Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Zhang, Xinming; Fu, Zihao; Chen, Haiyan; Mao, Wentao; Liu, Shangwang; Liu, Guoqi

doi:10.1109/access.2019.2936254

Cited by 8 publications

(10 citation statements)

References 40 publications

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“…Then, a local search is performed for the mutation of the frogs with the lowest merit toward the frogs with the highest merit. This mutation is according to the following equation (Zhang et al, 2019)…”

Section: Structure Of the Improved Sflamentioning

confidence: 99%

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

Ghadi,

Kotb,

Aboras

et al. 2023

Front. Energy Res.

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A combined algorithm for reconfiguring and placement of energy storage systems, electric vehicles, and distributed generation (DG) in a distribution network is presented in this paper. The impact of this technique on increasing the network’s resilience during critical periods is investigated, as well as improvements in the network’s technical and economic characteristics. The three objective functions in this regard are the network losses, voltage profile, and running costs, which include the costs of purchasing electrical energy from the upstream network and the devices used, as well as the cost of load shedding. In addition, the impacts of the presence of DG on power flow and the modeling of the problem’s objective function are examined. To solve the problem of reconfiguration and placement of a multi-objective distribution feeder, a genetic algorithm (GA) and shuffled frog leaping algorithm (SFLA) hybrid algorithm is used. The proposed GA–SFLA algorithm is used to solve the problem with changes in its structure and its combination in three stages. Finally, the proposed method is implemented on the 33-bus distribution network. The simulation results show that the proposed method has an effective performance in improving the considered objective functions and by establishing a suitable fit between the different objective functions based on the three-dimensional Pareto front. Moreover, it introduced a more optimized architecture with lower loss, lower operating costs, and greater reliability compared to other optimization algorithms.

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Section: Structure Of the Improved Sflamentioning

confidence: 99%

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

Ghadi,

Kotb,

Aboras

et al. 2023

Front. Energy Res.

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“…where I is the maximum immigration rate, E is the maximum emigration rate, N k is the number of species of the habitat H k , and N is the maximum number of species. From equations (1) and (2), the migration presents a simple linear model, but more often, there are complex nonlinear models [22]. e migration operator modifies the habitat's SIVs by accepting features from other good habitats, which can be expressed as follows:…”

Section: Migration Operatormentioning

confidence: 99%

“…(1) In ILxBBO, the mutation operator is removed to omit calculating the mutation probability and the mutation operation to reduce the computation complexity. (2) e best habitat and the subbest habitat adopt a dynamic two-differential perturbing operator to update in ILxBBO, while the first two best habitats use the Laplace operator to update in LxBBO. 3e worst habitat uses a two-global-best guiding operator to update, while the worst habitat also uses the Laplace operator in LxBBO.…”

Section: Dynamic Two-differential Perturbing Operator De Proposed Bymentioning

confidence: 99%

“…However, they cannot find the optimal solution on other problems or within a reasonable time. In recent decades, inspired by nature, many Intelligent Optimization Algorithms (IOAs) such as Particle Swarm Optimization (PSO) [1], Shuffled Frog Leaping Algorithm (SFLA) [2], Differential Evolution (DE) [3], Krill Herd (KH) [4], Harmony Search (HS) [5], Cuckoo Search (CS) [6], Grey Wolf Optimizer (GWO) [7], and Biogeography-Based Optimization (BBO) [8], have sprung up, and they are widely used in many areas [6,[9][10][11].…”

Section: Introductionmentioning

confidence: 99%

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Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

et al. 2020

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Laplacian Biogeography-Based Optimization (LxBBO) is a BBO variant which improves BBO’s performance largely. When it solves some complex problems, however, it has some drawbacks such as poor performance, weak operability, and high complexity, so an improved LxBBO (ILxBBO) is proposed. First, a two-global-best guiding operator is created for guiding the worst habitat mainly to enhance the exploitation of LxBBO. Second, a dynamic two-differential perturbing operator is proposed for the first two best habitats’ updating to improve the global search ability in the early search phase and the local one in the late search one, respectively. Third, an improved Laplace migration operator is formulated for other habitats’ updating to improve the search ability and the operability. Finally, some measures such as example learning, mutation operation removing, and greedy selection are adopted mostly to reduce the computation complexity of LxBBO. A lot of experimental results on the complex functions from the CEC-2013 test set show ILxBBO obtains better performance than LxBBO and quite a few state-of-the-art algorithms do. Also, the results on Quadratic Assignment Problems (QAPs) show that ILxBBO is more competitive compared with LxBBO, Improved Particle Swarm Optimization (IPSO), and Improved Firefly Algorithm (IFA).

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“…In this regard, new strategies have been integrated into existing algorithms to design an effective algorithm that can handle these defects. Some of these successful improvements use strategies including opposition-based learning [20,21], levy flight [22,23], mutation [24,25], rough sets [26,27], and chaotic maps [28,29].…”

Section: Introductionmentioning

confidence: 99%

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

et al. 2021

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The moth-flame optimization (MFO) algorithm is an effective nature-inspired algorithm based on the chemical effect of light on moths as an animal with bilateral symmetry. Although it is widely used to solve different optimization problems, its movement strategy affects the convergence and the balance between exploration and exploitation when dealing with complex problems. Since movement strategies significantly affect the performance of algorithms, the use of multi-search strategies can enhance their ability and effectiveness to solve different optimization problems. In this paper, we propose a multi-trial vector-based moth-flame optimization (MTV-MFO) algorithm. In the proposed algorithm, the MFO movement strategy is substituted by the multi-trial vector (MTV) approach to use a combination of different movement strategies, each of which is adjusted to accomplish a particular behavior. The proposed MTV-MFO algorithm uses three different search strategies to enhance the global search ability, maintain the balance between exploration and exploitation, and prevent the original MFO’s premature convergence during the optimization process. Furthermore, the MTV-MFO algorithm uses the knowledge of inferior moths preserved in two archives to prevent premature convergence and avoid local optima. The performance of the MTV-MFO algorithm was evaluated using 29 benchmark problems taken from the CEC 2018 competition on real parameter optimization. The gained results were compared with eight metaheuristic algorithms. The comparison of results shows that the MTV-MFO algorithm is able to provide competitive and superior results to the compared algorithms in terms of accuracy and convergence rate. Moreover, a statistical analysis of the MTV-MFO algorithm and other compared algorithms was conducted, and the effectiveness of our proposed algorithm was also demonstrated experimentally.

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Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Cited by 8 publications

References 40 publications

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

Improved Laplacian Biogeography-Based Optimization Algorithm and Its Application to QAP

MTV-MFO: Multi-Trial Vector-Based Moth-Flame Optimization Algorithm

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