Optimal scheduling with opposition based differential evolution optimized fixed head hydro-thermal power system

Jena, Chitralekha; Sinha, Pampa; Nanda, Lipika; Pradhan, Arjyadhara; Panda, Babita

doi:10.1016/j.matpr.2022.01.470

Cited by 1 publication

(3 citation statements)

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“…In Step 2, a termination criterion |f(X worst ) − f(X best )| < ε is given since f(X worst ) − f(X best ) is the denominator in Eq. (8). When this diference approximates to zero, the numerical stability of the algorithm will lose.…”

Section: Avoiding the Crossover Probability Crmentioning

confidence: 99%

“…F � 0.5, Cr � 0.9. Tis is a recommend parameters setting for DE/Rand/1 in most of the references [1][2][3][4][5][6][7][8][9][10][11][12][13]; Strategy 2(DEG): F ∼ N(0, 1), C r � 0.9 [11]; Strategy 3(DE0.4): F � 0.4 + 0.4 • rand(0, 1), C r � 0.9 [12] Strategy 4(DEM): C r � 0.5 and F is calculated by the following formula [13]:…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…For the parameter identifcation of solar cells, the original FSDE in reference [6] was improved, which is the hybridization between free search and DE with oppositionbased learning by using a simple greedy strategy instead of a Gaussian noise update in the process of the potential solution generation for the proposed best solution update strategy [7]. Reference [8] also employed a DE with opposition-based learning for estimating optimum hourly energy generation scheduling of a hydro-thermal system.…”

Section: Introductionmentioning

confidence: 99%

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Differential Evolution without the Scale Factor and the Crossover Probability

Zhang

2023

Journal of Mathematics

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Differential evolution has made great achievements in various fields such as computational sciences, engineering optimization, and operations management in the past decades. It is well known that the control parameter setting plays a very important role in terms of the performance improvement of differential evolution. In this paper, a differential evolution without the scale factor and the crossover probability is presented, which eliminates almost all control parameters except for the population size. The proposed algorithm looks upon each individual as a charged particle to decide on the shift of the individual in the direction of the difference based on the attraction-repulsion mechanism in Coulomb’s Law. Moreover, Taguchi’s parameter design method with the two-level orthogonal array is merged into the crossover operation in order to obtain better individuals in the next generation by means of better combination of factor levels. What is more, a new ratio of the signal-to-noise is proposed for the purpose of fair comparison of the numerical experiment for the tested functions which have an optimal value with 0. Numerical experiments show that the proposed algorithm outperforms the other 5 compared algorithms for the 10 benchmark functions.

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Section: Avoiding the Crossover Probability Crmentioning

confidence: 99%