mutation strategy
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Symmetry ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 168
Trong-The Nguyen ◽  
Truong-Giang Ngo ◽  
Thi-Kien Dao ◽  
Thi-Thanh-Tan Nguyen

Microgrid operations planning is crucial for emerging energy microgrids to enhance the share of clean energy power generation and ensure a safe symmetry power grid among distributed natural power sources and stable functioning of the entire power system. This paper suggests a new improved version (namely, ESSA) of the sparrow search algorithm (SSA) based on an elite reverse learning strategy and firefly algorithm (FA) mutation strategy for the power microgrid optimal operations planning. Scheduling cycles of the microgrid with a distributed power source’s optimal output and total operation cost is modeled based on variables, e.g., environmental costs, electricity interaction, investment depreciation, and maintenance system, to establish grid multi-objective economic optimization. Compared with other literature methods, such as Genetic algorithm (GA), Particle swarm optimization (PSO), Firefly algorithm (FA), Bat algorithm (BA), Grey wolf optimization (GWO), and SSA show that the proposed plan offers higher performance and feasibility in solving microgrid operations planning issues.

2022 ◽  
Vol 10 ◽  
pp. 1-8
Saad Al-Ahmadi

Phishing websites have grown more recently than ever, and they become more intelligent, even against well-designed phishing detection techniques. Formerly, we have proposed in the literature a state-of-the-art URL-exclusive phishing detection solution based on Convolutional Neural Network (CNN) model, which we referred as PUCNN model. Phishing detection is adversarial as the phisher may attempt to avoid the detection. This adversarial nature makes standard evaluations less useful in predicting model performance in such adversarial situations. We aim to improve PUCNN by addressing the adversarial nature of phishing detection with a restricted adversarial scenario, as PUCNN has shown that an unrestricted attacker dominates. To evaluate this adversarial scenario, we present a parameterized text-based mutation strategy used for generating adversarial samples. These parameters tune the attacker’s restrictions. We have focused on text-based mutation due to our focus on URL-exclusive models. The PUCNN model generally showed robustness and performed well when the parameters were low, which indicates a more restricted attacker.

2022 ◽  
Vol 69 (1) ◽  
Abd-Elsalam R. Abd-Elhay ◽  
Wael A. Murtada ◽  
Mohamed I. Yosof

AbstractReaction wheels are crucial actuators in spacecraft attitude control subsystem (ACS). The precise modeling of reaction wheels is of fundamental need in spacecraft ACS for design, analysis, simulation, and fault diagnosis applications. The complex nature of the reaction wheel leads to modeling difficulties utilizing the conventional modeling schemes. Additionally, the absence of reaction wheel providers’ parameters is crucial for triggering a new modeling scheme. The Radial Basis Function Neural Network (RBFNN) has an efficient architecture, alluring generalization properties, invulnerability against noise, and amazing training capabilities. This research proposes a promising modeling scheme for the spacecraft reaction wheel utilizing RBFNN and an improved variant of the Quantum Behaved Particle Swarm Optimization (QPSO). The problem of enhancing the network parameters of the RBFNN at the training phase is formed as a nonlinear constrained optimization problem. Thus, it is proposed to efficiently resolve utilizing an enhanced version of QPSO with mutation strategy (EQPSO-2M). The proposed technique is compared with the conventional QPSO algorithm and different variants of PSO algorithms. Evaluation criteria rely upon convergence speed, mean best fitness value, stability, and the number of successful runs that has been utilized to assess the proposed approach. A non-parametric test is utilized to decide the critical contrast between the results of the proposed algorithm compared with different algorithms. The simulation results demonstrated that the training of the proposed RBFNN-based reaction wheel model with enhanced parameters by EQPSO-2M algorithm furnishes a superior prediction accuracy went with effective network architecture.

2022 ◽  
pp. 1-14
Hui Yu ◽  
Jun-qing Li ◽  
Xiao-Long Chen ◽  
Wei-meng Zhang

 During recent years, the outpatient scheduling problem has attracted much attention from both academic and medical fields. This paper considers the outpatient scheduling problem as an extension of the flexible job shop scheduling problem (FJSP), where each patient is considered as one job. Two realistic constraints, i.e., switching and preparation times of patients are considered simultaneously. To solve the outpatient scheduling problem, a hybrid imperialist competitive algorithm (HICA) is proposed. In the proposed algorithm, first, the mutation strategy with different mutation probabilities is utilized to generate feasible and efficient solutions. Then, the diversified assimilation strategy is developed. The enhanced global search heuristic, which includes the simulated annealing (SA) algorithm and estimation of distribution algorithm (EDA), is adopted in the assimilation strategy to improve the global search ability of the algorithm.?Moreover, four kinds of neighborhood search strategies are introduced to?generate new?promising?solutions.?Finally, the empires invasion strategy?is?proposed to?increase the diversity of the population. To verify the performance of the proposed HICA, four efficient algorithms, including imperialist competitive algorithm, improved genetic algorithm, EDA, and modified artificial immune algorithm, are selected for detailed comparisons. The simulation results confirm that the proposed algorithm can solve the outpatient scheduling problem with high efficiency.

2021 ◽  
pp. 1-14
Chenye Qiu ◽  
Ning Liu

This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power.

2021 ◽  
Vol 12 ◽  
Young-Su Yi ◽  
Han Gyung Kim ◽  
Ji Hye Kim ◽  
Woo Seok Yang ◽  
Eunji Kim ◽  

BackgroundInflammation, a vital immune response to infection and injury, is mediated by macrophage activation. While spleen tyrosine kinase (Syk) and myeloid differentiation primary response 88 (MyD88) are reportedly involved in inflammatory responses in macrophages, their roles and underlying mechanisms are largely unknown.MethodsHere, the role of the MyD88-Syk axis and the mechanism by which Syk and MyD88 cooperate during macrophage-mediated inflammatory responses are explored using knockout conditions of these proteins and mutation strategy as well as flowcytometric and immunoblotting analyses.ResultsSyk rapidly activates the nuclear factor-kappa B (NF-κB) signaling pathway in lipopolysaccharide (LPS)-stimulated RAW264.7 cells, and the activation of the NF-κB signaling pathway is abolished in Syk−/− RAW264.7 cells. MyD88 activates Syk and Syk-induced activation of NF-κB signaling pathway in LPS-stimulated RAW264.7 cells but Syk-induced inflammatory responses are significantly inhibited in MyD88−/− RAW264.7 cells. MyD88 interacts with Syk through the tyrosine 58 residue (Y58) in the hemi-immunoreceptor tyrosine-based activation motif (ITAM) of MyD88, leading to Syk activation and Syk-induced activation of the NF-κB signaling pathway. Src activates MyD88 by phosphorylation at Y58 via the Src kinase domain. In addition, Ras-related C3 botulinum toxin substrate 1 (Rac1) activation and Rac1-induced formation of filamentous actin (F actin) activate Src in LPS-stimulated RAW264.7 cells.ConclusionsThese results suggest that the MyD88-Syk axis is a critical player in macrophage-mediated inflammatory responses, and its function is promoted by an upstream Src kinase activated by Rac1-generated filamentous actin (F-actin).

2021 ◽  
Vol 12 (1) ◽  
pp. 35
Karn Moonsri ◽  
Kanchana Sethanan ◽  
Kongkidakhon Worasan ◽  
Krisanarach Nitisiri

This paper presents the Hybrid and Self-Adaptive Differential Evolution algorithms (HSADE) to solve an egg distribution problem in Thailand. We introduce and formalize a model for a multi-product, multi-depot vehicle routing problem with a time window, a heterogeneous fleet and inventory restrictions. The goal of the problem is to minimize the total cost. The multiple products comprise customers’ demands with different egg sizes. This paper presents a Mixed Integer Linear Programming (MILP) model, an initial solution-based constructive heuristic, a new self-adaptive mutation strategy, and a neighborhood search structure with the probability to improve DE. The two measurements of criteria are the heuristic performance (HP) compared with the solution obtained by MILP and the relative improvement (RI) of the solution compared with Thailand’s current egg distribution practice. The computational results show that the performance of HSADE is better than the current practice, and HSADE can provide on average a 14.13% improvement in total cost. Additionally, our proposed algorithm can be applied to similar agriculture logistics in Thailand and worldwide.

2021 ◽  
Vol 11 (23) ◽  
pp. 11192
Xiaoxu Yang ◽  
Jie Liu ◽  
Yi Liu ◽  
Peng Xu ◽  
Ling Yu ◽  

Aiming at the problems of the basic sparrow search algorithm (SSA) in terms of slow convergence speed and the ease of falling into the local optimum, the chaotic mapping strategy, adaptive weighting strategy and t-distribution mutation strategy are introduced to develop a novel adaptive sparrow search algorithm, namely the CWTSSA in this paper. In the proposed CWTSSA, the chaotic mapping strategy is employed to initialize the population in order to enhance the population diversity. The adaptive weighting strategy is applied to balance the capabilities of local mining and global exploration, and improve the convergence speed. An adaptive t-distribution mutation operator is designed, which uses the iteration number t as the degree of freedom parameter of the t-distribution to improve the characteristic of global exploration and local exploration abilities, so as to avoid falling into the local optimum. In order to prove the effectiveness of the CWTSSA, 15 standard test functions and other improved SSAs, differential evolution (DE), particle swarm optimization (PSO), gray wolf optimization (GWO) are selected here. The compared experiment results indicate that the proposed CWTSSA can obtain higher convergence accuracy, faster convergence speed, better diversity and exploration abilities. It provides a new optimization algorithm for solving complex optimization problems.

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