Reactive power optimization is important to ensure power quality, improve system security, and reduce active power loss. So, this paper proposed parallel immune particle swarm optimization (PIPSO) algorithm. This algorithm makes basic particle swarm optimization (BPSO) and discrete particle swarm optimization (DPSO) to optimize in parallel, and improves the convergence capability of particle sw...

clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. the aim of clustering is to collect data points. in this paper, a cultural algorithm (ca) is presented to optimize partition with n objects into k clusters. the ca is one of the effective methods for searching into the problem space in order to find a n...

shear-wave velocity ( ) is an important parameter used for site characterization in geotechnical engineering. however, dispersion curve inversion is challenging for most inversion methods due to its high non-linearity and mix-determined trait. in order to overcome these problems, in this study, a joint inversion strategy is proposed based on the particle swarm optimization (pso) algorithm. the ...

this paper presents a fuzzy decision-making approach to deal with a clustering supplier problem in a supply chain system. during recent years, determining suitable suppliers in the supply chain has become a key strategic consideration. however, the nature of these decisions is usually complex and unstructured. in general, many quantitative and qualitative factors, such as quality, price, and fl...

The objective of this thesis is to investigate how to improve Particle Swarm Optimization by hybridization of stochastic search heuristics and by a Self-Organized Criticality extension. The thesis will describe two hybrid models extending Particle Swarm Optimization with two aspects from Evolutionary Algorithms, recombination via breeding and gene flow restriction via subpopulations. A further ...

Developing an effective memetic algorithm that integrates the Particle Swarm Optimization (PSO) algorithm and a local search method is a difficult task. The challenging issues include when the local search method should be called, the frequency of calling the local search method, as well as which particle should undergo the local search operations. Motivated by this challenge, we introduce a ne...