Showing papers on "Particle swarm optimization published in 1970"
01 Jan 1970
TL;DR: Test results indicate that optimally placed OPF with FACTS controllers by the hybrid-PSO could enhance the higher power transfer capability more than those from EP and conventional PSO.
Abstract: In this paper, the new hybrid particle swarm optimization (hybrid-PSO) based on particle swarm optimization (PSO), evolutionary programming (EP), and tabu search (TS) is developed. Hybrid-PSO is proposed to determine the optimal allocation of multi-type flexible AC transmission system (FACTS) controllers for simultaneously maximizing the power transfer capability of power transactions between generators and loads in power systems without violating system constraints. The particular optimal allocation includes optimal types, locations, and parameter settings. Four types of FACTS controllers consist of thyristor-controlled series capacitor (TCSC), thyristor-controlled phase shifter (TCPS), static var compensator (SVC), and unified power flow controller (UPFC). Power transfer capability determinations are calculated based on optimal power flow (OPF) technique. Test results on IEEE RTS 24-bus system, IEEE 30-bus system and, IEEE 118-bus system indicate that optimally placed OPF with FACTS controllers by the hybrid-PSO could enhance the higher power transfer capability more than those from EP and conventional PSO.
6 citations
TL;DR: A reformed and modified concept of PSO with the thought that every swarm updates its position based upon cognitive and social environment knowledge only is presented and the key aspect used here is that these parameters are no longer assumed to be accelerating components rather position components.
Abstract: In 1995 swarm intelligence based PSO (Particle Swarm Optimization) has been designed and implemented for solving optimization problems. Since then many researchers have developed many versions, based upon its theoretical concept, technical aspects and parameters involve in the algorithm. In broad sense, every swarm updates its position based upon the knowledge of its initial velocity and accelerating components such as cognitive and social information. In this paper we have presented a reformed and modified concept of PSO with the thought that every swarm updates its position based upon cognitive and social environment knowledge only and the key aspect used here is that these parameters are no longer assumed to be accelerating components rather position components. This algorithm is termed by us as Cognitive and Social Information based PSO (CSIPSO). The performance of CSI-PSO is validated by 23 benchmark functions and the empirical results clearly support the effectiveness of our concept. Keywords : Particle Swarm, PSO, Swarm Theory, Benchmark functions
4 citations
01 Jan 1970
TL;DR: This paper proposes a hybrid of PSO and Generalized Generation Gap model with Parent- Centric Recombination operator (G3PCX) with PSPG, a well-known real-coded genetic algorithm that combines fast convergence and rotational invariance of G3 PCX as well as global search ability ofPSO.
Abstract: Particle Swarm Optimization (PSO) algorithm has recently gained more attention in the global optimization research due to its simplicity and global search ability. This paper proposes a hybrid of PSO and Generalized Generation Gap model with Parent- Centric Recombination operator (G3PCX) [25], a well-known real-coded genetic algorithm. The proposed hybrid algorithm, namely PSPG, combines fast convergence and rotational invariance of G3PCX as well as global search ability of PSO. The performance of PSPG algorithm is evaluated using 8 widely-used nonlinear benchmark functions of 30 and 200 decision variables having different properties. The experiments study the effects of its new probability parameter Px and swarm size for optimizing those functions. The results are analyzed and compared with those from the Standard PSO [14] and G3PCX algorithms. The proposed PSPG with Px = 0.10 and 0.15 can outperform both algorithms with a statistical significance for most functions. In addition, the PSPG is not much sensitive to its swarm size as most PSO algorithms are. The best swarm sizes are 40 and 50 for unimodal and multimodal functions, respectively, of 30 decision variables.
1 citations