Hybrid Harris Hawk Optimization Based on Differential Evolution (HHODE) Algorithm for Optimal Power Flow Problem

Biroğul, Serdar

doi:10.1109/access.2019.2958279

Cited by 44 publications

(23 citation statements)

References 80 publications

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“…When the generated mutation vector exceeds the boundary value, it will be replaced by the newly generated vector according to the boundary update rule. Some other mutation strategies have also been widely used in the DE algorithm [43][44][45].…”

Section: Mutation For Each Target Vector X Gmentioning

confidence: 99%

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the identification of the fractional order Hammerstein model by using proposed adaptive differential evolution with the Local search strategy (ADELS) algorithm with the steepest descent method and the overparameterization based auxiliary model recursive least squares (OAMRLS) algorithm. The parameters of the static nonlinear block and the dynamic linear block of the model are all unknown, including the fractional order. The initial value of the parameter is obtained by the proposed ADELS algorithm. The main innovation of ADELS is to adaptively generate the next generation based on the fitness function value within the population through scoring rules and introduce Chebyshev mapping into the newly generated population for local search. Based on the steepest descent method, the fractional order identification using initial values is derived. The remaining parameters are derived through the OAMRLS algorithm. With the initial value obtained by ADELS, the identification result of the algorithm is more accurate. The simulation results illustrate the significance of the proposed algorithm.

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Section: Mutation For Each Target Vector X Gmentioning

confidence: 99%

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Jin

Cai

et al. 2021

Mathematical Problems in Engineering

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“…Among them, HHO is the latest meta-heuristic algorithm proposed in 2019, due to its simple and easy to implement, less parameters to be tuned, and excellent performance, it has attracted widespread attention once it was proposed [22]- [25]. Therefore, in this paper, we firstly utilize HHO as the basis for solving QWSC.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, in this paper, we firstly utilize HHO as the basis for solving QWSC. However, one of the potential problems faced by all meta-heuristics is the possibility of early convergence or falling into local minima [25], in practice, it is usually required that the algorithm needs to be modified to take full advantage of the unique characteristics of the optimization problem to overcome this potential threat. Therefore, how to design a robust and high performance optimization technique for solving QWSC problem based on the HHO algorithm is still a challenging task.…”

Section: Introductionmentioning

confidence: 99%

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Chen

2020

IEEE Access

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Recently, with the rapid increase in the number of web services, QoS-aware Web Service Composition(QWSC) has become a popular topic in both industry and academia. Meta-heuristic algorithm, as an effective way to solve classical optimization problems, has been successfully applied to QWSC nowdays. However, such approach has intrinsic drawbacks and usually lack of good performance in large-scale scenarios. For example, some meta-heuristic algorithms are suitable for continuous search space, while the search space of QWSC is discrete. For solving those problems which were commonly faced when applying meta-heuristic algorithm on QWSC, in this research, we firstly introduce a preprocessing approach for constructing fuzzy continuous neighborhood relations of concrete services, which makes the local search strategy of meta-heuristic algorithms be as effective in discrete space as in continuous space, thus improving the optimization performance. Second, we combine Harris Hawks Optimization (HHO) algorithm and logical chaotic sequence to propose an improved meta-heuristic algorithm named CHHO for solving QWSC. The ergodic and chaotic characteristics of chaotic sequences are used to enhance the ability of the CHHO to jump out of the local optimum for further optimization. Experimental results show that the CHHO has better optimization performance by comparing with the existing mainstream algorithms when solving QWSC problems. Additionally, the preprocessing approach not only greatly improves the optimization performance of the CHHO but also can be freely utilized in other meta-heuristics based approaches. INDEX TERMS Meta-heuristic algorithm, QoS-aware web service composition, Harris hawks optimization, fuzzy continuous, logical chaotic sequence.

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“…Reference [10] obtained the optimal power flow solutions by adding a quadratic penalty term for slack variables and updating the quadratic variables and penalty coefficient in an iterative process. In [11], the optimal power flow (OPF) based on differential evolution algorithms is proposed without a valve-point effect and prohibited zones. Reference [12] proposes a dynamic adjustment strategy by using a genetic algorithm and interior point method to improve the efficiency of the hybrid algorithm.…”

Section: Introductionmentioning

confidence: 99%

Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

Liu

et al. 2020

IEEE Access

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The dynamic optimal power flow (DOPF) is a mixed-integer nonlinear programming problem. This paper builds a DOPF model with discrete and continuous variables, and then proposes the iterative method based on the master and sub-problems obtained from the generalized Benders decomposition (GBD). Firstly, the power output of conventional generators and the reactive power of the wind farm are modeled as the continuous decision variables, and the transformer taps ratio is built as a discrete decision variable. Secondly, the objective function is to minimize the total power generation cost and network loss. Thirdly, the DOPF problem is decomposed into the master problem and sub-problems by fixing a complex variable, which reduces the complexity of DOPF. Then, the proposed algorithm is used to solve the master and sub-problems. Finally, simulation results show that the proposed method has advantages in terms of reducing computational time and enhancing accuracy. INDEX TERMS DOPF model, wind power integration, discrete and continuous variables, GBD algorithm.

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Hybrid Harris Hawk Optimization Based on Differential Evolution (HHODE) Algorithm for Optimal Power Flow Problem

Cited by 44 publications

References 80 publications

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

Recursive Identification for Fractional Order Hammerstein Model Based on ADELS

A Meta-Heuristic-Based Approach for Qos-Aware Service Composition

Generalized Benders Decomposition Based Dynamic Optimal Power Flow Considering Discrete and Continuous Decision Variables

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