Improved PSO Performance using LSTM based Inertia Weight Estimation

Abstract

Particle Swarm Optimization (PSO) is first introduced in the year 1995. It is mostly an applied populationbased meta-heuristic optimization algorithm. PSO is diversely used in the areas of sciences, engineering, technology, medicine, and humanities. Particle Swarm Optimization (PSO) is improved its performance by tuning the inertia weight, topology, velocity clamping. Researchers proposed different Inertia Weight based PSO (IWPSO). Every Inertia Weight based PSO in excelling the existing PSOs. A Long Short Term Memory (LSTM) predicting inertia weight based PSO (LSTMIWPSO) is proposed and its performance is compared with constant, random, and linearly decreasing Inertia Weight PSO. Tests are conducted on swarm sizes 50, 75, and 100 with dimensions 10, 15, and 25. The experimental results show that LSTM based IWPSO supersedes the CIWPSO, RIWPSO, and LDIWPSO.

Using Optimized Long Short-Term Memory For Time-Series Forecasting of Electric Vehicles Battery Charging

“…Recent advancements in the Electric Vehicle (EV) charging prediction domain have highlighted diverse methodologies, each shedding light on distinct aspects of this complex problem. [11] introduced a Deep Learning (DL)--based LSTM recurrent neural network predictor model. The unique aspect of this model is the integration of the Empirical Mode Decomposition (EMD) for data decomposition and the Arithmetic Optimization Algorithm (AOA) for parameter tuning.…”