Swarms in dynamic environments
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
IEEE Transactions on Evolutionary Computation
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
IEEE Transactions on Evolutionary Computation
Adaptive swarm optimization for locating and tracking multiple targets
Applied Soft Computing
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This paper mainly concentrates on the problem of tracking multiple targets in the noisy environment. To better recognize the eccentric target in a specific environment, one proposed objective function gets the target's shape in the subgraph. Inspired by particle swarm optimization, the proposed algorithm of tracking multiple targets adaptively modifies the covered radii of each subgroup in terms of the minimum distances among the subgroups, and successfully tracks the conflicting targets. The theoretic results as well as the experiments on tracking multiple ants indicate that this efficient method has successfully been applied to the complex and changing practical systems.