Can Bio-Inspired Swarm Algorithms Scale to Modern Societal Problems
Darren M. Chitty, Elizabeth Wanner, Rakhi Parmar, Peter R. Lewis

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…
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Taxonomy
TopicsMetaheuristic Optimization Algorithms Research
