Swarm intelligence
Fundamentals of Computational Swarm Intelligence
Fundamentals of Computational Swarm Intelligence
Evolutionary Computation in Dynamic and Uncertain Environments (Studies in Computational Intelligence)
Journal of Global Optimization
The differential ant-stigmergy algorithm applied to dynamic optimization problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A dynamic artificial immune algorithm applied to challenging benchmarking problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Evolutionary programming using mutations based on the Levy probability distribution
IEEE Transactions on Evolutionary Computation
Multiswarms, exclusion, and anti-convergence in dynamic environments
IEEE Transactions on Evolutionary Computation
Dynamic bee colony algorithm based on multi-species co-evolution
Applied Intelligence
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Swarm Intelligence is based on developing metaheuristics that are modeled on certain life-sustaining principles exhibited by the biotic components of the ecosystem. There has been a surge in interest for nature inspired computing for devising more efficient models that can find solution to real-world problems using minimal resources at disposal. In this paper, an enhanced version of Artificial Bee Colony algorithm have been proposed that takes on the task of finding the optimal solution in a continuously changing (dynamic) solution space by incorporating a pool of varied perturbation strategies that operate on a multi-population group and synergizing the strategy pool with a set of diversity-inclusion techniques that help to maintain population diversity.