Multimodal optimization using crowding-based differential evolution

By René Thomsen. Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753), 2004

452 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/e44eac9d0d99f54eaf640e82a203ae76581e2fa3

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Citations in the corpus

DEMO: Differential Evolution for Multiobjective Optimization (2005, unknown): “The other two variants were inspired by the concept of Crowding DE, recently introduced by Thomsen in [11].”
A survey on evolutionary computation for complex continuous optimization (2021, background): “Crowding (Thomsen 2004) and speciation (Li et al.”
Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization' (2013, methodology): “To facilitate easy comparisons for participants in the competition, we use as baseline models two Differential Evolution (DE) niching variants, the recently proposed DE/nrand/1/bin algorithm [21] and the well known Crowding DE/rand/1/bin [20].”
A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization (2013, methodology): “Index Terms—Evolutionary computation, multimodal evolutionary optimization algorithm, niching technique, particle swarm optimization (PSO).”
Differential Evolution: A Survey of the State-of-the-Art (2011, background): “The niching techniques include crowding [103], fitness sharing [S203], [S209], clearing [S207], restricted tournament selection [81], [S204], and speciation [S205], [S208].”
Bio-inspired computation: Where we stand and what's next (2019, methodology): “Another bio-inspired solver successfully applied to multimodal optimization is DE, which has been hybridized with fitness sharing [11], speciation and islanding [178].”
Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, methodology): “Thomsen [6] proposed the Crowding DE which limits the competition between nearest (Euclidean distance) members to maintain the diversity.”
Recent advances in differential evolution - An updated survey (2016, background): “[95] proposed an variant of the Crowding DE (CDE) [188], for tracking multiple optima in a dynamic environment.”
A survey of swarm intelligence for dynamic optimization: Algorithms and applications (2017, background): “The two swarms follow the crowding differential evolution (CDE) [201] and PSO models, respectively.”