A Boosted Particle Swarm Method for Energy Efficiency Optimization of PRO Systems

Abstract

The analytical solutions of complex dynamic PRO systems pose challenges to ensuring that maximum power can be harvested in stable, rapid, and efficient ways in response to varying operational environments. In this paper, a boosted particle swarm optimization (BPSO) method with enhanced essential coefficients is proposed to enhance the exploration and exploitation stages in the optimization process. Moreover, several state-of-the-art techniques are utilized to evaluate the proposed BPSO of scaled-up PRO systems. The competitive results revealed that the proposed method improves power density by up to 88.9% in comparison with other algorithms, proving its ability to provide superior performance with complex and computationally intensive derivative problems. The analysis and comparison of the popular and recent metaheuristic methods in this study could provide a reference for the targeted selection method for different applications.

A mixed-integer linear optimization problem for highway emergency vehicle routing

“…It uses a stochastic search that accepts worse solutions with a decreasing probability as the search progresses, allowing the algorithm to escape local optima. Particle Swarm Optimization [34][35][36][37][38][39] algorithm is inspired by the social behavior of bird flocks or fish schools. It uses a population of particles that move through the search space and adapt their positions based on their own best experiences and the best experiences of their neighbors.…”