Hierarchical differential evolution algorithm combined with multi-cross operation

Liu, Zhao-Guang; Ji, Xuanrong; Yang, Yang

doi:10.1016/j.eswa.2019.04.040

Cited by 28 publications

(10 citation statements)

References 47 publications

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“…Differential evolution (DE) is a powerful evolutionary algorithm with three differential evolution operators for solving the tough global optimization problems [32]. Besides, DE has got more and more attention of scholars to evolve and improve in evolutionary computation, such as hybrid multiple crossover operations [33] and proposed DE/ neighbor/1 [34], due to its excellent global search capability. From these literature studies, we can see that DE has a good global search capability, so we will establish the guiding vector GV based on differential evolution operator to improve the global search capability of foraging behavior.…”

Section: E Guiding Vector Based On Differential Evolutionmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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Bird swarm algorithm is one of the swarm intelligence algorithms proposed recently. However, the original bird swarm algorithm has some drawbacks, such as easy to fall into local optimum and slow convergence speed. To overcome these short-comings, a dynamic multi-swarm differential learning quantum bird swarm algorithm which combines three hybrid strategies was established. First, establishing a dynamic multi-swarm bird swarm algorithm and the differential evolution strategy was adopted to enhance the randomness of the foraging behavior’s movement, which can make the bird swarm algorithm have a stronger global exploration capability. Next, quantum behavior was introduced into the bird swarm algorithm for more efficient search solution space. Then, the improved bird swarm algorithm is used to optimize the number of decision trees and the number of predictor variables on the random forest classification model. In the experiment, the 18 benchmark functions, 30 CEC2014 functions, and the 8 UCI datasets are tested to show that the improved algorithm and model are very competitive and outperform the other algorithms and models. Finally, the effective random forest classification model was applied to actual oil logging prediction. As the experimental results show, the three strategies can significantly boost the performance of the bird swarm algorithm and the proposed learning scheme can guarantee a more stable random forest classification model with higher accuracy and efficiency compared to others.

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Section: E Guiding Vector Based On Differential Evolutionmentioning

confidence: 99%

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Zhang

Xia

et al. 2020

Computational Intelligence and Neuroscience

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“…The value of the degree of greed control parameter P of the variation strategy in iLSHADE increases linearly as the number of fitness function evaluations increases (see Equation (20)).…”

Section: Ilshadementioning

confidence: 99%

“…Literature [11] summarizes the extensive research fields focused on DE in recent years. The algorithms based on DE [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] have gained very high rankings in various competitions held annually at the IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC). Therefore, DE-based algorithms are generally considered to be an effective and popular population-based evolutionary algorithm for singleobjective continuous optimization problems [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Modified jSO Algorithm for Solving Constrained Engineering Problems

et al. 2020

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Proposing new strategies to improve the optimization performance of differential evolution (DE) is an important research study. The jSO algorithm was the announced winner of the Congress on Evolutionary Computation (CEC) 2017 competition on numerical optimization, and is the state-of-the-art algorithm in the SHADE (Success-History based Adaptive Differential Evolution) algorithm series. However, the jSO algorithm converges prematurely in the search space with different dimensions and is prone to falling into local optimum during evolution, as well as the problem of decreasing population diversity. In this paper, a modified jSO algorithm (MjSO) is proposed which is based on cosine similarity with parameter adaptation and a novel opposition-based learning restart mechanism incorporated with symmetry to address the above problems, respectively. Moreover, it is well known that parameter setting has a significant impact on the performance of the algorithm and the search process can be divided into two symmetrical parts. Hence, a parameter control strategy based on a symmetric search process is introduced in the MjSO. The effectiveness of these designs is supported by presenting a population clustering analysis, along with a population diversity measure to evaluate the performance of the proposed algorithm, three state-of-the-art DE variant algorithms (EBLSHADE, ELSHADE-SPACMA, and SALSHADE-cnEPSin) and two original algorithms (jSO and LSHADE) are compared with it, for solving 30 CEC’17 benchmark functions and three classical engineering design problems. The experimental results and analysis reveal that the proposed algorithm can outperform other competitions in terms of the convergence speed and the quality of solutions. Promisingly, the proposed method can be treated as an effective and efficient auxiliary tool for more complex optimization models and scenarios.

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“…However, it has few issues such as convergence rate and local exploitation ability. In order to overcome its shortcomings, lots of robust and effective DE has been designed in the literature [24][25][26][27][28][29][30][31][32][33]. The characteristics, advantages, disadvantages with application of these techniques are presented below in brief.…”

Section: Introductionmentioning

confidence: 99%

Non-convex Dynamic Economic Dispatch Using an Innovative Hybrid Algorithm

Verma

Parouha

2021

J. Electr. Eng. Technol.

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An innovative hybrid algorithm (ihPSODE) is proposed in this paper to solve non-convex dynamic economic dispatch (DED) problem. It integrated with a novel particle swarm optimization (nPSO) and differential evolution (nDE). A new inertia weight, gradually varying acceleration coefficient and position update equation introduced in nPSO, to avoid stagnation. And in nDE a new mutation strategy and crossover rate are presented, to avoid premature convergence. Additionally, in ihPSODE after calculation identify best half member and discard rest of members from the population. In current population applied nPSO, to maintain exploration and exploitation. Then to enhance local search ability and improve convergence accuracy applied nDE. Hence, proposed ihPSODE has higher probability of avoiding local optima and it is likely to find global optima more quickly. Firstly, proposed ihPSODE as well as its anticipated integrating component nPSO and nDE are testified on IEEE CEC2006 constrained benchmark functions. Then performance of these proposed algorithms are evaluated with non-convex DED problem with valve-point effects using five different test systems (5-unit with losses, 10-unit without losses, 10-unit with losses, 15-unit with losses and 30-unit without losses). Several numerical and graphical experiments have been done to verify performances of proposed algorithms. Additionally, statistical and comparative analysis confirms superiority of proposed algorithms over many state-of-the-art algorithms.

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Hierarchical differential evolution algorithm combined with multi-cross operation

Cited by 28 publications

References 47 publications

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

Dynamic Multi-Swarm Differential Learning Quantum Bird Swarm Algorithm and Its Application in Random Forest Classification Model

A Modified jSO Algorithm for Solving Constrained Engineering Problems

Non-convex Dynamic Economic Dispatch Using an Innovative Hybrid Algorithm

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