A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

Pereira, Jorge Costa; Mendes, Jérôme; Júnior, Jorge Sampaio Silveira; Viegas, Carlos; Paulo, João

doi:10.3390/math10030300

Cited by 37 publications

(12 citation statements)

References 40 publications

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“…Optimal values for these parameters must be judiciously adjusted, considering the specific characteristics of the population. Genetic algorithms involve a dynamic process, and parameter tuning is essential for achieving optimal results [55,56].…”

Section: Evolution Parametersmentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

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Photovoltaic (PV) systems face challenges in achieving maximum energy extraction due to the non-linear nature of their current versus voltage (IxV) characteristics, which are influenced by temperature and solar irradiation. These factors lead to variations in power generation. The situation becomes even more complex under partial shading conditions, causing distortion in the characteristic curve and creating discrepancies between local and global maximum power points. Achieving the highest output is crucial to enhancing energy efficiency in such systems. However, conventional maximum power point tracking (MPPT) techniques often struggle to locate the global maximum point required to extract the maximum power from the PV system. This study employs genetic algorithms (GAs) to address this issue. The system can efficiently search for the global maximum point using genetic algorithms, maximizing power extraction from the PV arrangements. The proposed approach is compared with the traditional Perturb and Observe (P&O) method through simulations, demonstrating its superior effectiveness in achieving optimal power generation.

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Section: Evolution Parametersmentioning

confidence: 99%

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

de Oliveira,

Brandt,

Salvadori

et al. 2024

Inventions

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show abstract

“…It should be noted that the choice of using GA and CW to compose the optimization model to obtain better routes was because both algorithms have shown good results for routing problems and other related combinatorial optimization problems. Nevertheless, the use of heuristics to populate the initial GA population with feasible solutions has proven to be a good alternative for solving routing problems [34,[41][42][43]. In addition, we tested other metaheuristics such as Simulated Annealing-SA [44][45][46] and Tabu Search-TS [45,46], but the results obtained in preliminary experiments do not indicate improvements in using them.…”

Section: Proposed Ai-based Approach For Economic and Environmental As...mentioning

confidence: 99%

Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations

Oliveira Neto,

de Araujo,

Gomes

et al. 2023

Sensors

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The objective of this study was to apply simulation and genetic algorithms for the economic and environmental optimization of the reverse network (manufacturers, waste managers, and recyclers in Sao Paulo, Brazil) of waste from electrical and electronic equipment (WEEE) to promote the circular economy. For the economic evaluation, the reduction in fuel, drivers, insurance, depreciation, maintenance, and charges was considered. For the environmental evaluation, the impact of abiotic, biotic, water, land, air, and greenhouse gases was measured. It was concluded that the optimized structure of the WEEE reverse chains for Sao Paulo, Brazil provided a reduction in the number of collections, thus making the most of cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Therefore, the proposed approach is replicable in organizational practice, which is mainly required to meet the 2030 agenda of reducing the carbon footprint generated by transport in large cities. Thus, this study can guide companies in structuring the reverse WEEE chains in Sao Paulo, Brazil, and other states and countries for economic and environmental optimization, which is an aspect of great relevance considering the exponential generation of WEEE.

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“…It selects individuals with high fitness degrees as the operational objectives in the next iteration. The population is evolved/transformed during several generations in order to obtain a final population that contains individuals with the best possible quality for the problem at hand [42].…”

Section: Ga and Pso Algorithmmentioning

confidence: 99%

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

Tao

Wen

et al. 2022

Electronics

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A wall surface inspection robot mainly relies on the inertial measurement unit and global positioning system (GPS) signal during intelligent wall surface inspection. The robot may encounter incorrect positioning under a GPS-denied environment, easily triggering safety accidents. In order to obtain a path suitable for the safe work of the robot wall surface inspection robot in a GPS-denied environment, a global path-planning method for wall surface inspection robots was proposed based on the improved generic algorithm (GA). The influencing factor for GPS signal strength was introduced into the heuristic function in path planning for GA to address the aforementioned problem. Using the PSO algorithm, GA was initialized and the influencing term of GPS signal was introduced into the fitness degree function so as to achieve point-to-point path planning of vertical wall surface inspection robot. Path angle and probability of intersection and variation was taken into account for better path planning capability. Finally, the simulation experiments were performed. The generated path using the improved GA was found to avoid the blind area of the GPS signal. The algorithm proposed has a good performance with average convergence times of 35.9 times and an angle of 55.88° in simple environment. Contrary to the traditional GA and PSO algorithm, the method showed advantages in terms of the convergence rate, path quality, path angle change, and algorithm stability. The research presented in this article is meaningful and relatively sufficient. The simulation test is also quite convincing. The proposed method was proved to be effective in global path planning for a wall surface inspection robot.

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A Review of Genetic Algorithm Approaches for Wildfire Spread Prediction Calibration

Cited by 37 publications

References 40 publications

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Development of an MPPT-Based Genetic Algorithm for Photovoltaic Systems versus Classical MPPT Techniques in Scenarios with Partial Shading

Simulation of Electronic Waste Reverse Chains for the Sao Paulo Circular Economy: An Artificial Intelligence-Based Approach for Economic and Environmental Optimizations

A Path-Planning Method for Wall Surface Inspection Robot Based on Improved Genetic Algorithm

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