Optimization of biomimetic heliostat field using heuristic optimization algorithms

Rizvi, Arslan A.; Yang, Dong; Khan, Talha Ahmed

doi:10.1016/j.knosys.2022.110048

Cited by 13 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Building upon existing biomimetic spiral and Campo radial-staggered layouts, Belaid et al [26] employed the Grasshopper Optimization Algorithm (GOA) to evaluate the efficiency of heliostat fields. Rizvi et al [38] consolidated various advanced heuristic algorithms, including Particle Swarm Optimization, Genetic Algorithm, Whale Optimization Algorithm, and Gravity Algorithm, to accomplish the optimization process for biomimetic heliostat field design. Meanwhile, Arrif et al [39] conducted a comparative analysis of eight metaheuristic algorithms approach to optimize the radial-staggered heliostat field at the PS10 plant, and a hybrid GA-GOA algorithm was proposed.…”

Section: B Heliostat Field Layout Optimization Methodologymentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

View full text Add to dashboard Cite

Confronted with the challenges posed by climate change and the ongoing energy transition, solar energy is one of the important new energy sources, and the tower solar power plant has become an innovative solution to promote clean energy development. The optimization of heliostat field layout constitutes a crucial aspect in enhancing the operational efficiency of a concentrated solar power tower plant. Currently, the optimization of heliostat field layout has garnered widespread attention. In this paper, we propose the swarm optimization algorithm with niching and elite competition called NECSO to solve the large-scale heliostat field layout optimization. First, aiming to increase diversity and heterogeneity within the population, we employ a random grouping strategy to partition the population into distinct sub-swarms. Then, we design a niching and elite competition mechanism to harmonize the performance of the exploration. The niching competition is carried out within any sub-swarm to enhance the explorability of particles. The elite competition occurs between the elites which select from each sub-swarm to improve the convergence of particles. Additionally, we develop a mathematical model for the optimization of heliostat field layout. This model employs currently advanced computational methods, facilitating prompt and precise calculation of the optical efficiency in the heliostat field layout. To evaluate the performance of NECSO, we design 15 practical cases of heliostat field layout with varying scales. And then, we conduct comparative experiments with eight currently mainstream and excellent algorithms. The results indicate that NECSO exhibits competitive performance in solving the heliostat field layout optimization, particularly in large-scale cases.

show abstract

Section: B Heliostat Field Layout Optimization Methodologymentioning

confidence: 99%

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Zou,

Zhou,

2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Another method for generating a heliostat field is the Fermat spiral configuration [ 34 , 35 ]. It is based on the natural growth of sunflower heads, also known as "capitula" ( Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Steel structures are preferred for tower heights less than 120 m; however, concrete structures are made for larger towers. According to the research conducted by Sterns Rogers engineering company [ 34 ], tower heights between 50 and 300 m are economical. Therefore, the same range is taken as a constraint for optimization in this study.…”

Section: Optimization Of Field Layoutmentioning

confidence: 99%

Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant

Haris,

Rehman,

Iqbal

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…The main purpose of this optimization is that the individuals with the worst fitness in𝑋 𝑜 learn [19] from the individual with the best fitness in𝑋 𝜐 , while the best individual in𝑋 𝑜 learns from the worst individual in𝑋 𝜐 , and so on, as a way to expand the variability between the self-learning individuals and the sample individuals. According to the differences between the two, the individual position in𝑋 𝑜 is updated, and the update formula is:…”

Section: Wireless Sensor Network Initializationmentioning

confidence: 99%

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

SONGHE,

2024

ijacsa

View full text Add to dashboard Cite

To improve the positioning accuracy of wireless sensor nodes and ensure the target tracking effect, a wireless sensor node positioning and target tracking method based on an improved locust algorithm is proposed. The DV Hop algorithm is used to calculate the minimum hops and average hops distance between the unknown node and each anchor node to obtain the location of the unknown node, realize the rough positioning of wireless sensor nodes, and analyze the positioning error to determine the positioning accuracy target function; The improved locust algorithm is used to solve the positioning accuracy objective function to obtain the sensor node positioning results with the minimum error; The target tracking model and the target is calculated. According to the target observation information obtained by all sensor nodes, the target state in the wireless sensor network model is tracked using the probability hypothesis density filtering algorithm. The test results show that the algorithm has better performance, the spatial evaluation index results are all lower than 0.020, and the individual distribution in the solution set is better; The location of each unknown node in different node distribution states can be obtained; The positioning error under the surface and plane is less than 0.012; The maximum error of target tracking is 0.142m; It can track single target and multiple targets.

show abstract

Optimization of biomimetic heliostat field using heuristic optimization algorithms

Cited by 13 publications

References 34 publications

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Heliostat Field Layout via Niching and Elite Competition Swarm Optimization

Genetic algorithm optimization of heliostat field layout for the design of a central receiver solar thermal power plant

A Study on Wireless Sensor Node Localization and Target Tracking Based on Improved Locust Algorithm

Contact Info

Product

Resources

About