Can Bio-Inspired Swarm Algorithms Scale to Modern Societal Problems

TL;DR

This paper investigates the scalability of bio-inspired swarm algorithms for complex societal problems, demonstrating that reducing decision complexity enhances performance and efficiency in real-world fleet optimization tasks.

Contribution

It introduces an enhanced Partial-ACO method that significantly reduces decision-making, improving scalability and efficiency in complex fleet optimization problems.

Findings

Reducing decision-making by up to 90% improves algorithm effectiveness.

Achieved 40-50% reduction in traversal timings for large fleet problems.

Bio-inspired algorithms may struggle with high-dimensional problems without modifications.

Abstract

Taking inspiration from nature for meta-heuristics has proven popular and relatively successful. Many are inspired by the collective intelligence exhibited by insects, fish and birds. However, there is a question over their scalability to the types of complex problems experienced in the modern world. Natural systems evolved to solve simpler problems effectively, replicating these processes for complex problems may suffer from inefficiencies. Several causal factors can impact scalability; computational complexity, memory requirements or pure problem intractability. Supporting evidence is provided using a case study in Ant Colony Optimisation (ACO) regards tackling increasingly complex real-world fleet optimisation problems. This paper hypothesizes that contrary to common intuition, bio-inspired collective intelligence techniques by their very nature exhibit poor scalability in cases of high dimensionality when large degrees of decision making are required. Facilitating scaling of bio-inspired algorithms necessitates reducing this decision making. To support this hypothesis, an enhanced Partial-ACO technique is presented which effectively reduces ant decision making. Reducing the decision making required by ants by up to 90% results in markedly improved effectiveness and reduced runtimes for increasingly complex fleet optimisation problems. Reductions in traversal timings of 40-50% are achieved for problems with up to 45 vehicles and 437 jobs.