Information acquisition optimizer: a new efficient algorithm for solving numerical and constrained engineering optimization problems

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 80(18), P. 25736 - 25791

Published: Aug. 14, 2024

Language: Английский

Design and research of heat dissipation system of electric vehicle lithium-ion battery pack based on artificial intelligence optimization algorithm

Energy Informatics, Journal Year: 2024, Volume and Issue: 7(1)

Published: June 27, 2024

Abstract This research focuses on the design of heat dissipation system for lithium-ion battery packs electric vehicles, and adopts artificial intelligence optimization algorithm to improve efficiency system. By integrating genetic algorithms particle swarm optimization, goal is optimize key parameters cooling temperature control extend life. In process implementation, improves diversity population through crossover mutation operations, thus enhancing global search ability. Particle (PSO) local accuracy convergence speed by dynamically adjusting inertia weight learning factor. The effects different schemes performance were systematically evaluated using computational fluid dynamics (CFD) software. experimental results show that significantly improved after application algorithm, especially in aspects distribution uniformity maximum reduction. also successfully shortens thermal response time adaptability stability under working conditions. complexity execution these are analyzed, which proves feasibility practical applications. study demonstrates practicability effectiveness pack provides valuable reference guidance progress technology vehicles future.

Language: Английский

An improved Harris hawks optimizer with enhanced logarithmic spiral and dynamic factor and its application for predicting molten iron temperature in the blast furnace

Engineering Reports, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 1, 2024

Abstract In response to the problem of poor search performance and difficulty in escaping from local optimum Harris hawks optimizer, an improved optimizer with enhanced logarithmic spiral dynamic factor (IHHO‐ELSDF) is proposed this paper. The mechanism adopted exploration phase, its main feature use opposite‐learning hybrid for more promising regions. used replace energy improve global capability algorithm, it can better balance exploitation. addition, a random distribution strategy exploitation phase avoid falling into optimum. Based on 23 classical test functions, influence probability, three mechanisms, exploration–exploitation ratio IHHO‐ELSDF are analyzed. Subsequently, subjected comparative analysis 17 algorithms IEEE CEC2022 benchmark suite. These tests show that outperforms most competitors numerical optimization. Furthermore, assess applicability real‐world problems, employed optimize parameters wavelet neural network molten iron temperature prediction. simulation results based real production data prediction model achieves high precision , .

Language: Английский