Hormesis-Based Optimization (HBO) Algorithm: A Biologically Inspired Computational Approach

Abstract

The Hormesis-Based Optimization (HBO) algorithm is a new method designed to achieve optimization while keeping computational cost and time low. It can be applied to areas such as workload management, resource planning, and task scheduling. This method works by converting the system’s performance parameters into a single value termed as “stress”. It is inspired by the phenomenon of hormesis, where small doses of stress strengthen biological systems, and ensures a dynamic distribution of stress to optimize system performance. The key strength of HBO lies in its ability to quickly and effectively decide the optimal adjustment of this ‘stress’ across various components, which is critical for achieving the best possible performance and can be a NP-hard problem in multi-constrained system. To test this principle, we applied the HBO algorithm to the system of machines, tasked with predictive-reactive dynamic job shop scheduling (DJSS), with the aim of reducing the overall job completion time and latency of the total number of jobs. The results show that the HBO algorithm outperformed not only conventional techniques like Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS), but also their adaptive methods such as Adaptive Genetic Algorithm (AGA), Adaptive Simulated Annealing (ASA), and Adaptive Tabu Search (ATS). Specifically, it improved the total job completion time (makespan) by an average of 4.15% and reduced latency by 4.79%, with time complexity of O(T∙n∙log⁡〖(n))〗, as compared to best performing ATS technique which has time complexity of O(n^2 ) for the worst case.