A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

Zou, Juan; Li, Qingya; Yang, Shengxiang; Zheng, Jinhua; Zhou, Peng; Pei, Tingrui

doi:10.1016/j.swevo.2018.03.010

Cited by 39 publications

(8 citation statements)

References 41 publications

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“…e reason for the use of evolutionary algorithms is the large number of features and background factors in uencing the large and complex state of arrhythmia detection, which start with a random population that is a set of available solutions to the problem. During the optimization process and at each stage of the algorithm implementation, the optimal solutions are selected to be transferred to the next stage or in other words to be transferred to the next generation, which ultimately leads to the optimal answer to the problem [13][14][15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Al-Safi

Munilla

Rahebi

2022

Computational Intelligence and Neuroscience

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Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.

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Section: Introductionmentioning

confidence: 99%

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Al-Safi

Munilla

Rahebi

2022

Computational Intelligence and Neuroscience

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“…• Through the experimental comparisons, we ascertain which kind of strategy suits which type of DCOPs. Inspired by [48], we will hybridize CPSO, LTFR-DSPSO, and DyCODE based on their contributions to design more excellent and stable algorithms in the future.…”

Section: Resultsmentioning

confidence: 99%

Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

Wang

Yang

et al. 2019

Swarm and Evolutionary Computation

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Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled.

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“…Some studies recognise that knowledge of past environments and search experience can help address new environments [123,176]. In [176], a dynamic environmental evolutionary model was built, which records information about environments and search experience of population before and after a change. The recorded information and experience are then used to guide the search in new environments.…”

Section: Memory-based Approachesmentioning

confidence: 99%

Evolutionary Dynamic Multi-objective Optimisation: A Survey

et al. 2022

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Evolutionary dynamic multi-objective optimisation (EDMO) is a relatively young but rapidly growing area of investigation. EDMO employs evolutionary approaches to handle multi-objective optimisation problems that have time-varying changes in objective functions, constraints and/or environmental parameters. Due to the simultaneous presence of dynamics and multi-objectivity in problems, the optimisation difficulty for EDMO has a marked increase compared to that for single-objective or stationary optimisation. After nearly two decades of community effort, EDMO has achieved significant advancements on various topics, including theoretic research and applications. This paper presents a broad survey and taxonomy of existing research on EDMO. Multiple research opportunities are highlighted to further promote the development of the EDMO research field.

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A dynamic multiobjective evolutionary algorithm based on a dynamic evolutionary environment model

Cited by 39 publications

References 41 publications

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning

Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

Evolutionary Dynamic Multi-objective Optimisation: A Survey

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