An intensify atom search optimization for engineering design problems

Sun, Pu; Liu, Hao; Zhang, Yong; Tu, Liangping; Meng, Qingyao

doi:10.1016/j.apm.2020.07.052

Cited by 39 publications

(5 citation statements)

References 59 publications

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“…The findings demonstrate that ASO outperforms both traditional and novel algorithms and which can be accounted as an alternative approach for solving various engineering problems [33]. ASO has also been applied in electronic engineering with the objective of improving ASO's ability by combining it with other algorithms in a hybrid method [34][35][36][37]. However, this method is not widely used, but it is another one that quickly and efficiently converges on an answer that can be used to find reservoir rule curves.…”

Section: Introductionmentioning

confidence: 94%

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

et al. 2022

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This research aims to apply optimization techniques using atom search optimization (ASO), genetic programming (GP), and wind-driven optimization (WDO) with a reservoir simulation model for searching optimal rule curves of a multi-reservoir system, using the objective function with the minimum average quantity of release excess water. The multi-reservoir system consisted of five reservoirs managed by a single reservoir that caused severe problems in Sakon Nakhon province, Thailand, which was hit by floods in 2017. These included Huai Nam Bo Reservoir, the Upper Huai Sai-1 Reservoir, the Upper Huai Sai-2 Reservoir, the Upper Huai Sai-3 Reservoir, and the Huai Sai Khamin Reservoir. In this study, the monthly reservoir rule curves, the average monthly inflow to the reservoirs during 2005–2020, the water demand of the reservoirs, hydrological data, and physical data of the reservoirs were considered. In addition, the performance of the newly obtained rule curves was evaluated by comparing the operation with a single reservoir and the operation with a multi-reservoir network. The results showed situations of water shortage and water in terms of frequency, duration, average water, and maximum water. The newly obtained rule curves from the multi-reservoir system case showed an average water excess of 43.722 MCM/year, which was less than the optimal curves from the single reservoir case, where the average water excess was 45.562 MCM/year. An analysis of the downstream reservoir of the multi-reservoir system, which diverts water from the upstream reservoirs, was performed. The results showed that the new optimal rule curves of ASO, GP, and WDO operated as a multi-reservoir system performed better than when operated as a single reservoir. Therefore, this research is suitable for sustainable water management without construction.

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Section: Introductionmentioning

confidence: 94%

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

et al. 2022

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“…This problem is more complex (Sun and othersg, 2021), involving seven design variables: the face width ( 1 ), module of teeth ( 2 ), number of teeth in the pinion ( 3 ), length of the first shaft between bearings 1 ( 4 ), length of the second shaft between bearings 2 ( 5 ), diameter of first shaft 1 ( 6 ), and diameter of second shaft 2 ( 7 ). The goal is to minimize the total weight of the reducer.…”

Section: Speed Reducer Design Problemmentioning

confidence: 99%

A meta-inspired termite queen algorithm for global optimization and engineering design problems

Chen

Zhou

Zhang

et al. 2022

Engineering Applications of Artificial Intelligence

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“…Additionally, GA and its variants have been employed in production scheduling [22,23], allocation optimization [24], search and localization tasks [25,26], and neural network optimization [27][28][29]. Furthermore, other metaheuristic algorithms, such as ASO, GWO, WOA, HHO, and their variations, have also been widely applied to address complex optimization problems in fields such as battery modeling [30,31], feature selection [1,[32][33][34][35][36][37] and path optimization [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

Liu,

Guo,

Liu

et al. 2023

PLoS ONE

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This paper proposes a novel hybrid algorithm, named Multi-Strategy Hybrid Harris Hawks Tunicate Swarm Optimization Algorithm (MSHHOTSA). The primary objective of MSHHOTSA is to address the limitations of the tunicate swarm algorithm, which include slow optimization speed, low accuracy, and premature convergence when dealing with complex problems. Firstly, inspired by the idea of the neighborhood and thermal distribution map, the hyperbolic tangent domain is introduced to modify the position of new tunicate individuals, which can not only effectively enhance the convergence performance of the algorithm but also ensure that the data generated between the unknown parameters and the old parameters have a similar distribution. Secondly, the nonlinear convergence factor is constructed to replace the original random factor c1 to coordinate the algorithm’s local exploitation and global exploration performance, which effectively improves the ability of the algorithm to escape extreme values and fast convergence. Finally, the swarm update mechanism of the HHO algorithm is introduced into the position update of the TSA algorithm, which further balances the local exploitation and global exploration performance of the MSHHOTSA. The proposed algorithm was evaluated on eight standard benchmark functions, CEC2019 benchmark functions, four engineering design problems, and a PID parameter optimization problem. It was compared with seven recently proposed metaheuristic algorithms, including HHO and TSA. The results were analyzed and discussed using statistical indicators such as mean, standard deviation, Wilcoxon’s rank sum test, and average running time. Experimental results demonstrate that the improved algorithm (MSHHOTSA) exhibits higher local convergence, global exploration, robustness, and universality than BOA, GWO, MVO, HHO, TSA, ASO, and WOA algorithms under the same experimental conditions.

show abstract

An intensify atom search optimization for engineering design problems

Cited by 39 publications

References 59 publications

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization

A meta-inspired termite queen algorithm for global optimization and engineering design problems

MSHHOTSA: A variant of tunicate swarm algorithm combining multi-strategy mechanism and hybrid Harris optimization

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