Can Bio-Inspired Swarm Algorithms Scale to Modern Societal Problems
This addresses scalability issues in bio-inspired algorithms for researchers and practitioners in optimization, though it is incremental as it builds on existing ACO methods.
The paper tackles the scalability of bio-inspired swarm algorithms like Ant Colony Optimization to complex societal problems, finding that they exhibit poor scalability in high-dimensional scenarios, and presents an enhanced Partial-ACO technique that reduces ant decision making by up to 90%, achieving 40-50% reductions in traversal timings for fleet optimization problems with up to 45 vehicles and 437 jobs.
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.