Differential Evolution With an Individual-Dependent Mechanism

Tang, Lixin; Dong, Yuxiao; Liu, Jiyin

doi:10.1109/tevc.2014.2360890

Cited by 268 publications

(28 citation statements)

References 52 publications

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“…An adaptive version of DE named LSHADE44 [44] with reduction of the population size of four DE strategies is used in the first search stage. The adaptive DE variant with individual-dependent technique [45] is employed in the second search stage.…”

Section: Related Workmentioning

confidence: 99%

Push and pull search for solving constrained multi-objective optimization problems

Fan

Cai

et al. 2019

Swarm and Evolutionary Computation

347

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This paper proposes a push and pull search method in the framework of differential evolution (PPS-DE) to solve constrained singleobjective optimization problems (CSOPs). More specifically, two sub-populations, including the top and bottom sub-populations, are collaborated with each other to search global optimal solutions efficiently. The top sub-population adopts the pull and pull search (PPS) mechanism to deal with constraints, while the bottom sub-population use the superiority of feasible solutions (SF) technique to deal with constraints. In the top sub-population, the search process is divided into two different stages -push and pull stages. In the push stage, a CSOP is optimized without considering any constraints, which can help to get across to infeasible regions. In the pull stage, the CSOP is optimized with an improved epsilon constraint-handling method, which can help the population to search for feasible solutions. An adaptive DE variant with three trial vector generation strategies -DE /rand/1, DE/current-to-rand/1, and DE/current-to-pbest/1 is employed in the proposed PPS-DE. In the top sub-population, all the three trial vector generation strategies are used to generate offsprings, just like in CoDE. In the bottom sub-population, a strategy adaptation, in which the trial vector generation strategies are periodically self-adapted by learning from their experiences in generating promising solutions in the top sub-population, is used to choose a suitable trial vector generation strategy to generate one offspring. Furthermore, a parameter adaptation strategy from LSHADE44 is employed in both sup-populations to generate scale factor F and crossover rate CR for each trial vector generation strategy. Twenty-eight CSOPs with 10-, 30-, and 50-dimensional decision variables provided in the CEC2018 competition on real parameter single objective optimization are optimized by the proposed PPS-DE. The experimental results demonstrate that the proposed PPS-DE has the best performance compared with the other seven state-of-the-art algorithms, including AGA-PPS, LSHADE44, LSHADE44+IDE, UDE, IUDE, ǫMAg-ES and C 2 oDE.

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Section: Related Workmentioning

confidence: 99%

Push and pull search for solving constrained multi-objective optimization problems

Fan

Cai

et al. 2019

Swarm and Evolutionary Computation

347

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“…Assume that the nonlinear dynamical network in (11) can be forced to the following reference state s(t):…”

Section: Impulsive Controlmentioning

confidence: 99%

“…where f (e i (t)) =f (x i (t)) −f (s(t)). Consider heterogeneous impulsive effects in system (11) or (14), we obtain the following model:…”

Section: Impulsive Controlmentioning

confidence: 99%

“…In addition to these three basic operators, there are three control parameters which greatly influence the performance of DE: the mutation scale factor F, the crossover rate CR, and the population size NP. Most of the current research on DE has focused on four aspects to enhance the performance of DE: developing novel mutation operators [11][12][13][14][15][16][17][18][19], designing new parameter control strategies [11][12][13][20][21][22][23][24][25], improving crossover operator [12,[26][27][28], and pooling multiple mutation strategies [29][30][31][32][33]. These four categories of research on DE are described in detail as follows.…”

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confidence: 99%

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Differential Evolution With Event-Triggered Impulsive Control

Leung

Tang

et al. 2017

IEEE Trans. Cybern.

100

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Differential evolution (DE) is a simple but powerful evolutionary algorithm, which has been widely and successfully used in various areas. In this paper, an event-triggered impulsive (ETI) control scheme is introduced to improve the performance of DE. Impulsive control (IPC), the concept of which derives from control theory, aims at regulating the states of a network by instantly adjusting the states of a fraction of nodes at certain instants, and these instants are determined by event-triggered mechanism (ETM). By introducing IPC and ETM into DE, we hope to change the search performance of the population in a positive way after revising the positions of some individuals at certain moments. At the end of each generation, the IPC operation is triggered when the update rate of the population declines or equals to zero. In detail, inspired by the concepts of IPC, two types of impulses are presented within the framework of DE in this paper: 1) stabilizing impulses and 2) destabilizing impulses. Stabilizing impulses help the individuals with lower rankings instantly move to a desired state determined by the individuals with better fitness values. Destabilizing impulses randomly alter the positions of inferior individuals within the range of the current population. By means of intelligently modifying the positions of a part of individuals with these two kinds of impulses, both exploitation and exploration abilities of the whole population can be meliorated. In addition, the proposed ETI is flexible to be incorporated into several state-of-the-art DE variants. Experimental results over the IEEE Congress on Evolutionary Computation (CEC) 2014 benchmark functions exhibit that the developed scheme is simple yet effective, which significantly improves the performance of the considered DE algorithms.

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“…Many methods of EAs have been suggested to deal with such problems [13,14]. This optimization problem has also been considered by different heuristic methods such as; tabu search [15][16][17], simulated annealing [18][19][20], memetic algorithms [11], differential evolution [21,22], particle swarm optimization [23,24], ant colony optimization [25], variable neighborhood search [26], scatter search [27,28] and hybrid approaches [29][30][31]. Multiple applications in various areas such as computer engineering, computer science, economic, engineering, computational science and medicine can be expressed or redefined as problem in Equation (1), see [2,32] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

Hedar

Deabes

Almaraashi

et al. 2020

MCA

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Enhancing Evolutionary Algorithms (EAs) using mathematical elements significantly contribute to their development and control the randomness they are experiencing. Moreover, the automation of the primary process steps of EAs is still one of the hardest problems. Specifically, EAs still have no robust automatic termination criteria. Moreover, the highly random behavior of some evolutionary operations should be controlled, and the methods should invoke advanced learning process and elements. As follows, this research focuses on the problem of automating and controlling the search process of EAs by using sensing and mathematical mechanisms. These mechanisms can provide the search process with the needed memories and conditions to adapt to the diversification and intensification opportunities. Moreover, a new quadratic coding and quadratic search operator are invoked to increase the local search improving possibilities. The suggested quadratic search operator uses both regression and Radial Basis Function (RBF) neural network models. Two evolutionary-based methods are proposed to evaluate the performance of the suggested enhancing elements using genetic algorithms and evolution strategies. Results show that for both the regression, RBFs and quadratic techniques could help in the approximation of high-dimensional functions with the use of a few adjustable parameters for each type of function. Moreover, the automatic termination criteria could allow the search process to stop appropriately.

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Differential Evolution With an Individual-Dependent Mechanism

Cited by 268 publications

References 52 publications

Push and pull search for solving constrained multi-objective optimization problems

Push and pull search for solving constrained multi-objective optimization problems

Differential Evolution With Event-Triggered Impulsive Control

Evolutionary Algorithms Enhanced with Quadratic Coding and Sensing Search for Global Optimization

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