Adaptive multi-tracker optimization algorithm for global optimization problems: emphasis on applications in chemical engineering

Khosravi, Hooman; Zakeri, Ehsan; Xie, Wenfang; Ahmadi, Bahar

doi:10.1007/s00366-020-01101-z

Cited by 3 publications

(2 citation statements)

References 61 publications

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“…Then, all GTs change their points and find answers in comparison with GOP and they may be replaced if these answers are better compared to GO. After completing the steps of the MTOA algorithm, the first step is the point of the algorithm starting and then the algorithm iteration moves to other stages if the termination condition is not met [38,39]. For further conceptual details of this algorithm, more programmingoriented articles may be referred to [31,39].…”

Section: Multi-tracker Optimization Algorithmmentioning

confidence: 99%

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

2023

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Energy performance analysis in buildings is becoming more and more highlighted, due to the increasing trend of energy consumption in the building sector. Many studies have declared the great potential of soft computing for this analysis. A particular methodology in this sense is employing hybrid machine learning that copes with the drawbacks of single methods. In this work, an optimized version of a popular machine learning model, namely feed-forward neural network (FFNN) is used for simultaneously predicting annual thermal energy demand (ATED) and annual weighted average discomfort degree-hours (WADDH) by analyzing eleven input factors that represent the building circumstances. The optimization task is carried out by a multi-tracker optimization algorithm (MTOA) which is a powerful metaheuristic algorithm. Moreover, three benchmark algorithms including the slime mould algorithm (SMA), seeker optimization algorithm (SOA), and vortex search algorithm (VSA) perform the same task for comparison purposes. The accuracy of the models is assessed using error and correlation indicators. Based on the results, the MTOA (with root mean square errors 2.48 and 5.88, along with Pearson correlation coefficients 0.995 and 0.998 for the ATED and WADHH, respectively) outperformed the benchmark techniques in learning the energy behavior of the building. This algorithm could optimize 100 internal variables of the FFNN and acquire the trend of ATED and WADHH with excellent accuracy. Despite different rankings of the four algorithms in the prediction phase, the MTOA (with root mean square errors 9.84 and 95.96, along with Pearson correlation coefficients 0.972 and 0.997 for the ATED and WADHH, respectively) was still among the best, and altogether, the hybrid of FFNN-MTOA is recommended for promising applications of building energy analysis in real-world projects.

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Section: Multi-tracker Optimization Algorithmmentioning

confidence: 99%

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

2023

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“…Therefore, to adjust the parameters of the MAPF and find the optimal solution of CGTSP a mix-integer optimization algorithm can be employed. To this end, the mixinteger version of multi-tracker optimization algorithms (MTOA) is developed and applied to the problem [49]. Note that MTOA is a population-based optimizer that can find the optimal solution with higher precision and reliability compared to those of the other well-known methods such as genetic algorithm (GA) [50], particle swarm optimization algorithm (PSO) [51], and grey wolf optimizer (GWO) [52].…”

Section: Introductionmentioning

confidence: 99%

Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots

2022

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A hybrid hyper-heuristic whale optimization algorithm for reusable launch vehicle reentry trajectory optimization

Dai

Liu

2021

Aerospace Science and Technology

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Adaptive multi-tracker optimization algorithm for global optimization problems: emphasis on applications in chemical engineering

Cited by 3 publications

References 61 publications

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

A Neural Network Trained by Multi-Tracker Optimization Algorithm Applied to Energy Performance Estimation of Residential Buildings

Optimal Image-Based Task-Sequence/Path Planning and Robust Hybrid Vision/Force Control of Industrial Robots

A hybrid hyper-heuristic whale optimization algorithm for reusable launch vehicle reentry trajectory optimization

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