Tag: pso

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Dissipative Particle Swarm Optimization (DPSO)

The dissipative particle swarm optimization (DPSO) is developed according to the self-organization of dissipative structure. The negative entropy is introduced into the particle swarm to construct an opening dissipative system that is far-from-equilibrium so as to driving the evolutionary process towards better fitness. Xiao-Feng Xie, Wen-Jun Zhang, and Zhi-Lian Yang. A dissipative particle swarm optimization.

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DEPSO Algorithm: Project Portal

DEPSO [1], or called DEPS, is an algorithm for (constrained) numerical optimization problem (NOP). DEPSO combines the advantages of Particle Swarm Optimization (PSO) and Differential Evolution (DE). It is incorporated into cooperative group optimization (CGO) system [2]. The DEPSO paper has been cited over 400 times with various applications. DEPSO was also implemented (by Sun

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Particle Swarm Optimization (PSO) Software

Particle swarm optimization (PSO) is a population-based stochastic optimization technique inspired by swarm intelligence. The underlying motivation for the development of PSO algorithm was social behavior of animals such as bird flocking, fish schooling, and swarm theory. The fundamental to the development of PSO is a hypothesis that social sharing of information among peers offers an evolutionary

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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 Cooperative Group Optimization System (CGOS). DEPSO is a effective algorithm to combine the advantages

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