A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

Sayed, Gehad Ismail; Darwish, Ashraf; Hassanien, Aboul Ella

doi:10.1080/0952813x.2018.1430858

Cited by 67 publications

(14 citation statements)

References 48 publications

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“…Studies have shown the promising performance of MVO when compared with other metaheuristic algorithms, and hence it is a candidate for the optimization problem in this article. 53,60,61 SBO is a biology-based optimization algorithm whose inspiration was drawn from the phenomenon of male bowerbirds' capability of attracting the female for breeding. 62 Finally, we present the LCBO optimization as a candidate in the category of life-based algorithms.…”

Section: Metaheuristic Algorithms and Related Workmentioning

confidence: 99%

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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Convolutional neural networks (CNN) are deep learning models widely reported as performing well in several image classification tasks. Training the networks for optimal performance is considered an NP-hard problem. The combination of a metaheuristic algorithm with CNN has been proposed to address this problem because the metaheuristic algorithms have efficient performance, parameter tuning, and stagnation prevention methods. Moreover, there is now an increasing need to apply the best performing metaheuristic algorithm to optimize the parameters, training and learning rate of the CNN models. This study aims to investigate the best performing metaheuristic algorithm for fine-tuning the weights, biases, and hyperparameters of CNN networks for solving the problem of characterization of abnormalities in breast images. Furthermore, hybrid models consisting of a CNN architecture and five representative metaheuristic algorithms to efficiently detect breast cancer abnormalities are presented. The adopted approach involves training a CNN network using genetic algorithm (GA), whale optimization algorithm (WOA), multiverse optimizer (MVO), satin bower optimization (SBO), and life choice-based optimization (LCBO) algorithms to optimize only weights and bias of the model. Two categories of experiments were carried out: the first involved training of the CNN model, while the second involved optimizing the training of CNN model with metaheuristic algorithms. A comparative analysis of the impact of the proposed GA, WOA, MVO, SBO, and LCBO metaheuristic algorithms on the performance of the CNN architecture was carried out. Empirical and statistical analyses are presented to validate further the findings obtained in the study. Results obtained showed that MVO, SBO and LCBO outperformed GA, WOA. The classification accuracy obtained for CNN-GA, CNN-WOA, CNN-MVO, CNN-SBO, and CNN-LCBO at the fifth epoch was 0.76, 0.75, 0.84, 0.80, and 0.86, respectively. At the same time, the traditional CNN achieved an accuracy of 0.66. The outcome of this study showed that contrary to widespread practice, physics-based, biology-based, and human-based optimization algorithms promise better performance parameter tuning for CNN as compared with evolutionary-based and swarm-based metaheuristic algorithms.

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Section: Metaheuristic Algorithms and Related Workmentioning

confidence: 99%

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Oyelade

Ezugwu

2021

Concurrency and Computation

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“…A combination of these characteristic properties of chaotic maps entails a significant improvement in the optimization performance of metaheuristic algorithms [52]. Some literature studies reported an enhanced solution diversity and quick avoidance of the local optimum points on the search space by utilizing the merits of the chaotic systems [53]. A typical chaotic system with M dimension can be expressed by the following equation Table 1 reports the mathematical formulations of the ten best performing chaotic maps where x t (y t ) denotes the t th number of member x (y) in the chaotic sequence and t represents the current index of the chaotic variable in the sequence.…”

Section: Chaotic Mapsmentioning

confidence: 99%

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

Turgut

2020

SN Appl. Sci.

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This research study aims to introduce chaos theory into the Manta Ray Foraging Optimization (MRFO) Algorithm and optimize a real-world design problem through the chaos-enhanced versions of this method. Manta Ray Foraging Optimization algorithm is a bio-inspired swarm intelligence-based metaheuristic algorithm simulating the distinctive food search behaviors of the manta rays. However, MRFO suffers from some intrinsic algorithmic inefficiencies such as slow and premature convergence and unexpected entrapment to the local optimum points in the search domain like most of the metaheuristic algorithms in the literature. Recently, random numbers generated by chaos theory have been incorporated into the metaheuristic algorithms to solve these problems. More than twenty chaotic maps are applied to the base algorithm and ten best performing methods are considered for performance evaluation on high-dimensional optimization test problems. Forty test problems comprising unimodal and multimodal functions have been solved by chaotic variants of MRFO and extensive statistical analysis is performed. Furthermore, thermo-economic design optimization of an air-fin cooler is maintained by the chaotic MRFO variants to assess their optimization capabilities over complex engineering design problems. Ten decisive design variables of an air fin cooler are optimized in terms of total annual cost rates and optimum solutions obtained by five best chaotic MRFO algorithms are compared to the preliminary design. A significant improvement is observed in the objective function values when MRFO with chaotic operators is applied to this considered thermal design problem.

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“…Alatas et al introduced the chaotic particle swarm optimization algorithm [26]. Sayed et al introduced the chaotic multiverse optimization algorithm [27]. e chaosbased whale optimization algorithm is introduced by using chaotic systems in whale optimization processes [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Memristive Hyperjerk Chaotic System: Amplitude Control, FPGA Design, and Prediction with Artificial Neural Network

Wang

Çiçek

et al. 2021

Complexity

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An amplitude controllable hyperjerk system is constructed for chaos producing by introducing a nonlinear factor of memristor. In this case, the amplitude control is realized from a single coefficient in the memristor. The hyperjerk system has a line of equilibria and also shows extreme multistability indicated by the initial value-associated bifurcation diagram. FPGA-based circuit realization is also given for physical verification. Finally, the proposed memristive hyperjerk system is successfully predicted with artificial neural networks for AI based engineering applications.

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A new chaotic multi-verse optimization algorithm for solving engineering optimization problems

Cited by 67 publications

References 48 publications

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

Characterization of abnormalities in breast cancer images using nature‐inspired metaheuristic optimized convolutional neural networks model

A novel chaotic manta-ray foraging optimization algorithm for thermo-economic design optimization of an air-fin cooler

A Memristive Hyperjerk Chaotic System: Amplitude Control, FPGA Design, and Prediction with Artificial Neural Network

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