Binary ant algorithm

When facing dynamic optimization problems the goal is no longer to find the extrema, but to track their progression through the space as closely as possible. Over these kind of over changing, complex and ubiquitous real-world problems, the explorativeexploitive subtle counterbalance character of our current state-ofthe-art search algorithms should be biased towards an increased explorative behavior. While counterproductive in classic problems, the main and obvious reason of using it in severe dynamic problems is simple: while we engage ourselves in exploiting the extrema, the extrema moves elsewhere. In order to tackle this subtle compromise, we propose a novel algorithm for optimization in dynamic binary landscapes, stressing the role of negative feedback mechanisms. The Binary Ant Algorithm (BAA) mimics some aspects of social insects’ behavior. Like Ant Colony Optimization (ACO), BAA acts by building pheromone maps over a graph of possible trails representing pseudo-solutions of i...

• Type: Conference
• Year: 2007
• Where: GECCO
• Authors: Carlos Fernandes, Agostinho C. Rosa, Vitorino Ramos
• Keywords: Dynamic Optimization Problems, GECCO 2007, Optimization Problems, Pheromone Reinforcement