Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Swarm intelligence
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
The Ant System Applied to the Quadratic Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Applying Population Based ACO to Dynamic Optimization Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Tracking Extrema in Dynamic Environments
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
Ant colony optimization theory: a survey
Theoretical Computer Science
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Introducing a binary ant colony optimization
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
UMDAs for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A self-organized criticality mutation operator for dynamic optimization problems
Proceedings of the 10th annual conference on Genetic and evolutionary computation
An ant-based rule for UMDA's update strategy
ECAL'09 Proceedings of the 10th European conference on Advances in artificial life: Darwin meets von Neumann - Volume Part II
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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 explorative-exploitive subtle counterbalance character of our current state-of-the-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 increasing quality to a specific optimization problem. Main differences rely on the way this search space is represented and provided to the colony in order to explore/exploit it, while and more important, we enrol in providing strong evaporation to the problem-habitat. By a process of pheromone reinforcement and evaporation the artificial insect's trails over the graph converge to regions near the ideal solution of the optimization problem. Over each generation, positive feedbacks made available by pheromone reinforcement consolidate the best solutions found so far, while enhanced negative feedbacks given by the evaporation mechanism provided the system with population diversity and fast self-adaptive characteristics, allowing BAA to be particularly suitable for severe complex dynamic optimization problems. Experiments made with some well known test functions frequently used in the Evolutionary Algorithms' research field illustrate the efficiency of the proposed method. BAA was also compared with other algorithms, proving to be more able to track fast moving extrema on several test problems.