Self-feedback differential evolution adapting to fitness landscape characteristics

Li, Wei; Li, Shanni; Chen, Zhangxin; Zhong, Liu; Ouyang, Cheng

doi:10.1007/s00500-017-2833-y

Cited by 27 publications

(7 citation statements)

References 22 publications

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“…Recently in 2019, Li et al have proposed a self-feedback DE called SFDE [12]. The algorithm calculates the indicator number to approximate the number of optima in the fitness landscape presented by the population points and uses this number to adapt to the fitness landscape characteristics of the problem.…”

Section: Enhanced De Variantsmentioning

confidence: 99%

“…DEASC is compared with the well-known SaDE, jDE, and JADE algorithms first for 30dimensional and 100-dimensional functions, and then compared with the recently developed SFDE algorithm for 30-dimensional functions. The maximum number of function evaluations (maxnf ) are set the same as the original corresponding settings in [30] and in [12], respectively. Each algorithm is performed 50 independent runs.…”

Section: Performance Comparison Of Deasc and Some Adaptive De Variantsmentioning

confidence: 99%

“…Table 10 presents the performance comparison of DEASC and SFDE. The best values of SFDE are taken from the original paper [12]. The last column of the table shows the performances of DEASC compared with SFDE using the Welch t-test at a 0.05 level of significance.…”

Section: Performance Comparison Of Deasc and Some Adaptive De Variantsmentioning

confidence: 99%

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An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Puphasuk

Wetweerapong

2020

Foundations of Computing and Decision Sciences

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AbstractDesigning an efficient optimization method which also has a simple structure is generally required by users for its applications to a wide range of practical problems. In this research, an enhanced differential evolution algorithm with adaptation of switching crossover strategy (DEASC) is proposed as a general-purpose population-based optimization method for continuous optimization problems. DEASC extends the solving ability of a basic differential evolution algorithm (DE) whose performance significantly depends on user selection of the control parameters: scaling factor, crossover rate and population size. Like the original DE, the proposed method is aimed at e ciency, simplicity and robustness. The appropriate population size is selected to work in accordance with good choices of the scaling factors. Then, the switching crossover strategy of using low or high crossover rates are incorporated and adapted to suit the problem being solved. In this manner, the adaptation strategy is just a convenient add-on mechanism. To verify the performance of DEASC, it is tested on several benchmark problems of various types and di culties, and compared with some well-known methods in the literature. It is also applied to solve some practical systems of nonlinear equations. Despite its much simpler algorithmic structure, the experimental results show that DEASC greatly enhances the basic DE. It is able to solve all the test problems with fast convergence speed and overall outperforms the compared methods which have more complicated structures. In addition, DEASC also shows promising results on high dimensional test functions.

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Section: Enhanced De Variantsmentioning

confidence: 99%

Section: Performance Comparison Of Deasc and Some Adaptive De Variantsmentioning

confidence: 99%

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An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Puphasuk

Wetweerapong

2020

Foundations of Computing and Decision Sciences

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“…Of course, particle swarm optimization algorithm is a population intelligent optimization algorithm. In addition to particle swarm optimization algorithm, common intelligent algorithms include differential evolution algorithm (DE) [ 3 ], ant colony optimization (ACO) algorithm [ 4 ], artificial bee colony (ABC) [ 5 ], programming algorithm (FEP) [ 6 ], simulated annealing algorithm [ 7 ], neural network [ 8 ], text clustering [ 9 ], resource allocation [ 10 ], and task allocation [ 11 ]. Particle swarm optimization algorithm has been widely accepted with the advantages of rapid convergence, excellent robustness, and concise understanding.…”

Section: Introductionmentioning

confidence: 99%

Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

Xie

Huang

Liu

2022

Computational Intelligence and Neuroscience

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With the large-scale optimization problems in the real world becoming more and more complex, they also require different optimization algorithms to keep pace with the times. Particle swarm optimization algorithm is a good tool that has been proved to deal with various optimization problems. Conventional particle swarm optimization algorithms learn from two particles, namely, the best position of the current particle and the best position of all particles. This particle swarm optimization algorithm is simple to implement, simple, and easy to understand, but it has a fatal defect. It is hard to find the global optimal solution quickly and accurately. In order to deal with these defects of standard particle swarm optimization, this paper proposes a particle swarm optimization algorithm (SHMPSO) based on the hybrid strategy of seed swarm optimization (using codes available from https://gitee.com/mr-xie123234/code/tree/master/). In SHMPSO, a subpopulation coevolution particle swarm optimization algorithm is adopted. In SHMPSO, an elastic candidate-based strategy is used to find a candidate and realize information sharing and coevolution among populations. The mean dimension learning strategy can be used to make the population converge faster and improve the solution accuracy of SHMPSO. Twenty-one benchmark functions and six industries-recognized particle swarm optimization variants are used to verify the advantages of SHMPSO. The experimental results show that SHMPSO has good convergence speed and good robustness and can obtain high-precision solutions.

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“…Scholars have applied different approaches to the increasing number of structurally complex optimization problems, which are difficult to solve using traditional means, including evolutionary algorithms such as the genetic algorithm (GA) [ 1 ], bee colony (ABC) algorithm [ 2 ], difference (DE) algorithm [ 3 ], simulated annealing (SA) [ 4 ], ant colony (ACO) algorithm [ 5 ], and PSO [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy

Shen

Cai

Kang

et al. 2021

Entropy

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Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO.

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Self-feedback differential evolution adapting to fitness landscape characteristics

Cited by 27 publications

References 22 publications

An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

An enhanced differential evolution algorithm with adaptation of switching crossover strategy for continuous optimization

Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy

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