An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems

Dhawale, Dinesh; Kamboj, Vikram Kumar; Anand, Priyanka

doi:10.1007/s00366-021-01487-4

Cited by 27 publications

(13 citation statements)

References 125 publications

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“…The successful capture relies on attacking strategies of Hawks and escaping nature of prey depending upon change of escape (r). Hawks perform a soft besiege for r ≥ 0.5&|E| ≥ 0.5 [24].…”

Section: Mathematical Formulation Of Hybrid Hho-igwo Algorithmmentioning

confidence: 99%

Optimum generation scheduling incorporating wind energy using HHO–IGWO algorithm

Dhawale

Kamboj

Anand

2023

Journal of Electrical Systems and Inf Technol

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Recently, renewable energy participation is gaining importance in the existing power system. However, the large penetration of these renewable energy sources into the existing power system network may cause an imbalance in supply and demand response. Unit commitment is the decision-making process in which generating units are turned ON and OFF at the hourly interval as per the load demand under certain constraints to provide economic scheduling. Thus, an advanced intelligent approach is needed to cope with this combined unit commitment problem with a large penetration of intermittent sources. This paper offers the solution to optimal scheduling by implementing the hybrid Harris Hawks optimizer algorithm (HHO–IGWO). Standard IEEE systems with 10-, 19-, 20-, and 40 units are simulated. Further, to test the feasibility and effectiveness of the proposed method, a comparative analysis for a 10-, 20-, and 40-unit system has also been performed with penetration. The comparative analysis reveals that proposed is more efficient in tackling unit commitment problem in the presence of wind as renewable energy source.

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Section: Mathematical Formulation Of Hybrid Hho-igwo Algorithmmentioning

confidence: 99%

Optimum generation scheduling incorporating wind energy using HHO–IGWO algorithm

Dhawale

Kamboj

Anand

2023

Journal of Electrical Systems and Inf Technol

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“…Dhawale et al [158] developed an improved chaotic HHO (CHHO). They tested their algorithm using 23 benchmark functions and compared it with SSA, DE, PSO, GWO, MVO, MFO, SCA, CS, TSA, GA, MMA, ACO, and HS.…”

Section: Chaotic Hhomentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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“…For the past few years, many scholars have proved that chaotic maps [ 44 , 45 ] are capable of improving the search process of a population-based metaheuristic algorithm. In general, chaotic maps are usually introduced into one or several processes such as initial population, exploration, or exploitation stage.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

Song

Jia

Abualigah

et al. 2022

Computational Intelligence and Neuroscience

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One of the most popular population-based metaheuristic algorithms is Harris hawks optimization (HHO), which imitates the hunting mechanisms of Harris hawks in nature. Although HHO can obtain optimal solutions for specific problems, it stagnates in local optima solutions. In this paper, an improved Harris hawks optimization named ERHHO is proposed for solving global optimization problems. Firstly, we introduce tent chaotic map in the initialization stage to improve the diversity of the initialization population. Secondly, an exploration factor is proposed to optimize parameters for improving the ability of exploration. Finally, a random walk strategy is proposed to enhance the exploitation capability of HHO further and help search agent jump out the local optimal. Results from systematic experiments conducted on 23 benchmark functions and the CEC2017 test functions demonstrated that the proposed method can provide a more reliable solution than other well-known algorithms.

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An improved Chaotic Harris Hawks Optimizer for solving numerical and engineering optimization problems

Cited by 27 publications

References 125 publications

Optimum generation scheduling incorporating wind energy using HHO–IGWO algorithm

Optimum generation scheduling incorporating wind energy using HHO–IGWO algorithm

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Modified Harris Hawks Optimization Algorithm with Exploration Factor and Random Walk Strategy

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