An Advanced Crow Search Algorithm for Solving Global Optimization Problem

Lee, Donwoo; Kim, Jeonghyun; Shon, Sudeok; Lee, Seungjae

doi:10.3390/app13116628

Cited by 6 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To further improve the hybrid algorithm, additional investigations are necessary to enhance the desired population distribution through hybridization. One possible direction is to apply adaptive CSA parameters (fl and AP), which will affect the exploitation and exploration performance of the algorithm [32][33][34].…”

Section: Numerical Resultsmentioning

confidence: 99%

“…Finally, to improve CSA's performance, some authors have applied dynamic algorithm parameters. In [33], the authors proposed dynamic AP and fl, whose values change at each iteration of the algorithm. These dynamic parameters improved the exploitation and exploration performance of the algorithm compared to a set of competing metaheuristic algorithms, not only in the case of both unimodal and multimodal BTFs but also for five real engineering problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

View full text Add to dashboard Cite

Cultivation process (CP) modeling and optimization are ambitious tasks due to the nonlinear nature of the models and interdependent parameters. The identification procedures for such models are challenging. Metaheuristic algorithms exhibit promising performance for such complex problems since a near-optimal solution can be found in an acceptable time. The present research explores a new hybrid metaheuristic algorithm built upon the good exploration of the genetic algorithm (GA) and the exploitation of the crow search algorithm (CSA). The efficiency of the proposed GA-CSA hybrid is studied with the model parameter identification procedure of the E. coli BL21(DE3)pPhyt109 fed-batch cultivation process. The results are compared with those of the pure GA and pure CSA applied to the same problem. A comparison with two deterministic algorithms, i.e., sequential quadratic programming (SQP) and the Quasi-Newton (Q-N) method, is also provided. A more accurate model is obtained by the GA-CSA hybrid with fewer computational resources. Although SQP and Q-N find a solution for a smaller number of function evaluations, the resulting models are not as accurate as the models generated by the three metaheuristic algorithms. The InterCriteria analysis, a mathematical approach to revealing certain relations between given criteria, and a series of statistical tests are employed to prove that there is a statistically significant difference between the results of the three stochastic algorithms. The obtained mathematical models are then successfully verified with a different set of experimental data, in which, again, the closest one is the GA-CSA model. The GA-CSA hybrid proposed in this paper is proven to be successful in the collaborative hybridization of GA and CSA with outstanding performance.

show abstract

Section: Numerical Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Roeva,

Zoteva

2023

Fermentation

View full text Add to dashboard Cite

show abstract

“…Table 2 shows the benchmark function used to compare the convergence performance according to parameter changes and the number of qubits used in each benchmark function [39,40]. Here, Min is the minimum value of the function, and t max is the maximum number of generations.…”

Section: Characteristics Of the Qbhs Algorithmmentioning

confidence: 99%

Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm

Lee,

Ha,

Shon

et al. 2023

Buildings

Self Cite

View full text Add to dashboard Cite

Recently, a new field that combines metaheuristic algorithms and quantum computing has been created and is being applied to optimization problems in various fields. However, the application of quantum computing-based metaheuristic algorithms to the optimization of structural engineering is insufficient. Therefore, in this paper, we tried to optimize the weight of the truss structure using the QbHS (quantum-based harmony search) algorithm, which combines quantum computing and conventional HS (harmony search) algorithms. First, the convergence performance according to the parameter change of the QbHS algorithm was compared. The parameters selected for the comparison of convergence performance are QHMS, QHMCR, QPAR, ϵ, and θr. The selected parameters were compared using six benchmark functions, and the range for deriving the optimal convergence performance was found. In addition, weight optimization was performed by applying it to a truss structure with a discrete cross-sectional area. The QbHS algorithm derived a lower weight than the QEA (quantum-inspired evolutionary algorithm) and confirmed that the convergence performance was better. A new algorithm that combines quantum computing and metaheuristic algorithms is required for application to various engineering problems, and this effort is essential for the expansion of future algorithm development.

show abstract

“…Metaheuristics algorithms, which are classified into four categories (Evolutionary, Swarm, Physics, and Human behavior) for imitated natural phenomena, are creating new fields that solve optimization problems by combining them with qubit characteristics [ 10 , 11 , 12 ]. The first case of combination with an evolutionary-based algorithm is the quantum-inspired genetic algorithm (QGA), proposed by Han and Kim in 2000 [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

“…Quantum computers are characterized by being able to express various information at the same time, so they have the expectation that the operation speed is faster than existing computers (supercomputers) [9]. Metaheuristics algorithms, which are classified into four categories (Evolutionary, Swarm, Physics, and Human behavior) for imitated natural phenomena, are creating new fields that solve optimization problems by combining them with qubit characteristics [10][11][12]. The first case of combination with an evolutionary-based algorithm is the quantum-inspired genetic algorithm (QGA), proposed by Han and Kim in 2000 [13].…”

Section: Introductionmentioning

confidence: 99%

Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis

Lee,

Shon

2023

Biomimetics

Self Cite

View full text Add to dashboard Cite

Since the mention of the Fourth Industrial Revolution in 2016, quantum computers and quantum computing (QC) have emerged as key technologies. Many researchers are trying to realize quantum computers and quantum computing. In particular, most of the development and application of metaheuristics algorithms using quantum computing is focused on computer engineering fields. Cases in which the developed algorithm is applied to the optimal design of a building or the optimal design results presented by expanding the algorithm in various directions are very insufficient. Therefore, in this paper, we proposed four methods of adopting qubits to perform pitch adjusting in the optimization process of the QbHS (quantum-based harmony search) algorithm and applied it to TTO (truss topology optimization) using four methods to compare the results. The four methods of adopting qubits have the same or decreased number of qubits adopted as the number of iterations changes. As a result of applying TTO using four methods, convergence performance differed depending on the adoption method, and convergence performance was superior to conventional HS (harmony search) algorithms in all methods. The optimal design of structural engineering using such QC is expected to contribute to the revitalization of future technologies in the architectural field and the field of computer information systems.

show abstract

An Advanced Crow Search Algorithm for Solving Global Optimization Problem

Cited by 6 publications

References 33 publications

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Model Identification of E. coli Cultivation Process Applying Hybrid Crow Search Algorithm

Weight Optimization of Discrete Truss Structures Using Quantum-Based HS Algorithm

Qubit Adoption Method of a Quantum Computing-Based Metaheuristics Algorithm for Truss Structures Analysis

Contact Info

Product

Resources

About