Ants can effectively forage on one-way trails, overcoming the disruption of orientation cues and navigating through unidirectional traffic systems.

Ants Can Learn to Forage on One-Way Trails

Ants form complex transportation networks, using pheromones and sight to navigate to food sources and return to their nests, with some species absconding in the trails to search for food.

Diversity, distribution and secrets of ant transportation networks

Ant Leptothorax albipennis colony emigrates to the best available new site using a quorum requirement, relying on decentralized interactions of poorly informed insects to achieve adaptive behaviors.

Quorum sensing, recruitment, and collective decision-making during colony emigration by the ant Leptothorax albipennis

Diacamma indicum ants have the highest tandem running speeds and path efficiency in colony relocation, with their ability to optimize short paths even without chemical trails.

Marching with ants to a new nest

Desert ants can find their way home even if dropped, proving their ability to navigate with a brain the size of a sesame seed.

How lost “passenger” ants find their way home

Colony emigration in the ant Aphaenogaster senilis involves a synchronized relocation of workers, with some differences in worker relocation methods.

Dynamics of colony emigration in the ant Aphaenogaster senilis

The ant colony optimization methodology outperforms other methods in terms of solution quality, evaluation time, and processing time for optimal clustering of N objects into K clusters.

An ant colony approach for clustering

The wood ant Formica polyctena Först. has survived in an old nuclear weapons bunker for years, despite low temperatures and scanty food, due to the unique accumulation of workers in a gigantic pitfall.

Living beyond the limits of survival: wood ants trapped in a gigantic pitfall

The Ant Colony Optimization (ACO) algorithm effectively solves various discrete optimization problems by mimicking real ants' shortest-path finding abilities and using pheromone trails for communication.

Ant Colony Optimization

Ant colony optimization (ACO) is a successful swarm intelligence technique that uses pheromones to guide other ants towards a favorable path, with numerous successful applications.

The ant colony method is a metaheuristic that is inspired by ant behavior, which shows that ants can collectively find optimal solutions for their development and survival, with a biomass similar to that of humans.

The Ant Colony Method

The Ant Colony Optimization (ACO) Algorithm effectively optimized the solid waste collection route at the University of Port Harcourt, reducing distance by about 16% cumulatively.

Application of Ant Colony Optimisation Algorithm on Solid Waste Collection: A Case of University of Port Harcourt

Ant transporters improve their performance by reducing time spent at the old and new nests during repeated emigrations, but not adult transport.

The behaviour of ant transporters at the old and new nests during successive colony emigrations

Ant colony optimization (ACO) is a successful swarm intelligence technique that uses pheromones to guide other ants towards a favorable path, with applications in various fields.

Ant colony optimization

Smaller ant colonies are more dependent on a few key individuals for transporting items during colony emigrations, suggesting that colony organization and division of labor should consider individual work amounts.

Why do not all workers work? Colony size and workload during emigrations in the ant Temnothorax albipennis

Ant colonies with a single queen strategically move their queen during the middle of emigrations, minimizing risk to her and acting as group-level adaptive units.

Queen transport during ant colony emigration: a group-level adaptive behavior

Ant colony optimization, inspired by real ant colonies, can effectively solve discrete, continuous, and telecommunications problems, with potential for hybridization with AI and operations research techniques.

Ant colony optimization: Introduction and recent trends

Ant Colony Optimization" is an algorithm that mimics real ant abilities to solve complex problems, demonstrating better performance than other optimization heuristics.

ANT COLONY OPTIMIZATION

Multi colony ant algorithms can be easily parallelized by placing the colonies on different processors, compared to single colony ant algorithms, for solving optimization problems like the Traveling Salesperson problem and Quadratic Assignment problem.

Multi Colony Ant Algorithms

Ant Colony Optimization (ACO) has advanced significantly since its initial proposal, with high-performing variants, a generic framework, and successful applications on various computationally hard problems.

Ant Colony Optimization: Overview and Recent Advances

How do ants find their way back to the colony?

These studies suggest ants find their way back to the colony using pheromones, sight, and efficient path optimization, even overcoming disrupted orientation cues and unidirectional traffic systems.