Hyper-Heuristics to customise metaheuristics for continuous optimisation

Cruz–Duarte, Jorge M.; Amaya, Iván; Ortíz-Bayliss, José Carlos; Conant-Pablos, Santiago Enrique; Terashima-Marín, Hugo; Shi, Yong

doi:10.1016/j.swevo.2021.100935

Cited by 47 publications

(22 citation statements)

References 49 publications

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“…Frameworks developed include HyFlex [22], EvoHyp [23] and SHH [24], etc. HyFlex explores a decision space of low-level heuristics or heuristic operators (e.g., taking search operators from ten well-known techniques as building blocks [25]) while EvoHyp adapts evolutionary algorithms as high-level strategies. SHH is specifically built for automatically combining different components of swarm intelligence algorithms [24].…”

Section: A Existing Framework For Automated Algorithm Designmentioning

confidence: 99%

Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework

Jiao

et al. 2023

IEEE Trans. Evol. Computat.

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Section: A Existing Framework For Automated Algorithm Designmentioning

confidence: 99%

Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework

Jiao

et al. 2023

IEEE Trans. Evol. Computat.

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“…Similarly, Abd Elaziz et al developed a hyper-heuristic for enhancing the initial population used by a recent metaheuristic known as the Whale Optimization Algorithm [17]. Conversely, Cruz-Duarte et al have proposed an approach for generating new metaphorless metaheuristics through hyper-heuristics [25,26]. Additionally, Fritsche et al have focused on tackling many-objective problems through HHs [27].…”

Section: Motivation and Significancementioning

confidence: 99%

MatHH: A Matlab-based Hyper-Heuristic framework

2022

Self Cite

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“…Cruz et al [43] provided an approach for customizing population-based metaheuristics based on a heuristic model driven by simulated annealing. The method uses search operators from 10 meta-heuristic strategies as building blocks for new strategies.…”

Section: Related Workmentioning

confidence: 99%

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

Damaševičius

Maskeliūnas

2021

Algorithms

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This paper describes a unique meta-heuristic technique for hybridizing bio-inspired heuristic algorithms. The technique is based on altering the state of agents using a logistic probability function that is dependent on an agent’s fitness rank. An evaluation using two bio-inspired algorithms (bat algorithm (BA) and krill herd (KH)) and 12 optimization problems (cross-in-tray, rotated hyper-ellipsoid (RHE), sphere, sum of squares, sum of different powers, McCormick, Zakharov, Rosenbrock, De Jong No. 5, Easom, Branin, and Styblinski–Tang) is presented. Furthermore, an experimental evaluation of the proposed scheme using the industrial three-bar truss design problem is presented. The experimental results demonstrate that the hybrid scheme outperformed the baseline algorithms (mean rank for the hybrid BA-KH algorithm is 1.279 vs. 1.958 for KH and 2.763 for BA).

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Hyper-Heuristics to customise metaheuristics for continuous optimisation

Cited by 47 publications

References 49 publications

Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework

Automated Design of Metaheuristics Using Reinforcement Learning Within a Novel General Search Framework

MatHH: A Matlab-based Hyper-Heuristic framework

Agent State Flipping Based Hybridization of Heuristic Optimization Algorithms: A Case of Bat Algorithm and Krill Herd Hybrid Algorithm

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