Improved Electro Search Algorithm with Intelligent Controller Control System: ESPID Algorithm

Abstract

Studies have established that hybrid models outperform single models. The particle swarm algorithm (PSO)-based PID (proportional-integral-derivative) controller control system is used in this study to determine the parameters that directly impact the speed and performance of the Electro Search (ESO) algorithm to obtain the global optimum point. ESPID algorithm was created by integrating this system with the ESO algorithm. The improved ESPID algorithm has been applied to 7 multi-modal benchmark test functions. The acquired results were compared to those derived using the ESO, PSO, Atom Search Optimization (ASO), and Vector Space Model (VSM) algorithms. As a consequence, it was determined that the ESPID algorithm’s mean score was superior in all functions. Additionally, while comparing the mean duration value and standard deviations, it is observed that it is faster than the ESO algorithm and produces more accurate results than other algorithms. ESPID algorithm has been used for the least cost problem in the production of pressure vessels, which is one of the real-life problems. Statistical results were compared with ESO, Genetic algorithm and ASO. ESPID was found to be superior to other methods with the least production cost value of 5885.452.