A Population‐Based Optimization Method Using Newton Fractal

Jeong, Soyeong; Kim, Pilwon

doi:10.1155/2019/5379301

Cited by 5 publications

(2 citation statements)

References 15 publications

(18 reference statements)

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“…Unlike in previously reported works, this research studies the problem of the optimal sizing and locating of PV sources in AC distribution networks operating at mediumvoltage levels, considering the annualized investment and operating costs of the PV sources summed with the total energy purchasing costs at the substation bus. The main contributions of our research were the following: (i) the application of a recently developed metaheuristic optimization algorithm named the Newton metaheuristic algorithm (NMA) with a mixed discrete-continuous codification that allows defining the set of nodes where the PV sources will be located as well as their optimal sizes [17]. The proposed optimization scheme is based on the master-slave approach, where the master stage is the NMA and the slave approach corresponds to a classical power flow method for AC distribution grids.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm

et al. 2021

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This research addresses the problem of the optimal placement and sizing of (PV) sources in medium voltage distribution grids through the application of the recently developed Newton metaheuristic optimization algorithm (NMA). The studied problem is formulated through a mixed-integer nonlinear programming model where the binary variables regard the installation of a PV source in a particular node, and the continuous variables are associated with power generations as well as the voltage magnitudes and angles, among others. To improve the performance of the NMA, we propose the implementation of a discrete–continuous codification where the discrete component deals with the location problem and the continuous component works with the sizing problem of the PV sources. The main advantage of the NMA is that it works based on the first and second derivatives of the fitness function considering an evolution formula that contains its current solution (xit) and the best current solution (xbest), where the former one allows location exploitation and the latter allows the global exploration of the solution space. To evaluate the fitness function and its derivatives, the successive approximation power flow method was implemented, which became the proposed solution strategy in a master–slave optimizer, where the master stage is governed by the NMA and the slave stage corresponds to the power flow method. Numerical results in the IEEE 34- and IEEE 85-bus systems show the effectiveness of the proposed optimization approach to minimize the total annual operative costs of the network when compared to the classical Chu and Beasley genetic algorithm and the MINLP solvers available in the general algebraic modeling system with reductions of 26.89% and 27.60% for each test feeder with respect to the benchmark cases.

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Section: Introductionmentioning

confidence: 99%

Efficient Reduction in the Annual Investment Costs in AC Distribution Networks via Optimal Integration of Solar PV Sources Using the Newton Metaheuristic Algorithm

et al. 2021

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“…Known human behavior-based algorithms primarily include Brain Strom Optimization [30], Soccer League Competition [31], and [32][33][34][35][36][37][38], etc. Familiar physicsbased algorithms include Thermal Exchange Optimization [39], Gravitational Search Algorithm [40], Sine Cosine Algorithm (SCA) [41], and [42][43][44][45][46][47][48][49][50], etc.…”

Section: Introductionmentioning

confidence: 99%

Political optimizer with interpolation strategy for global optimization

Zhu

et al. 2021

PLoS ONE

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Political optimizer (PO) is a relatively state-of-the-art meta-heuristic optimization technique for global optimization problems, as well as real-world engineering optimization, which mimics the multi-staged process of politics in human society. However, due to a greedy strategy during the election phase, and an inappropriate balance of global exploration and local exploitation during the party switching stage, it suffers from stagnation in local optima with a low convergence accuracy. To overcome such drawbacks, a sequence of novel PO variants were proposed by integrating PO with Quadratic Interpolation, Advance Quadratic Interpolation, Cubic Interpolation, Lagrange Interpolation, Newton Interpolation, and Refraction Learning (RL). The main contributions of this work are listed as follows. (1) The interpolation strategy was adopted to help the current global optima jump out of local optima. (2) Specifically, RL was integrated into PO to improve the diversity of the population. (3) To improve the ability of balancing exploration and exploitation during the party switching stage, a logistic model was proposed to maintain a good balance. To the best of our knowledge, PO combined with the interpolation strategy and RL was proposed here for the first time. The performance of the best PO variant was evaluated by 19 widely used benchmark functions and 30 test functions from the IEEE CEC 2014. Experimental results revealed the superior performance of the proposed algorithm in terms of exploration capacity.

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