Common Benchmark Functions for Metaheuristic Evaluation: A Review

Hussain, Kashif; Salleh, Mohd Najib Mohd; Cheng, Shi; Naseem, Rashid

doi:10.30630/joiv.1.4-2.65

Cited by 89 publications

(52 citation statements)

References 21 publications

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“…We benchmarked the described hybrid algorithms on multidimensional test functions for optimization, such as the Sphere [32] function f 1 , the Styblinsky-Tang function [33] f 2 , the Rosenbrock [34] function f 3 and the Rastrigin [35] function f 4 . We performed the tests using 3, 5 and 10 dimensions.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…We performed the tests using 3, 5 and 10 dimensions. Additionally, we considered such two-dimensional test functions, as Ackley [32], Matyas [36], Eggholder [37] and Booth [36] functions, defined in Table 1 as f 5 , f 6 , f 7 and f 8 , respectively. The Michalewicz [32] function f 9 was tested with 2, 5 and 10 dimensions.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…Additionally, we considered such two-dimensional test functions, as Ackley [32], Matyas [36], Eggholder [37] and Booth [36] functions, defined in Table 1 as f 5 , f 6 , f 7 and f 8 , respectively. The Michalewicz [32] function f 9 was tested with 2, 5 and 10 dimensions. The cause of this is that according to [32] the approximate optimum values for the Michalewicz function are well known only for these dimensions.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…The Michalewicz [32] function f 9 was tested with 2, 5 and 10 dimensions. The cause of this is that according to [32] the approximate optimum values for the Michalewicz function are well known only for these dimensions.…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Demidova

Gorchakov

2020

Algorithms

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Inspired by biological systems, swarm intelligence algorithms are widely used to solve multimodal optimization problems. In this study, we consider the hybridization problem of an algorithm based on the collective behavior of fish schools. The algorithm is computationally inexpensive compared to other population-based algorithms. Accuracy of fish school search increases with the increase of predefined iteration count, but this also affects computation time required to find a suboptimal solution. We propose two hybrid approaches, intending to improve the evolutionary-inspired algorithm accuracy by using classical optimization methods, such as gradient descent and Newton’s optimization method. The study shows the effectiveness of the proposed hybrid algorithms, and the strong advantage of the hybrid algorithm based on fish school search and gradient descent. We provide a solution for the linearly inseparable exclusive disjunction problem using the developed algorithm and a perceptron with one hidden layer. To demonstrate the effectiveness of the algorithms, we visualize high dimensional loss surfaces near global extreme points. In addition, we apply the distributed version of the most effective hybrid algorithm to the hyperparameter optimization problem of a neural network.

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Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

Section: Numerical Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Demidova

Gorchakov

2020

Algorithms

View full text Add to dashboard Cite

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“…For all the algorithms, the maximum number of iterations was 1500. The experimental settings including test functions, number of dimensions, and the number of experimental runs are taken from the commonly used settings suggested in [21].…”

Section: A Experimental Settingsmentioning

confidence: 99%

Personal Best Cuckoo Search Algorithm for Global Optimization

Hussain

Salleh

Prasetyo

et al. 2018

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

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Real-life optimization problems demand robust algorithms that perform efficient search in the environment without trapping in local optimal locations. Such algorithms are equipped with balanced exploration and exploitation capabilities. Cuckoo search (CS) algorithm is also one of these optimization algorithms, which is inspired by nature. Despite effective search strategies such as Lévy flights and solution switching approach, CS suffers from a lack of population diversity when implemented in hard optimization problems. In this paper, enhanced local and global search strategies have been proposed in the CS algorithm. The proposed CS variant uses personal best information in solution generation process, hence called Personal Best Cuckoo Search (pBestCS). Moreover, instead of constant value for switching parameter, pBestCS dynamically updates switching parameter. The prior approach enhances local search ability, whereas the later modification enforces effective global search in the algorithm. The experimental results on test suite with different dimensions validated the efficiency of the proposed modification on optimization problems. Based on statistical and convergence analysis, pBestCS outperformed standard CS algorithm, as well as, particle swarm optimization (PSO) and artificial bee colony (ABC).

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Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

Wang,

Chen,

Zhang

et al. 2023

Energy Science & Engineering

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The hydraulic turbine in turbine mode (TMHT) has significant advantages in residual energy recovery, but rapid optimization of its runner parameters has always been a challenge. To address this issue, the optimization algorithm ISMA‐BPNN is proposed based on the improved slime mold algorithm (ISMA) and back propagation neural network (BPNN). First, an efficiency characteristic neural network (ECNN) and a water head characteristic neural network (HCNN), which take the geometric parameters of the runner as input, and the efficiency and water head as outputs, respectively, are constructed by combining the orthogonal test‐based sample data, computational fluid dynamics (CFD), and BPNN. Then, the slime mold algorithm is improved and the optimal runner geometric parameters are obtained based on the ISMA, ECNN, and HCNN, to achieve rapid optimization of the TMHT runner. Finally, the CFD‐based numerical calculation accuracy is verified through real machine tests, and the feasibility of the ISMA‐BPNN‐based rapid optimization strategy for TMHT performance is further verified through CFD numerical simulation.

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Common Benchmark Functions for Metaheuristic Evaluation: A Review

Cited by 89 publications

References 21 publications

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Research and Study of the Hybrid Algorithms Based on the Collective Behavior of Fish Schools and Classical Optimization Methods

Personal Best Cuckoo Search Algorithm for Global Optimization

Optimization of geometric parameters of hydraulic turbine runner in turbine mode based on ISMA and BPNN

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