Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

Zhang, Wenqiang; Hou, Wenlin; Li, Chen; Yang, Weidong; Gen, Mitsuo

doi:10.23919/csms.2021.0017

Cited by 37 publications

(18 citation statements)

References 35 publications

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“…Due to its strong optimization capability, the developed MSMA can also be applied to other optimization problems, such as multi-objective or many optimization problems [75][76][77], big data optimization problems [78], and combination optimization problems [79]. Moreover, it can be applied to tackle the practical problems such as medical diagnosis [80][81][82][83], location-based service [84,85], service ecosystem [86], communication system conversion [87][88][89], kayak cycle phase segmentation [90], image dehazing and retrieval [91,92], information retrieval service [93][94][95], multi-view learning [96], human motion capture [97], green supplier selection [98], scheduling [99][100][101], and microgrid planning [102] problems.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%

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

2022

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“…MAs, inspired by nature, are algorithms that are based on population and probability [27][28][29][30][31][32][33][34][35][36][37]. A wide range of MAs have been used to many applications such as expensive optimization problems [38,39], multi-objective or many optimization problems [40][41][42][43], gate resource allocation [44,45], wind speed prediction [46], and scheduling problems [47]. They can also effectively solve unknown parameter of solar cells, including but not limited to, genetic algorithm (GA) [48], particle swarm optimizer (PSO) [49], differential evolution (DE) [50], grey wolf optimization (GWO) [51], Harris hawk optimizer (HHO) [12,52], slime mould algorithm (SMA) [53], cat swarm optimization (CSO) [54], sunflower optimization algorithm (SFO) [55], multi-verse optimizer (MVO) [56], demand response algorithm (DRA) [57], ant lion optimizer (ALO) [58], and firework algorithm (FWA) [59].…”

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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“…In the meantime, this method enabled them to well estimate the effects of different government policies on the production of environmentally friendly products [20] . What is more, Du et al [21] explored the incentive efficiency and its evolution process under different compensation structures through computational experiment. In production scheduling, Zhang et al [22] improved the efficiency of solving complex multiobjective mnifsp by establishing multi-objective particle swarm optimization algorithm.…”

Section: Relevant Studies Of Computational Experimentsmentioning

confidence: 99%

An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation

Lin

et al. 2022

Complex Syst. Model. Simul.

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Decision making is one of the common human activities. But in complex, interactive, and dynamic systems, it is extremely important to make decisions scientifically because the influence of the behavior after decision making is generally irreversible. The predictability of behavior influence is an effective way to improve the scientific decision making. As a new branch of computing, computational experiment is an emerging management method for research on complex systems. In this paper, based on particle swarm intelligence, an evolutionary adaptive system model of brand innovation in the toy industry cluster is constructed. By imitating the evolution process of the complex adaptive system, this method is helpful to analyze the impact of the management behavior brought to simulation system, predict the management behavior in real world, and finally choose the best management strategy. This simulation tried to figure out the affection of government regulation strategies and provide corresponding assessments and recommendations, which gives a new solution to assist the government to effectively judge the influence of the macro policy, as well as provides a new way of thinking of the related intelligent decision making.

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Multidirection Update-Based Multiobjective Particle Swarm Optimization for Mixed No-Idle Flow-Shop Scheduling Problem

Cited by 37 publications

References 35 publications

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

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

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

An Evolutionary Adaptive System for Prediction of Strategy Influence: A Case Study of Government Regulation Guided Brand Innovation

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