The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Multiswarms, exclusion, and anti-convergence in dynamic environments
Abstract
Many real-world problems are dynamic, requiring an optimization algorithm which is able to continuously track a changing optimum over time. In this paper, we explore new variants of particle swarm optimization (PSO) specifically designed to work well in dynamic environments. The main idea is to split the population of particles into a set of interacting swarms. These swarms interact locally by an exclusion parameter and globally through a new anti-convergence operator. In addition, each swarm maintains diversity either by using charged or quantum particles. This paper derives guidelines for setting the involved parameters and evaluates the multiswarm algorithms on a variety of instances of the multimodal dynamic moving peaks benchmark. Results are also compared with other PSO and evolutionary algorithm approaches from the literature, showing that the new multiswarm optimizer significantly outperforms previous approaches
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Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
The particle swarm - explosion, stability, and convergence in a multidimensional complex space
Clerc, M., & Kennedy, J. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.985692 · Cited by 9,017
A Cooperative Approach to Particle Swarm Optimization
vandenBergh, F., & Engelbrecht, A. P. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826069 · Cited by 2,120
Population structure and particle swarm performance
Kennedy, J., & Mendes, R. (2003) · Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600) · doi:10.1109/cec.2002.1004493 · Cited by 1,586
Evolutionary Optimization in Uncertain Environments—A Survey
Jin, Y., & Branke, J. (2005) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2005.846356 · Cited by 1,572
Self-citationRecent approaches to global optimization problems through Particle Swarm Optimization
Parsopoulos, K. E., & Vrahatis, M. N. (2002) · Natural Computing · doi:10.1023/a:1016568309421 · Cited by 1,406
Tracking and optimizing dynamic systems with particle swarms
Eberhart, R. C., & Shi, Y. (2002) · Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546) · doi:10.1109/cec.2001.934376 · Cited by 1,013
Evolutionary Optimization in Dynamic Environments
Branke, J. (2002) · Genetic Algorithms and Evolutionary Computation · doi:10.1007/978-1-4615-0911-0 · Cited by 829
Self-citationMemory enhanced evolutionary algorithms for changing optimization problems
Branke, J. (2003) · Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406) · doi:10.1109/cec.1999.785502 · Cited by 743
Self-citationOn the Computation of All Global Minimizers Through Particle Swarm Optimization
Parsopoulos, K. E., & Vrahatis, M. N. (2004) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2004.826076 · Cited by 681
Cited by
Particle swarm optimization: An overview
Poli, R., Kennedy, J., & Blackwell, T. (2007) · Swarm Intelligence · doi:10.1007/s11721-007-0002-0 · Cited by 21,101
Self-citationA comprehensive review of firefly algorithms
Fister, I., Fister, I., Yang, X.-S., & Brest, J. (2013) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2013.06.001 · Cited by 1,308
Evolutionary dynamic optimization: A survey of the state of the art
Nguyen, T. T., Yang, S., & Branke, J. (2012) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2012.05.001 · Cited by 656
Self-citationBio-inspired computation: Where we stand and what's next
Del Ser, J., Osaba, E., Molina, D., Yang, X.-S., Salcedo-Sanz, S., Camacho, D., Das, S., Suganthan, P. N., Coello Coello, C. A., & Herrera, F. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2019.04.008 · Cited by 593
A survey of swarm intelligence for dynamic optimization: Algorithms and applications
Mavrovouniotis, M., Li, C., & Yang, S. (2017) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2016.12.005 · Cited by 585
Metadata
- Authors
- Blackwell, T., & Branke, J.
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2006-08
- Volume
- 10
- Issue
- 4
- Pages
- 459-472
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2115350182
- Cited by
- 536
- References
- 23
- FWCI
- 35.98
- Type
- journal article
- Language
- en
- Open access
- closed
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