Amplitude Angle Modulated Bat Algorithm With Application to Zero-One Knapsack Problem

Huang, Xingwang; Li, Peijia; Pu, Yunming

doi:10.1109/access.2019.2901988

Cited by 12 publications

(6 citation statements)

References 27 publications

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“…The search process continues until a stopping criterion is met. The pseudocode for AMBA can be found in [21], and it can be used for A-AMBA and P-AMBA by increasing the random initialization dimension to five and six, respectively.…”

Section: T-methods With Angle Modulated Bat Algorithm Algorithmmentioning

confidence: 99%

“…Realizing the deficiency of the existing generator function in the angle modulated binary Bat algorithm, [21] introduced an amplitude angle modulated version that modified the generator function by incorporating an additional variable coefficient, e, as (12). The new coefficient, e, aims to control the amplitude of the sin wave, likewise of coefficient b towards the amplitude of the cos wave.…”

Section: 𝐴𝑀𝐵𝐴 𝑔(𝑥) = 𝑠𝑖𝑛 (2𝜋(𝑥 𝑖𝑗 − 𝑎) * 𝑏 * 𝑐𝑜𝑠(2𝜋(𝑥 𝑖𝑗 − 𝑎) * 𝑐)) +...mentioning

confidence: 99%

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Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction

et al. 2022

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Analysis of multivariate historical information in predicting future state or unknown outcomes is the core function of Taguchi’s T-method. Introduced by Dr. Genichi Taguchi under Mahalanobis-Taguchi system, the T-method combines regression principle and robust quality engineering element in formulating a predictive model and employs taguchi’s orthogonal array design in optimizing the model through feature or variable selection process. There is a concern regarding the sub-optimality of the T-method prediction accuracy, particularly when the orthogonal array failed to offer a significant number of combinations in search for an optimal subset of features. This is due to the fixed and limited combination offered for evaluation as well as the lack of higher-order interaction of combination. In response to this issue, this paper proposed an angle modulated Bat algorithm to be integrated with the T-method in optimizing the prediction model. A comparison study was conducted using energy efficiency benchmark datasets with the mean absolute error metric used as the performance measure. The results show that the proposed method improved the prediction accuracy by 10.74%, from 6.05 to 5.4, by integrating only four features over the original eight in the prediction model.

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Section: T-methods With Angle Modulated Bat Algorithm Algorithmmentioning

confidence: 99%

Section: 𝐴𝑀𝐵𝐴 𝑔(𝑥) = 𝑠𝑖𝑛 (2𝜋(𝑥 𝑖𝑗 − 𝑎) * 𝑏 * 𝑐𝑜𝑠(2𝜋(𝑥 𝑖𝑗 − 𝑎) * 𝑐)) +...mentioning

confidence: 99%

Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction

et al. 2022

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“…Finally, the proposed GQMBA algorithm might be a good choice to integrate with angle modulation method [35–37] to solve binary optimization problems. We are suggesting angle modulation method because it can change the search space from high dimension to low dimension.…”

Section: Conclusion and Future Research Directionsmentioning

confidence: 99%

Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

Huang

et al. 2019

Computational Intelligence and Neuroscience

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Quantum-behaved bat algorithm with mean best position directed (QMBA) is a novel variant of bat algorithm (BA) with good performance. However, the QMBA algorithm generates all stochastic coefficients with uniform probability distribution, which can only provide a relatively small search range, so it still faces a certain degree of premature convergence. In order to help bats escape from the local optimum, this article proposes a novel Gaussian quantum bat algorithm with mean best position directed (GQMBA), which applies Gaussian probability distribution to generate random number sequences. Applying Gaussian distribution instead of uniform distribution to generate random coefficients in GQMBA is an effective technique to promote the performance in avoiding premature convergence. In this article, the combination of QMBA and Gaussian probability distribution is applied to solve the numerical function optimization problem. Nineteen benchmark functions are employed and compared with other algorithms to evaluate the accuracy and performance of GQMBA. The experimental results show that, in most cases, the proposed GQMBA algorithm can provide better search performance.

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“…These complex features make it difficult for traditional mathematical programming methods such as conjugate gradient, sequential quadratic programming, Newton's method, and quasi-Newton's method to find optimum [3]. Meta-heuristic algorithms (MAs) have become prevalent in many applied disciplines in recent decades because of higher performance and lower required computing capacity and time than deterministic algorithms in various optimization problems [4][5][6][7][8][9]. As a branch of random optimization, meta-heuristic algorithms can find a near-optimal solution by using available resources, although it is not always guaranteed to find the global optimum.…”

Section: Introductionmentioning

confidence: 99%

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

Dong

Zou

et al. 2022

Preprint

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This paper proposes a novel hybrid optimization algorithm named GPOFWA, which integrates political optimizer (PO) with fireworks algorithm (FWA) to solve numerical and engineering optimization problems. The original PO uses subgroup optimal solutions such as party leaders and constituency winners to guide the movement of the search agent. However, the number of such subgroup optimal solutions is limited, which leads to insufficient global exploration capabilities of PO. In addition, the recent past-based position updating strategy (RPPUS) of PO lacks effective verification of the updated candidate solutions, which reduces the convergence speed of the algorithm. The proposed hybrid algorithm uses the spark explosion mechanism in FWA to perform explosion spark and Gauss explosion spark operations on the subgroup optimal solutions (party leader and constituency winner) respectively based on the greedy strategy, which optimizes the subgroup optimal solution and enhances the exploitative ability of the algorithm. Moreover, Gaussian explosion sparks are also used to correct the candidate solutions after RPPUS, which makes up for the shortcomings of the original PO. In addition, a new subgroup optimal solution called the Converged Mobile Center (CMC) based on two-way consideration is designed to guide the movement of search agents. We test the presented hybrid algorithm on 30 well-known benchmark test functions and three engineering optimization problems. The experimental results show that GPOFWA is superior to nine state-of-the-art methods in terms of the quality of the resulting solution.

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Amplitude Angle Modulated Bat Algorithm With Application to Zero-One Knapsack Problem

Cited by 12 publications

References 27 publications

Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction

Enhanced Taguchi’s T-method using angle modulated Bat algorithm for prediction

Gaussian Quantum Bat Algorithm with Direction of Mean Best Position for Numerical Function Optimization

GPOFWA: A hybrid greedy political optimizer with fireworks algorithm for numerical and engineering optimization problems

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