Bacterial Colony Optimization

Bacterial colony foraging optimization

This paper proposes a novel bacterial colony foraging (BCF) algorithm for complex optimization problems. The proposed BCF extend original bacterial foraging algorithm to adaptive and cooperative mode by combining bacterial chemotaxis, cell-to-cell communication, and a self-adaptive foraging strategy. The cell-to-cell communication enables the historical search experience sharing among the bacte...

An Ant Colony Optimization Classifier System for Bacterial Growth

Internet Electron. J. Mol. Des. 2003, 1, 000–000 Abstract Motivation. Ant colony optimization is one of the most recent nature-inspired metaheuristics. The algorithm mimics cooperative foraging behavior of real life ants, has already exhibited superior performance in solving combinatorial optimization problems. In this work, we have explored the searching capabilities of this metaheuristic for ...

Ant Colony System Optimization

Successful heuristic algorithms for solving combinatorial optimization problems have mimicked processes observed in nature. Two highly successful families of algorithms that do this are simulated annealing and genetic algorithms. Here, a third family of algorithms, ant colony optimization is explored and implemented in C#. The test bed for evaluating the quality of solutions is based on several...

The Ant Colony Optimization

Ant Colony Optimization

Swarm intelligence is a relatively novel approach to problem solving that takes inspiration from the social behaviors of insects and of other animals. In particular, ants have inspired a number of methods and techniques among which the most studied and the most successful one is the ant colony optimization. Ant colony optimization (ACO) algorithm, a novel population-based and meta-heuristic app...