Analysis of multiobjective evolutionary algorithms on the biobjective traveling salesman problem (1,2)

Lai, Xinsheng; Zhou, Yuejiao

doi:10.1007/s11042-020-09399-z

Cited by 41 publications

(17 citation statements)

References 35 publications

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“…Although SFO, DRM, and FWA explore well, they tend to fall into local optima. To date, multiple improved metaheuristics have been developed to efficiently tackle real-world optimization problems including performance optimization [61], neural network training [62], object tracking [63,64], engineering design problems [65][66][67][68], scheduling problem [69,70], image segmentation [71,72], traveling salesman problem [73], multi-attribute decision making [74][75][76][77], feature selection [78][79][80][81][82], medical data classification [83][84][85][86], bankruptcy prediction [87][88][89], PID optimization control [90][91][92], gate resource allocation [93,94], fault diagnosis of rolling bearings [95,96] and detection of foreign fiber in cotton [97,98].…”

Section: Authors Methods Remarksmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

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In this paper, an efficient sine cosine differential gradient‐based optimization method is proposed for identifying unknown parameters of photovoltaic models. In the simulation, parameter identification is formulated as an objective function to be minimized based on the error between the estimated and experimental data. Based on the original gradient‐based optimization method, the proposed method combines the sine cosine method and the mutation crossover of the differential evolution algorithm. Specifically, the crossover operator enables the algorithm to avoid local optima; and meanwhile, the sine cosine strategy encourages the new individual to calculate the worst position. The simulation results demonstrate that the new optimization method can achieve the minimal root mean square error and obtain better optima relative to other algorithms in different photovoltaic cells. Therefore, the proposed optimization method has great potential to be used for estimating photovoltaic model parameters.

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Section: Authors Methods Remarksmentioning

confidence: 99%

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Chen

Heidari

et al. 2022

IET Renewable Power Gen

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“…Swarm intelligence algorithms are good at solving many optimization problems, such as traveling salesman problems [41], feature selection [42][43][44][45][46], object tracking [47,48], wind speed prediction [49], PID optimization control [50][51][52], image segmentation [53,54], the hard maximum satisfiability problem [55,56], parameter optimization [22,[57][58][59], gate resource allocation [60,61], fault diagnosis of rolling bearings [62,63], the detection of foreign fibers in cotton [64,65], large-scale supply chain network design [66], cloud workflow scheduling [67,68], neural network training [69], airline crew rostering problems [70], and energy vehicle dispatch [71]. This section conducts a qualitative analysis of MSMA.…”

Section: The Qualitative Analysis Of Msmamentioning

confidence: 99%

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

2022

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In this study, the authors aimed to study an effective intelligent method for employment stability prediction in order to provide a reasonable reference for postgraduate employment decision and for policy formulation in related departments. First, this paper introduces an enhanced slime mould algorithm (MSMA) with a multi-population strategy. Moreover, this paper proposes a prediction model based on the modified algorithm and the support vector machine (SVM) algorithm called MSMA-SVM. Among them, the multi-population strategy balances the exploitation and exploration ability of the algorithm and improves the solution accuracy of the algorithm. Additionally, the proposed model enhances the ability to optimize the support vector machine for parameter tuning and for identifying compact feature subsets to obtain more appropriate parameters and feature subsets. Then, the proposed modified slime mould algorithm is compared against various other famous algorithms in experiments on the 30 IEEE CEC2017 benchmark functions. The experimental results indicate that the established modified slime mould algorithm has an observably better performance compared to the algorithms on most functions. Meanwhile, a comparison between the optimal support vector machine model and other several machine learning methods on their ability to predict employment stability was conducted, and the results showed that the suggested the optimal support vector machine model has better classification ability and more stable performance. Therefore, it is possible to infer that the optimal support vector machine model is likely to be an effective tool that can be used to predict employment stability.

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“…As a result, more and more researchers have started introducing swarm intelligence algorithm (SIOA) into the traditional MTIS to improve the segmentation efficiency instead of the traditional exhaustive method. These SIOAs has offered greater efficiency in optimization tasks such as expensive optimization problems [ 32 , 33 ], medical diagnosis [ [34] , [35] , [36] , [37] ], PID optimization control [ [38] , [39] , [40] ], plant disease recognition [ 41 ], feature selection [ [42] , [43] , [44] , [45] ], object tracking [ 46 , 47 ], economic emission dispatch problem [ 48 ], engineering design [ [49] , [50] , [51] ], parameter tuning for machine learning models [ [52] , [53] , [54] ], constrained optimization problems [ 55 , 56 ], combination optimization problems [ 57 ], traveling salesman problem [ 58 ], multi-objective or many optimization problems [ [59] , [60] , [61] ], and scheduling problems [ [62] , [63] , [64] ].…”

Section: Introductionmentioning

confidence: 99%

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

Zhao

Elmannai

et al. 2022

Computers in Biology and Medicine

130

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Analysis of multiobjective evolutionary algorithms on the biobjective traveling salesman problem (1,2)

Cited by 41 publications

References 35 publications

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Parameter identification of photovoltaic models using a sine cosine differential gradient based optimizer

Multi-Population Enhanced Slime Mould Algorithm and with Application to Postgraduate Employment Stability Prediction

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization

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