Integrating a dimensional perturbation module into exponential distribution optimizer for solving optimization problems

Abstract

Exponential distribution optimizer (EDO) is a recently proposed optimization technique that is based on the exponential probability distribution model. As most swarm intelligence algorithms (SIAs), EDO is very good at handling common optimization problems. However, when addressing multimodal and center-bias problems, it exhibits limitations like imbalanced global and local search capabilities and poor solution accuracy. Building upon this, we introduce an improved version of exponential distribution optimizer that incorporates a dimensional perturbation module (DPM) called EDO_DPM. By testing the impact of dimensional perturbation, the K-dimensional evolution strategy is adopted in our approach. Concurrently, to modulate the usage frequency of DPM, an adjustment parameter, named evolutionary state factor (ESF), is designed contingent on the population’s evolutionary state. Moreover, to alleviate the deficiency of non full-dimensional perturbation, a bounce out operation is embedded in the algorithm. The efficacy of EDO_DPM has been substantiated through testing on 20 different types of functions and CEC2017 benchmark suite. Comparative analyses with the state-of-the-art algorithms have demonstrated a marked enhancement in EDO_DPM’s capability to manage multimodal problems and its proficiency in resolving center-bias problems. Meanwhile, it can achieve excellent results on CEC2017 by comparing excellent improved variants. Furthermore, EDO_DPM is applied to the problem of parameter estimation for photovoltaic (PV) generation systems. Comparison results show that EDO_DPM has good application capability.