36 ◽  
Author(s):  
Wei Li ◽  
Lei Wang ◽  
Quanzhu Yao ◽  
Qiaoyong Jiang ◽  
Lei Yu ◽  
...  
Keyword(s):  
Solid State ◽  
Differential Evolution ◽  
Liquid State ◽  
Differential Evolution Algorithm ◽  
Optimization Method ◽  
Benchmark Problems ◽  
Gaseous State ◽  
Mutation Strategy ◽  
Evolution Algorithm ◽  
Cloud Particles

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

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.

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