2015 IEEE Congress on Evolutionary Computation (CEC) 2015
DOI: 10.1109/cec.2015.7257050
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An adaptive local search algorithm for real-valued dynamic optimization

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Cited by 15 publications
(13 citation statements)
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“…Finally, a recent application of a trajectory-based method to DOPs can be found in [25]. The proposal consists on a local search algorithm called S3 that also keeps the best solutions found so far in a memory archive.…”
Section: Related Workmentioning
confidence: 99%
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“…Finally, a recent application of a trajectory-based method to DOPs can be found in [25]. The proposal consists on a local search algorithm called S3 that also keeps the best solutions found so far in a memory archive.…”
Section: Related Workmentioning
confidence: 99%
“…This fact is illustrated in Figure 5, that displays how the proposed mechanism adjusts the value of δ min along the search process. Copyright: the authors [35] Clustering particle swarm optimizer for locating and tracking multiple optima mCPSO [5] Multiswarm with charged particles as well as exclusion and anti-convergence mechanisms mQSO [5] Multiswarms with quantum particles as well as exclusion and anti-convergence mechanisms SOS+LS [3] Self-Organizing scouts algorithm coupled with local search DynPopDE [12] Differential Evolution for dynamic environments with unknown numbers of optima ESCA [21] Multi-population hybrid between a Particle Swarm Optimization method and a Evolutionary Algorithm CPSOR [19] General framework of multipopulation methods with clustering for undetectable dynamic environments CHPSO [32] Hybrid adaptive collaborative approach based on particle swarm optimization and local search AMSO [20] Adaptive multi-swarm optimizer for dynamic optimization problems MLSDO [17] Multiple local search algorithm for continuous dynamic optimization problems DynS3 [25] Adaptive local search with memory archive for continuous dynamic optimization problems precision of the local search depending on the effort required to track the local optima.…”
Section: Co-published By Atlantis Press and Taylor And Francismentioning
confidence: 99%
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“…It is well known that the performance of a hybrid algorithm is heavily dependent on the setting of control parameters. For that, there is an increasing recent trend to consider adaptive mechanisms to alter operator choices and/or their parameters such as an adpative local search for a continuous dynamic optimisation problems (Riley et al, 2016;Yu et al, 2013;Mavrovouniotis et al, 2015). This latter propose a single-solution-based metaheuristic that perturbs the variables separately in order to select the next search direction.…”
Section: Introductionmentioning
confidence: 99%