A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

Liu, Haipeng; Duan, Shaomi; Luo, Huilong

doi:10.1515/mt-2021-2138

Cited by 9 publications

(2 citation statements)

References 76 publications

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“…The algorithm combined with PSO algorithm and used the information sharing mechanism to achieve co-evolution, which increased the diversity of individuals. The study's suggested method was shown to have the best resilience and optimization seeking abilities when compared to other algorithms, according to evaluation results [9]. Abadi MQ H and other scholars proposed the HSSAGA model to address the scheduling problem of medical personnel during the COVID-19 epidemic.…”

Section: Related Workmentioning

confidence: 99%

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Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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In emergency task scheduling, this study proposes a complex model for emergency scheduling. It is based on the particle swarm algorithm and improves upon the traditional version. Additionally, the study recommends the use of the binary particle swarm optimization algorithm (PSO). The study proposes applying the multi-objective task scheduling-particle swarm optimization algorithm (MOTS-PSO) to the complex emergency scheduling model by combining it with the multi-objective function. Compared to other algorithms, the proposed improved algorithm exhibited the lowest average number of iterations, which consistently fell within the range of 130, and achieved a 100% success rate for optimization searches on the majority of functions. When compared with other models, the proposed research model demonstrated superior performance, exhibiting the lowest total scheduling cost, total execution time, and data transfer time of 280 and 900, respectively, for the task quantity of 5000. Furthermore, the proposed model exhibited the lowest maximum execution cost for a single node, which remained within the range of 0.45S. The outcomes of the experiments demonstrate that the proposed research model adequately satisfies the requirements for complex scheduling and its simulability has been confirmed.

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Section: Related Workmentioning

confidence: 99%

“…When the firefly technique is used, it can result in "storage" and assist particles in leaving the local optimal solution and reexamining it. Equation represents the local search position update when the "elite solution" is the cause of the current solution (9).…”

Section: Optimization and Improvement Of Cs Strategiesmentioning

confidence: 99%

Modeling and simulation of complex emergency dispatch based on BIPSO

Sun,

Huang

2024

Int. J. Simul. Multidisci. Des. Optim.

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Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Nurmuhammed,

Akdağ,

Karadağ

2024

Neural Comput & Applic

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Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

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Bayesian system identification and chaotic prediction from data for stochastic Mathieu-van der Pol-Duffing energy harvester

Liu

Ma²

2023

Theoretical and Applied Mechanics Letters

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A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

Cited by 9 publications

References 76 publications

Modeling and simulation of complex emergency dispatch based on BIPSO

Modeling and simulation of complex emergency dispatch based on BIPSO

Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Bayesian system identification and chaotic prediction from data for stochastic Mathieu-van der Pol-Duffing energy harvester

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