Optimal Stochastic Process Optimizer: A New Metaheuristic Algorithm With Adaptive Exploration-Exploitation Property

Xu, Jiahong; Xu, Lihong

doi:10.1109/access.2021.3101939

Cited by 12 publications

(5 citation statements)

References 60 publications

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“…The optimization of the outer-level problem, with respect to u H 1 in Equation ( 9), was realized by metaheuristic algorithms, such as the OSPO algorithm [38]; the optimization of the inner-level problem, with respect to u H 2 in Equation ( 9), was realized by deterministic algorithms, such as the sequential quadratic programming [39] embedded in the fmincon algorithm in MATLAB. The optimization of Equation ( 8) was also realized by the fmincon algorithm in MATLAB.…”

Section: Resultsmentioning

confidence: 99%

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

Lai

Guo

et al. 2022

Agronomy

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Path-tracking control algorithms in agriculture typically focus on how to improve the trajectory-tracking performance of autonomous agricultural machinery, and the agricultural productivity is optimized in a two-layer way. The upper operational layer optimizes an optimal tracking trajectory with the best agricultural productivity, and the lower control layer—such as Nonlinear Model Predictive Control (NMPC)—receives this optimized tracking trajectory first, and then steers the vehicle to track this trajectory with high accuracy. However, this two-layer structure cannot improve the agricultural productivity at the control layer online, which makes the agricultural operation sub-optimal. In this paper, we focus on agricultural machinery operational efficiency, to represent agricultural productivity; in order to realize optimizing control to further improve agricultural machinery operational efficiency, a new path-tracking control algorithm, named Efficiency-oriented Model Predictive Control (EfiMPC), is proposed. EfiMPC is intrinsically a nested structure, which can consider the global performance of the whole system defined in the operational layer—like the agricultural machinery operational efficiency considered in this paper—in the control layer online; thus, the agricultural machinery operational efficiency can be improved during the farming operation. An unreachable tracking point, denoted as the pseudo-point, has been proposed, to indicate the agricultural machinery operational efficiency objective in a receding horizon fashion; EfiMPC can utilize this pseudo-point to realize the optimizing control online. A simulation case study was used to test the superiority of the proposed EfiMPC algorithm, and the results show that, compared with the traditional NMPC algorithm, the agricultural machinery operational efficiency realized by EfiMPC was improved by 8.56%; thus, the effectiveness of the EfiMPC has been demonstrated.

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Section: Resultsmentioning

confidence: 99%

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

Lai

Guo

et al. 2022

Agronomy

Self Cite

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“…( 2013 ) 348 Optics Inspired Optimization (OIO) Kashan ( 2015 ) 349 Optimal Foraging Algorithm (OFA) Sayed et al. ( 2019a ) 350 Optimal Stochastic Process Optimizer (OSPO) Xu and Xu ( 2021 ) 351 Orca Optimization Algorithm (OOA) Golilarz et al. ( 2020 ) 352 Orca Predation Algorithm (OPA) Jiang et al.…”

Section: Metaheuristicsmentioning

confidence: 99%

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

2023

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As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called ‘novel’ if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on ‘novel ideas’, so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

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“…For example, when dealing with multi-extremum problems, they can escape local optima and find global optima. Moreover, their optimization results are not dependent on initial conditions, exhibiting strong robustness and universality [20], [21], [22]. As a result, they have been widely applied in fields such as optimization scheduling problems and engineering design.…”

Section: Introductionmentioning

confidence: 99%

Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

Luo,

Chen,

Sun

et al. 2024

IEEE Access

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The strip exit thickness is an important indicator for measuring the production quality of steel rolling products. Controlling its accuracy is crucial in producing high-quality rolling products in the steel industry. This study proposed an improved marine predators algorithm (IMPA) optimized bidirectional gated recurrent unit (BiGRU) for predicting the strip exit thickness. Firstly, an adaptive weight factor was introduced into the marine predators algorithm (MPA) to overcome the drawbacks of slow early convergence speed and limited search range of the algorithm, addressing the issue of local optimization. Secondly, an average fitness strategy was developed to enhance the quality of the algorithm population, improve optimization accuracy, and strengthen algorithm stability. In addition, IMPA was used to optimize parameters, reducing the impact of hyper-parameters such as the number of hidden layer neurons, learning rate, and batch size on the prediction accuracy of BiGRU, thereby establishing IMPA-BiGRU model. The experiments were conducted on an actual rolling process dataset. The experimental results showed that IMPA outperformed traditional MPA, particle swarm optimization (PSO), grey wolf optimization (GWO), whale optimization algorithm (WOA), and recent variants of MPA. The IMPA-BiGRU also demonstrated excellent prediction capabilities for strip exit thickness, meeting the practical production requirements for high-quality rolling products.INDEX TERMS BiGRU, parameter optimization, feature selection, marine predators algorithm, heuristic algorithm, strip thickness.

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Optimal Stochastic Process Optimizer: A New Metaheuristic Algorithm With Adaptive Exploration-Exploitation Property

Cited by 12 publications

References 60 publications

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

Efficiency-Oriented MPC Algorithm for Path Tracking in Autonomous Agricultural Machinery

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

Improved Marine Predators Algorithm Optimized BiGRU for Strip Exit Thickness Prediction

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