Performance Analysis of Multi-Threaded Distributed Evolutionary Algorithms for Image-Processing Applications
International journal of intelligent engineering and systems · 2023
Evolutionary algorithms (EAs) in the repository of the evolutionary computing (EC) paradigm are simplistic and stochastic in nature.The desirable characteristic of adaptive simplicity has sustained research community's rampant curiosity in EAs.Distributed evolutionary algorithmic (DEA) framework is an instinctive extension of EAs.This paper summarizes the design and implementation of a multi-threaded DEA (MTDEA) framework.Empirical analysis between a traditional DEA framework and a simulated MTDEA is presented.The MTDEA framework was outfitted with three plug-in algorithms to handle commonly occurring faults in distributed environments.The propriety of the MTDEA framework was validated in two customary applications -Image thresholding and Image reproduction.The thresholding and reproduction problems were addressed by incorporation of the differential evolution (DE) and genetic algorithm (GA) methods respectively, into the MTDEA framework.The performances of DE and GA routines were compared with their sequential counterpart frameworks.The MTDEA framework was evaluated for reliability and scalability on a high-performance computing (HPC) system.