A Gravitational Search Algorithm With Chaotic Neural Oscillators

Wang, Yirui; Gao, Shangce; Yu, Yang; Wang, Ziqian; Cheng, Jiujun; Todo, Yuki

doi:10.1109/access.2020.2971505

Cited by 35 publications

(15 citation statements)

References 69 publications

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“…Particle Swarm Optimization (PSO) [14], Bat Algorithm (BA) [15], Grey Wolf Optimizer (GWO) [11] until the recent algorithms such as Firefly Algorithm (FFA) [16], Ant Lion Optimization (ALO) [12], Whale Optimization Algorithm (WOA) [13] and Monarch Butterfly Optimization (MBO) [17]. PBA employs some rules of physics to perform optimization such as Gravitational Search Algorithm (GSA) [18], Charged System Search (CSS) [19] until the recent algorithms, i.e., Slime Mould Algorithm (SMA) [20]. Meanwhile, PBa shares some features regardless of their nature such as Stochastic Fractal Search (SFS) [21], SCA [22], and Harris Hawks Optimization (HHO) [23].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

Rosli¹,

Rahim²,

Rani³

et al. 2022

CMC

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The essential purpose of radar is to detect a target of interest and provide information concerning the target's location, motion, size, and other parameters. The knowledge about the pulse trains' properties shows that a class of signals is mainly well suited to digital processing of increasing practical importance. A low autocorrelation binary sequence (LABS) is a complex combinatorial problem. The main problems of LABS are low Merit Factor (MF) and shorter length sequences. Besides, the maximum possible MF equals 12.3248 as infinity length is unable to be achieved. Therefore, this study implemented two techniques to propose a new metaheuristic algorithm based on Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA) using Inverse Filtering (IF) and clipping method to achieve better results. The proposed algorithms, LABS-IF and HMSCACSA-IF, achieved better results with two large MFs equal to 12.12 and 12.6678 for lengths 231 and 237, respectively, where the optimal solutions belong to the skew-symmetric sequences. The MF outperformed up to 24.335% and 2.708% against the state-of-the-art LABS heuristic algorithm, xLastovka, and Golay, respectively. These results indicated that the proposed algorithm's simulation had quality solutions in terms of fast convergence curve with better optimal means, and standard deviation.

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Section: Introductionmentioning

confidence: 99%

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

Rosli¹,

Rahim²,

Rani³

et al. 2022

CMC

View full text Add to dashboard Cite

show abstract

“…The outcomes achieved surpassed findings achieved using GSA (Genetic Search Algorithm) and PSO (Particle Swarm Optimization). A study by [26], utilized chaotic neural oscillators to update GSA-CNO, a gravitational constant. Experiments revealed that chaotic neural oscillators successfully adjusted the gravitational constant, allowing GSA-CNO to act well and maintain its reliability against four gravitational search algorithms variants on functions.…”

Section: Purpose Of the Investigationmentioning

confidence: 99%

Hybrid Local-Global Optimum Search Using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) for Waveguide Selection

et al. 2021

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Multiple beam combination in optical interferometry with concurrent measurement of intricate visibilities, around each possible baseline, is a trending research area. In this work, a hybrid method is proposed for three different waveguide arrays and several waveguides are excited simultaneously in each array. Each waveguide array acts as a beam combiner, the output of which determines the field intensity of each waveguide mode. The output intensity depends on the waveguide selected for excitation. Thus, waveguide selection is the major factor that can affect the output intensity. The main goal of this research is to provide an effective solution for the selection of waveguides, to provide high visibility and intensity at the output of the multi-beam combiner. In addition to this, the use of metaheuristic optimization algorithms to solve the problem of waveguide selection is proposed. To accomplish this, firstly, an analytical study has been conducted to analyze the performance of optimization algorithms, including PSO, FA and GSA, and then the results of these algorithms have been compared with the conventional approaches. And finally, a model of Hybrid Local-Global Optimum Search Algorithm using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) has been developed for waveguide selection. The performance of the proposed hybrid model is examined in MATLAB simulation software. The simulated outcomes are determined for PSO, FA, and GSA-based models, as well as, for the proposed hybrid model, in terms of normalized intensity, visibility, and min-max 1/SNR values. The results obtained from simulation show that the PSO and GSAbased models are giving better results, followed by FA and conventional approaches. This worked as a motivation behind using PSO and GSA together in the proposed system, resulting in higher intensity and visibility values. Thus, the proposed hybrid model is concluded to be more efficient and convenient, for selecting optimum waveguides from the array, to attain an optimum output.

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“…In [70], the piecewise liner chaotic map (PWLCM) is implemented to perform a CLS and then incorporated into PSO. CLS has already been widely employed in gravitational search algorithm [71]- [73], artificial bee colony optimization [74], brain storm optimization [75], differential evolution [76], salp swarm algorithm [77], and many others [62], [78]- [82]. It is worth pointing out that these previous algorithms only use a single chaotic map to perform CLS, while most recently several searches have noticed that multiple chaotic maps might perform better as they can simultaneously use different search dynamics.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

Yang

et al. 2021

IEEE Access

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As a meta-heuristic algorithm that simulates the intelligence of gray wolves, grey wolf optimizer (GWO) has a wide range of applications in practical problems. As a kind of local search, chaotic local search (CLS) has a strong ability to get rid of the local optimum due to its integration of chaotic maps. To enhance GWO, CLS is always incorporated into GWO to increase its population diversity and accelerate algorithm's convergence. However, it is still unclear that how may chaotic maps should be used in CLS and how to embed them into GWO. To address these challenging issues, this paper studies both single and multiple chaotic maps incorporated GWOs. Extensive comparative experiments are conducted based on IEEE Congress on Evolutionary Computation (CEC) benchmark test suit. The results show that CLS incorporated GWOs generally perform better than the original GWO, suggesting the effectiveness of such hybridization. Moreover, a remarkable finding of this work is that the piecewise linear chaotic map (PWLCM) and Gaussian map have the most potential to improve the search performance of GWO. Additionally, CLS incorporated GWOs also perform significantly better than some other state-of-the-art meta-heuristic algorithms. This study not only gives more insights into the mechanism of how CLS makes influence on GWO, but also finds that the most suitable choice of chaotic map for it.

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A Gravitational Search Algorithm With Chaotic Neural Oscillators

Cited by 35 publications

References 69 publications

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

A Hybrid Modified Sine Cosine Algorithm Using Inverse Filtering and Clipping Methods for Low Autocorrelation Binary Sequences

Hybrid Local-Global Optimum Search Using Particle Swarm Gravitation Search Algorithm (HLGOS-PSGSA) for Waveguide Selection

Comparative Study on Single and Multiple Chaotic Maps Incorporated Grey Wolf Optimization Algorithms

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