Differential Evolution (DE)
Differential Evolution (DE) is an optimizatin method developed by Price and Storn in 1995. DE is original proposed as an evolution strategy (ES). It may also be considered as an swarm intelligence (SI) based method.
DE has been implemented into the Multiagent Optimization System (MAOS).
Xiao-Feng Xie, Jiming Liu. A compact multiagent system based on autonomy oriented computing, IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT), Compiégne, France, 2005: 38-44. [DOI]
Xiao-Feng Xie, Wen-Jun Zhang. SWAF: swarm algorithm framework for numerical optimization. Genetic and Evolutionary Computation Conference (GECCO), LNCS 3102, Seattle, WA, USA, 2004: 238-250. [DOI]
Xiao-Feng Xie, Wen-Jun Zhang, De-Chun Bi. Handling equality constraints by adaptive relaxing rule for swarm algorithms. Congress on Evolutionary Computation (CEC), Portland, OR, USA, 2004: 2012-2016. [DOI]
Wen-Jun Zhang, Xiao-Feng Xie. DEPSO: hybrid particle swarm with differential evolution operator. IEEE International Conference on Systems, Man, and Cybernetics (SMCC), Washington, DC, USA, 2003: 3816-3821. [DOI]
Xiao-Feng Xie, Wen-Jun Zhang, Guo-Rui Zhang, Zhi-Lian Yang. Empirical study for differential evolution. Control and Decision, 2004, 19(1): 49-52.
*abbreviations: SRC=source code; BIN=binary code
||(JAVA) Hybrid Particle Swarm Optimization (PSO) with DE [DOC] for solving (constrained) numerical optimization, including periodic boundary handling method [DOC] & adaptive relaxing rule for equality constraints [DOC]. It is also a component of Swarm Algorithm Framework (SWAF).
Return to homepage
Maintained by AdaptiveBox StUdIo, under a Creative Commons Attribution 3.0 License.