Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Hussain, Kashif; Zhu, William; Salleh, Mohd Najib Mohd

doi:10.1109/access.2019.2946664

Cited by 71 publications

(34 citation statements)

References 51 publications

(61 reference statements)

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“…In response to these problems, some scholars have proposed improvement strategies from different perspectives. For example, literature [42] introduced long-term memory to the HHO algorithm, allowing individuals to exercise based on experience, increasing the population's diversity. However, it ignored the running time of the algorithm and was less effective in highdimensional problems.…”

Section: Use Local Communication Betweenmentioning

confidence: 99%

“…In each experimental test, the performance of the improved ADHHO algorithm was compared with classical SI algorithm and other improved versions of HHO, including the original HHO [35], PSO [12], FF [21], Long-term memory Harris hawks optimization (LMHHO) [42] and Dynamic Harris hawks optimization with mutation mechanism (DHHO/M) [36]. TABLE 4 lists the parameter settings of all algorithms.…”

Section: B Parameter Settingmentioning

confidence: 99%

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Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

2020

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Harris hawks optimization (HHO) is a new swarm intelligence optimization technique. Because of its simple structure and easy to implement, HHO has attracted research interest from scholars in different fields. However, the low population diversity and the single search method in the exploration phase weakened the global search capability of the HHO algorithm. In response to these defects, this paper proposes an improved HHO algorithm based on adaptive cooperative foraging and dispersed foraging strategies. First, the adaptive cooperative foraging strategy uses three random individuals to guide the position update, which achieves cooperation between individuals. Then the cooperation behavior is embedded in the onedimensional update operation framework, and the one-dimensional or total-dimensional update operation is adaptively selected. This way allows the algorithm to perform position update operations for a specific dimension of individual vectors with a certain probability, which improves the population diversity. Second, the dispersed foraging strategy is introduced into the HHO, forcing a part of Harris hawks to leave their current position to find more prey to obtain a better candidate solution. This way effectively avoids the algorithm falling into local optimum. Finally, a randomly shrinking exponential function is used to simulate the energy change of the prey, so that the algorithm maintains the exploration ability in the later exploitation process, effectively balancing the exploration and exploitation ability of the algorithm. The performance of the proposed ADHHO algorithm is evaluated using Wilcoxon's test on unimodal, multimodal and CEC 2014 benchmark functions. Numerical results and statistical experiments show that ADHHO provides better solution quality, convergence accuracy and stability compared with other state-of-the-art algorithms. INDEX TERMS Harris hawks optimization, adaptive cooperative foraging, dispersed foraging, Wilcoxon's test, CEC 2014 benchmark functions.

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Section: Use Local Communication Betweenmentioning

confidence: 99%

Section: B Parameter Settingmentioning

confidence: 99%

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

2020

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“…Arora et al designed an improved Harris hawks optimizer which is used to analysis the sensitivity of load frequency control problem including wind power penetration 99 . Hussain et al applied HHO problem to solve the problem including optimum and high dimensional power flow 100 . Du et al designed hybrid multiobjective Harris hawks optimizer (MOHHO) for daily check PM 2.5 and PM 10 forecasting with high stability and accuracy 75 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…99 Hussain et al applied HHO problem to solve the problem including optimum and high dimensional power flow. 100 Du et al designed hybrid multiobjective Harris hawks optimizer (MOHHO) for daily check PM 2.5 and PM 10 forecasting with high stability and accuracy. 75 Islam et al designed single and multi-objective optimum power flow including emission of environment.…”

Section: Literature Reviewmentioning

confidence: 99%

ACanis lupusinspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

Nandi

Kamboj

2020

Numerical Meth Engineering

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Recently established Harris hawks optimization (HHO) has natural behavior for finding an optimum solution in global search space without getting trapped in previous convergence. However, the exploitation phase of the current Harris hawks optimizer algorithm is poor. In the present research, an improved version of the HHO algorithm, which combines Harris hawks optimizer with Canis lupus inspire grey wolf optimizer (GWO) and named as hHHO-GWO algorithm, has been proposed to find the solution of various optimization problems such as nonlinear, nonconvex, and highly constrained engineering design problem. In the proposed research, the phase of exploration and exploitation of the existing HHO algorithm has been further improved using GWO algorithm and its performance has been tested for various benchmarks problems including CEC2005 (unimodal, multimodal, and fixed dimensions functions), multimodal functions with variable dimensions, and CEC-BC-2017 test functions. Further, the developed hybrid optimizer has been tested for 11 different engineering design and optimization problems and experimental results of hHHO-GWO have been compared with other optimizer.

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“…In previous literature there can be seen that implementation of HHO in OPF problems is yet to be found. Kashif Hussain et al is considered as the first attempt to apply HHO on OPF problem [58].…”

Section: Introductionmentioning

confidence: 99%

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

Biroğul

2019

IEEE Access

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Harri's Hawk Optimization (HHO) algorithm manifests as a new meta-heuristic algorithm in literature. When we look at studies that have used with this algorithm, we can see that its results in test functions and in the solutions of some test functions in IEEE Congress on Evolutionary Computation (CEC) are much better compared to other heuristic and meta heuristic algorithm results. In this study, an algorithm has been developed which has been hybridized with the mutation operators of Differential Evolution (DE) to further improve the HHO algorithm. This algorithm is named as Hybrid Harris Hawk Optimization based on Differential Evolution (HHODE). Performance of the proposed HHODE algorithm has been first compared with HHO and then compared with the results of other algorithms which have been most commonly used in the literature. In this comparison process, the most commonly used test functions in the literature and some of the other test functions in CEC2005 and CEC2017 as a new application field, have been solved. When the results of the comparison of HHODE with other algorithms are analyzed, it is observed that the balance between the exploratory tendency and exploitative tendency of the algorithm is well consistent. Formula 1 ranking method is used in the order of HHODE according to HHO and other algorithms. When a general evaluation of HHODE was performed, it was found to be an even more powerful algorithm as a result of the combination of strong features of both HHO and DE. The optimal power flow (OPF) problem is one of the most important problems of the modern power system. The HHODE algorithm is proposed to solve the OPF problem, which is considered without valve-point effect and prohibited zones (1) and with prohibited zones (2) in this paper. The effectiveness of the HHODE hybrid algorithm is tested on modified IEEE 30bus test system. The result of HHODE algorithms are compared with other optimization algorithms in the literature.

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Long-Term Memory Harris’ Hawk Optimization for High Dimensional and Optimal Power Flow Problems

Cited by 71 publications

References 51 publications

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

Improved Harris Hawks Optimization Based on Adaptive Cooperative Foraging and Dispersed Foraging Strategies

ACanis lupusinspired upgraded Harris hawks optimizer for nonlinear, constrained, continuous, and discrete engineering design problem

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

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