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The networked evolutionary algorithm: A network science perspective
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Particle Swarm Inspired Evolutionary Algorithm (PS-EA) for Multi-Criteria Optimization Problems
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Computational Intelligence: An Introduction
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Computational Intelligence: An Introduction
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A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm
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Particle swarm optimization
inferredcites →https://doi.org/10.1016/j.amc.2015.06.062openalex-snapshot
Adequate is better: particle swarm optimization with limited-information
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Cluster-based differential evolution with Crowding Archive for niching in dynamic environments
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Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
inferredcites →https://doi.org/10.1038/srep09295openalex-snapshot
Selectively-informed particle swarm optimization
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Introduction to genetic algorithms
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Evolutionary programming made faster
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Proceedings Congress on Evolutionary Computation 2001
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Population structure and particle swarm performance
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Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization
inferredcites →https://doi.org/10.1109/icec.1998.699146openalex-snapshot
A modified particle swarm optimizer
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Self-Organizing Hierarchical Particle Swarm Optimizer With Time-Varying Acceleration Coefficients
inferredcites →https://doi.org/10.1109/tevc.2004.826074openalex-snapshot
The Fully Informed Particle Swarm: Simpler, Maybe Better
inferredcites →https://doi.org/10.1109/tevc.2005.857610openalex-snapshot
Comprehensive learning particle swarm optimizer for global optimization of multimodal functions
inferredcites →https://doi.org/10.1109/tevc.2009.2026270openalex-snapshot
Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology
inferredcites →https://doi.org/10.1109/tevc.2010.2050024openalex-snapshot
Erratum to “Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology” [Feb 10 150-169
inferredcites →https://doi.org/10.1109/tevc.2012.2203138openalex-snapshot
A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization
inferredcites →https://doi.org/10.1109/tsipn.2015.2429471openalex-snapshot
Diffusion and Superposition Distances for Signals Supported on Networks
inferredcites →https://doi.org/10.1109/tsipn.2015.2452811openalex-snapshot
Optimal Forwarding in Opportunistic Delay Tolerant Networks With Meeting Rate Estimations
inferredcites →https://doi.org/10.1109/tsipn.2015.2470110openalex-snapshot
Exploiting Social Trust Assisted Reciprocity (STAR) Toward Utility-Optimal Socially-Aware Crowdsensing
inferredcites →https://doi.org/10.1109/tsmcb.2009.2015956openalex-snapshot
Adaptive Particle Swarm Optimization
inferredcites →https://doi.org/10.1109/tsmcb.2011.2171946openalex-snapshot
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems
inferredcites →https://doi.org/10.1126/science.286.5439.509openalex-snapshot
Emergence of Scaling in Random Networks
inferredcites →https://doi.org/10.1142/2792openalex-snapshot
Evolutionary Computation: Theory and Applications
inferredcites →https://doi.org/10.1155/2014/121782openalex-snapshot
Towards the Novel Reasoning among Particles in PSO by the Use of RDF and SPARQL
inferredcites →https://doi.org/10.1371/journal.pone.0097822openalex-snapshot
Particle Swarm Optimization with Scale-Free Interactions
inferredcites →https://doi.org/10.21105/joss.00613openalex-snapshot
NiaPy: Python microframework for building nature-inspired algorithms
inferredcites →https://doi.org/10.7551/mitpress/1090.001.0001openalex-snapshot
Adaptation in Natural and Artificial Systems
inferredcites →https://doi.org/10.7551/mitpress/3927.001.0001openalex-snapshot
An Introduction to Genetic Algorithms
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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