How to solve it: modern heuristics
How to solve it: modern heuristics
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
An artificial immune network for multimodal function optimization on dynamic environments
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An immune memory clonal algorithm for numerical and combinatorial optimization
Frontiers of Computer Science in China
Composite particle optimization with hyper-reflection scheme in dynamic environments
Applied Soft Computing
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
Information Sciences: an International Journal
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In many real-world scenarios, in contrast to standard benchmark optimization problems, we may face some uncertainties regarding the objective function. One source of these uncertainties is a constantly changing environment in which the optima change their location over time. New heuristics or adaptations to already available algorithms must be conceived in order to deal with such problems. Among the desirable features that a search strategy should exhibit to deal with dynamic optimization are diversity maintenance, a memory of past solutions, and a multipopulation structure of candidate solutions. In this paper, an immune-inspired algorithm that presents these features, called dopt-aiNet, is properly adapted to deal with six newly proposed benchmark instances, and the obtained results are outlined according to the available specifications for the competition at the Congress on Evolutionary Computation 2009.