Computational Chemotaxis in Ants and Bacteria over Dynamic Environments

Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know De Jong test suite. Later, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.

💡 Research Summary

The paper investigates how chemotaxis‑inspired stigmergic mechanisms can be harnessed for dynamic optimization by comparing two swarm‑based algorithms: a Swarm Search Algorithm (SSA) modeled on ant colony foraging, and an Artificial Bacterial Foraging Algorithm (BFOA) that mimics bacterial chemotaxis. Both algorithms rely on indirect communication—ants via pheromone trails, bacteria via nutrient gradients—but differ in how they update the shared medium and adapt to environmental changes. SSA alternates between exploration and exploitation phases, depositing pheromone proportional to the fitness of visited points; pheromone evaporates over time, allowing the colony to forget outdated information quickly. BFOA follows a four‑stage cycle (chemotaxis, transition, reproduction, elimination) where each bacterium performs a biased random walk toward higher nutrient concentration, reproduces the fittest, and discards the weakest. To evaluate performance, the authors use the six classic De Jong benchmark functions (F1–F6) and introduce dynamic variants where the optimum moves or the landscape reshapes during the run. Experimental conditions (population size, number of generations, function‑evaluation budget) are kept identical for both algorithms to ensure a fair comparison. Results show that SSA consistently converges faster, tracks moving optima more accurately, and maintains higher solution quality under abrupt changes. In scenarios where the swarm must simultaneously pursue contradictory objectives (e.g., minimizing one function while maximizing another), SSA’s pheromone redistribution enables rapid re‑orientation, whereas BFOA exhibits lag due to its reliance on individual gradient following and slower population turnover. The study highlights that simple local rules—pheromone deposition, evaporation, and random exploration—are sufficient for generating sophisticated global behavior, confirming the robustness of stigmergic coordination for severe dynamic optimization problems. The authors conclude by suggesting extensions such as hybrid ant‑bacterial models, multi‑objective handling, and real‑time applications in robotics or sensor networks, positioning SSA as a promising framework for future adaptive swarm intelligence research.