Adaptive differential evolution with multi-population-based mutation operators for constrained optimization

Xu, Bin; Tao, Lili; Xu, Chunxiang; Cheng, Wushan

doi:10.1007/s00500-017-3001-0

Cited by 20 publications

(7 citation statements)

References 48 publications

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“…In [46] Differential Evolution parameters are adaptively adjusted according to the statistical information learned from the previous searches in generating improved solutions. In [5] fuzzy system is used to control population diversity at decision variable space by self-adapting the crossover rate control parameter.…”

Section: Adaptive Mechanismsmentioning

confidence: 99%

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Łapa,

Rutkowska,

Byrski

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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In this paper, a new mechanism for detecting population stagnation based on the analysis of the local improvement of the evaluation function and the infinite impulse response filter is proposed. The purpose of this mechanism is to improve the population stagnation detection capability for various optimization scenarios, and thus to improve multi-population-based algorithms (MPBAs) performance. In addition, various other approaches have been proposed to eliminate stagnation, including approaches aimed at both improving performance and reducing the complexity of the algorithms. The developed methods were tested, among the others, for various migration topologies and various MPBAs, including the MNIA algorithm, which allows the use of many different base algorithms and thus eliminates the need to select the population-based algorithm for a given simulation problem. The simulations were performed for typical benchmark functions and control problems. The obtained results confirm the validity of the developed method.

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Section: Adaptive Mechanismsmentioning

confidence: 99%

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Łapa,

Rutkowska,

Byrski

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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“…8. The wolves are mutated according to equations (18) and (19), and the wolves are processed out of range according to equation (22). 9.…”

Section: Grey Wolf Boundary Position Strategymentioning

confidence: 99%

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

Wang

Xie

et al. 2019

Journal of Algorithms & Computational Technology

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Aiming at the problem of wireless sensor network node coverage optimization with obstacles in the monitoring area, based on the grey wolf optimizer algorithm, this paper proposes an improved grey wolf optimizer (IGWO) algorithm to improve the shortcomings of slow convergence, low search precision, and easy to fall into local optimum. Firstly, the nonlinear convergence factor is designed to balance the relationship between global search and local search. The elite strategy is introduced to protect the excellent individuals from being destroyed as the iteration proceeds. The original weighting strategy is improved, so that the leading wolf can guide the remaining grey wolves to prey in a more reasonable way. The design of the grey wolf's boundary position strategy and the introduction of dynamic variation strategy enrich the population diversity and enhance the ability of the algorithm to jump out of local optimum. Then, the benchmark function is used to test the convergence performance of genetic algorithm, particle swarm optimization, grey wolf optimizer, and IGWO algorithm, which proves that the convergence performance of IGWO algorithm is better than the other three algorithms. Finally, the IGWO algorithm is applied to the deployment of wireless sensor networks with obstacles (rectangular obstacle, trapezoidal obstacle and triangular obstacles). Simulation results show that compared with GWO algorithm, IGWO algorithm can effectively improve the coverage of wireless sensor network nodes and obtain higher coverage rate with fewer nodes, thereby reducing the cost of deploying the network.

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“…Lixin combined adaptive population classification, adaptive control parameters, and mutation to create an Individual Dependent Mechanism (IDE) [33]. Some other concepts or methods were introduced, such as, neighborhood-based mutation operator (DEGL) [34], ensemble of parameters and mutation (EPSDE) [35], (MDEpBX) [36], a similarity-based mutant vector generation strategy (DE-SIM) [37], multipopulation-based mutation operators (CAMDE) [38], random neighbors based strategy (RNDE) [39], and adaptive lagrange interpolation search (ADELI) [40].…”

Section: Introductionmentioning

confidence: 99%

An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

Zhang

2021

Mathematics

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Recently, the differential evolution (DE) algorithm has been widely used to solve many practical problems. However, DE may suffer from stagnation problems in the iteration process. Thus, we propose an enhancing differential evolution with a rank-up selection, named RUSDE. First, the rank-up individuals in the current population are selected and stored into a new archive; second, a debating mutation strategy is adopted in terms of the updating status of the current population to decide the parent’s selection. Both of the two methods can improve the performance of DE. We conducted numerical experiments based on various functions from CEC 2014, where the results demonstrated excellent performance of this algorithm. Furthermore, this algorithm is applied to the real-world optimization problem of the four-bar linkages, where the results show that the performance of RUSDE is better than other algorithms.

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Adaptive differential evolution with multi-population-based mutation operators for constrained optimization

Cited by 20 publications

References 48 publications

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

A New Approach to Detecting and Preventing Populations Stagnation Through Dynamic Changes in Multi-Population-Based Algorithms

Node coverage optimization algorithm for wireless sensor networks based on improved grey wolf optimizer

An Enhancing Differential Evolution Algorithm with a Rank-Up Selection: RUSDE

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