Octopus Inspired Optimization (OIO): A Hierarchical Framework for Navigating Protein Fitness Landscapes

TL;DR

The paper introduces Octopus Inspired Optimization (OIO), a hierarchical metaheuristic inspired by octopus neural architecture, effectively navigating complex protein fitness landscapes and outperforming existing algorithms in multiple benchmarks.

Contribution

OIO presents a novel hierarchical framework that unifies global exploration and local exploitation, inspired by octopus neural architecture, for protein landscape optimization.

Findings

OIO outperformed 15 metaheuristics on a protein engineering benchmark.

OIO ranked top on the NK-Landscape benchmark.

OIO achieved first place on the CEC2022 benchmark.

Abstract

Navigating vast, rugged biological fitness landscapes to discover high-value functional patterns-such as optimal protein sequences-is a central challenge in health informatics. However, conventional algorithms often struggle with the exploration-exploitation dilemma, failing to synergize global search with deep local refinement, which leads to entrapment in suboptimal solutions. To overcome this barrier, we introduce Octopus Inspired Optimization (OIO), a novel hierarchical metaheuristic that mimics the octopus's unique neural architecture to intrinsically unify centralized global exploration and parallelized local exploitation. We validated OIO on a real-world protein engineering benchmark, where it surpassed 15 competing metaheuristics. This success is underpinned by OIO's architectural suitability for protein-like landscapes, confirmed by its top ranking on the NK-Landscape benchmark, and its powerful optimization engine, demonstrated by its first-place performance on the gold-standard CEC2022 benchmark. OIO thus provides a robust, nature-inspired computational tool for complex optimization problems in drug discovery and personalized medicine.