scholarly journals A Novel Multicomponent PSO Algorithm Applied in FDE–AJTF Decomposition

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 51
Author(s):  
Lei Yu ◽  
Guochao Lao ◽  
Chunsheng Li ◽  
Yang Sun ◽  
Yingying Li
Keyword(s):  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Optimal Parameters ◽  
Time Frequency ◽  
Modified Maximum Likelihood ◽  
Maneuvering Targets ◽  
Polynomial Phase ◽  
Phase Signal ◽  
Evolution Algorithm

The echo of maneuvering targets can be expressed as a multicomponent polynomial phase signal (mc-PPS), which should be processed by time frequency analysis methods, while, as a modified maximum likelihood (ML) method, the frequency domain extraction-based adaptive joint time frequency (FDE–AJTF) decomposition method is an effective tool. However, the key procedure in the FDE–AJTF method is searching for the optimal parameters in the solution space, which is essentially a multidimensional optimization problem with different extremal solutions. To solve the problem, a novel multicomponent particle swarm optimization (mc-PSO) algorithm is presented and applied in the FDE–AJTF decomposition with the new characteristic that can extract several components simultaneously based on the feature of the standard PSO, in which the population is divided into three groups and the neighborhood of the best particle in the optimal group is set as the forbidden area for the suboptimal group, and then two different independent components can be obtained and extracted in one extraction. To analyze its performance, three simulation tests are carried out and compared with a standard PSO, genetic algorithm, and differential evolution algorithm. According to the tests, it is verified that the mc-PSO has the best performance in that the convergence, accuracy, and stability are improved, while its searching times and computation are reduced.

A Variables Clustering Based Differential Evolution Algorithm to Solve Multistage Goal Programming Model in Defense Projects Portfolio

2014 ◽  
Vol 1046 ◽  
pp. 367-370
Author(s):  
Yu Zhou ◽  
Yong Bin Li ◽  
Zhong Zheng Shi ◽  
Zheng Xin Li ◽  
Lei Zhang
Keyword(s):  
Differential Evolution ◽  
Goal Programming ◽  
Large Scale ◽  
Programming Model ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Pso Algorithm ◽  
Goal Programming Model ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm

The multistage goal programming model is popular to model the defense projects portfolio optimization problem in recent years. However, as its high-dimensional variables and large-scale solution space, the addressed model is hard to be solved in an acceptable time. To deal with this challenge, we propose an improved differential evolution algorithm which combines three novel strategies i.e. the variables clustering based evolution, the whole randomized parameters, and the child-individual based selection. The simulation results show that this algorithm has the fastest convergence and the best global searching capability in 6 test instances with different scales of solution space, compared with classical differential evolution algorithm (CDE), genetic algorithm (GA) and particle swarm optimization (PSO) algorithm.

scholarly journals Study on Component Characteristic Correction Algorithm of Turbofan Engine

2021 ◽  
Author(s):  
Shuai LIU ◽  
WANG Wei ◽  
BAI Jie ◽  
DAI ShiJie
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Differential Evolution Algorithm ◽  
Pso Algorithm ◽  
Correction Algorithm ◽  
Turbofan Engine ◽  
Model Modification ◽  
De Algorithm ◽  
Engine Component ◽  
Evolution Algorithm ◽  
Parameter Correction

Abstract The optimization of component characteristics is an important part of the research on model modification of turbofan engine. This paper studied three kinds of modification and optimization algorithms: genetic algorithm (GA), differential evolution algorithm (DE) and particle swarm optimization algorithm (PSO), and made a comparative analysis on the principles of the algorithms. In addition, the DGEN380 turbofan engine component model was taken as the research object to test and compare the modification performance of each algorithm. The results show that：compared with GA, DE algorithm and PSO algorithm have higher adaptability to multi-objective and multi-parameter correction, the success rate can reach 100%, the error is kept within 1%, and the convergence ability is strong. Compared with the other two algorithms, the solving speed of PSO algorithm is more influenced by the initial parameters. This conclusion can provide an important reference for selecting and improving the algorithm.

scholarly journals An Enhanced Differential Evolution Algorithm with Fast Evaluating Strategies for TWT-NFSP with SSTs and RTs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rong Hu ◽  
Xing Wu ◽  
Bin Qian ◽  
Jian L. Mao ◽  
Huai P. Jin
Keyword(s):  
Differential Evolution ◽  
Flow Shop ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Total Weighted Tardiness ◽  
Release Times ◽  
Evolution Algorithm ◽  
No Wait ◽  
Dependent Setup Times

The no-wait flow-shop scheduling problem with sequence-dependent setup times and release times (i.e., the NFSP with SSTs and RTs) is a typical NP-hard problem. This paper proposes an enhanced differential evolution algorithm with several fast evaluating strategies, namely, DE_FES, to minimize the total weighted tardiness objective (TWT) for the NFSP with SSTs and RTs. In the proposed DE_FES, the DE-based search is adopted to perform global search for obtaining the promising regions or solutions in solution space, and a fast local search combined with three presented strategies is designed to execute exploitation from these obtained regions. Test results and comparisons with two effective meta-heuristics show the effectiveness and robustness of DE_FES.

scholarly journals A Two-Stage Differential Evolution Algorithm with Mutation Strategy Combination

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2163
Author(s):  
Xingping Sun ◽  
Da Wang ◽  
Hongwei Kang ◽  
Yong Shen ◽  
Qingyi Chen
Keyword(s):  
Differential Evolution ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
Solution Space ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Two Stage ◽  
Mutation Strategy ◽  
Strategy Combination ◽  
Evolution Algorithm

For most of differential evolution (DE) algorithm variants, premature convergence is still challenging. The main reason is that the exploration and exploitation are highly coupled in the existing works. To address this problem, we present a novel DE variant that can symmetrically decouple exploration and exploitation during the optimization process in this paper. In the algorithm, the whole population is divided into two symmetrical subpopulations by ascending order of fitness during each iteration; moreover, we divide the algorithm into two symmetrical stages according to the number of evaluations (FEs). On one hand, we introduce a mutation strategy, DE/current/1, which rarely appears in the literature. It can keep sufficient population diversity and fully explore the solution space, but its convergence speed gradually slows as iteration continues. To give full play to its advantages and avoid its disadvantages, we propose a heterogeneous two-stage double-subpopulation (HTSDS) mechanism. Four mutation strategies (including DE/current/1 and its modified version) with distinct search behaviors are assigned to superior and inferior subpopulations in two stages, which helps simultaneously and independently managing exploration and exploitation in different components. On the other hand, an adaptive two-stage partition (ATSP) strategy is proposed, which can adjust the stage partition parameter according to the complexity of the problem. Hence, a two-stage differential evolution algorithm with mutation strategy combination (TS-MSCDE) is proposed. Numerical experiments were conducted using CEC2017, CEC2020 and four real-world optimization problems from CEC2011. The results show that when computing resources are sufficient, the algorithm is competitive, especially for complex multimodal problems.

Modified differential evolution algorithm for semi-supervised fuzzy clustering

2009 ◽  
Vol 29 (4) ◽  
pp. 1046-1047
Author(s):  
Song-shun ZHANG ◽  
Chao-feng LI ◽  
Xiao-jun WU ◽  
Cui-fang GAO
Keyword(s):  
Differential Evolution ◽  
Fuzzy Clustering ◽  
Differential Evolution Algorithm ◽  
Evolution Algorithm
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