A Population Based Approach for ACO
Proceedings of the Applications of Evolutionary Computing on EvoWorkshops 2002: EvoCOP, EvoIASP, EvoSTIM/EvoPLAN
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Applying Population Based ACO to Dynamic Optimization Problems
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Stigmergic optimization in dynamic binary landscapes
Proceedings of the 2007 ACM symposium on Applied computing
The Differential Ant-Stigmergy Algorithm for Large Scale Real-Parameter Optimization
ANTS '08 Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence
A stigmergy-based algorithm for black-box optimization: noiseless function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
A stigmergy-based algorithm for black-box optimization: noisy function testbed
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Composite particle optimization with hyper-reflection scheme in dynamic environments
Applied Soft Computing
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
A multiple local search algorithm for continuous dynamic optimization
Journal of Heuristics
A clustering particle based artificial bee colony algorithm for dynamic environment
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
Dynamic evolutionary membrane algorithm in dynamic environments
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we present a stigmergy-based algorithm for solving optimization problems with continuous variables, labeled Differential Ant-Stigmergy Algorithm (DASA). The DASA is applied to dynamic optimization problems without any modification to the algorithm. The performance of the DASA is evaluated on the set of benchmark problems provided for CEC'2009 Special Session on Evolutionary Computation in Dynamic and Uncertain Environments.