Self-adaptive differential evolution algorithm with improved mutation mode

Wang, Shihao; Li, Yuzhen; Yang, Hongyu

doi:10.1007/s10489-017-0914-3

Cited by 32 publications

(22 citation statements)

References 30 publications

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“…To assess the efficiency of the proposed approach, 10 widely used benchmark functions are applied in this section. ADE-SA is compared with several recent popular modified DE algorithms, including IDE [12], CDE [24], HDE [39], and IMMSADE [52], respectively.…”

Section: Numerical Experiments and Resultsmentioning

confidence: 99%

Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

Yang

2018

Mathematical Problems in Engineering

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Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.

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Section: Numerical Experiments and Resultsmentioning

confidence: 99%

Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

Yang

2018

Mathematical Problems in Engineering

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“…The experiment selects 21 benchmark functions from reference [15,[33][34][35], which include the low dimensional unimodal functions (F1, F2), the low dimensional multimodal functions (F3-F10), the high dimensional unimodal functions (F11-F17) and the high dimensional multimodal functions (F18-F21). The specific functions are shown in Table 1:…”

Section: Benchmark Functionsmentioning

confidence: 99%

An Improved Squirrel Search Algorithm for Global Function Optimization

Wang

2019

Algorithms

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An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

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“…For rigorous performance verification, Hybrid DA-DE is compared with the following 10 DE-based algorithms (i.e. IMMSADE [5], AGDE [7], EFADE [9], MPEDE [11], EDEV [14], Rank-jDE [15], SHADE [16], Rcr-JADE [17], LSHADE-EpSin [18], EsDEr-NR [21]) and 6 non-DE-based algorithms (i.e. MFO [42], MVO [43], DA [34], ALO [44], WOA [45] and SCA [46]), which are tested on CEC2005, CEC2015 and CEC2017 benchmark functions.…”

Section: Parameter Settings and Involved Algorithmsmentioning

confidence: 99%

“…Inappropriate configurations of mutation strategies and control parameters can cause stagnation or premature convergence. Therefore, many scholars have proposed a series of improved DE algorithms [5]- [33]. The contributions of DE modifications mainly focus on the following four orientations: mutation strategies, control parameters adaption or self-adaption, crossover strategies, hybrid mechanism.…”

Section: Introductionmentioning

confidence: 99%

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Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

Duan

Yang

et al. 2019

IEICE Trans. Inf. & Syst.

Self Cite

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Due to its simplicity and efficiency, differential evolution (DE) has gained the interest of researchers from various fields for solving global optimization problems. However, it is prone to premature convergence at local minima. To overcome this drawback, a novel hybrid dragonfly algorithm with differential evolution (Hybrid DA-DE) for solving global optimization problems is proposed. Firstly, a novel mutation operator is introduced based on the dragonfly algorithm (DA). Secondly, the scaling factor (F) is adjusted in a self-adaptive and individual-dependent way without extra parameters. The proposed algorithm combines the exploitation capability of DE and exploration capability of DA to achieve optimal global solutions. The effectiveness of this algorithm is evaluated using 30 classical benchmark functions with sixteen state-of-the-art meta-heuristic algorithms. A series of experimental results show that Hybrid DA-DE outperforms other algorithms significantly. Meanwhile, Hybrid DA-DE has the best adaptability to high-dimensional problems.

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Self-adaptive differential evolution algorithm with improved mutation mode

Cited by 32 publications

References 30 publications

Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

Adaptive Differential Evolution Based on Simulated Annealing for Large-Scale Dynamic Economic Dispatch with Valve-Point Effects

An Improved Squirrel Search Algorithm for Global Function Optimization

Hybridizing Dragonfly Algorithm with Differential Evolution for Global Optimization

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