Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO). In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO. The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which i...

In this paper, a new variant of particle swarm optimisation (PSO) called PSO with improved learning strategy (PSO-ILS) is developed. Specifically, an ILS module is proposed to generate a more effective and efficient exemplar, which could offer a more promising search direction to the PSO-ILS particle. Comparison is made on the PSO-ILS with 6 well-established PSO variants on 10 benchmark functio...

Original scientific paper This paper presents a heuristic algorithm PSO based simultaneous coordinated tuning of the SVC damping controller and power system stabilizer (PSS) in a two machine three bus power system. The coordinated tuning of PSS and SVC damping controller is converted to an optimization problem which is solved by particle swarm optimization (PSO) technique. The parameters like r...

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel eomputational method for solv­ ing seareh and optimization problems with single or multiple objeetives, HEM is an intelligent evolutionary optimization method that uses eonsensus knowledge from experts with the aim of inferring the most suitable parameters to aehieve the evolution in an intelligent way, HEM is abl...

In the electric power systems, there is a wide range of problems involving optimization processes. Among them, Economic Dispatch (ED) is one of the most important problems in the operation and management. Recently, modern meta-heuristic algorithms have been considered as effective tools for nonlinear optimization problems with applications to power systems scheduling. PSO is one of modern heuri...

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is supported by the several methods. Particle Swarm Optimization (PSO) algorithm is one these methods, however computation time required is a big bottleneck. This paper proposes three dynamic PSO-based deployment algorithms that reduce the computati...

In this paper, we present an effective and efficient diagnosis system based on particle swarm optimization (PSO) enhanced fuzzy k-nearest neighbor (FKNN) for Parkinson’s disease (PD) diagnosis. In the proposed system, named PSO–FKNN, both the continuous version and binary version of PSO were used to perform the parameter optimization and feature selection simultaneously. On the one hand, the ne...

This paper presents the optimal fuzzy controller design for load frequency control (LFC) of two area power system using Adaptive Particle Swarm Optimization (APSO). Firstly, the controller is designed according to Fuzzy Logic rules such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC) used earlier was optimized with PSO so as to obtain optimal adjustment of ...

This paper studies a facility location model with fuzzy random parameters and its swarm intelligence approach. A Value-at-Risk (VaR) based fuzzy random facility location model (VaR-FRFLM) is built in which both the costs and demands are assumed to be fuzzy random variables, and the capacity of each facility is unfixed but a decision variable assuming continuous values. Under this setting, the V...

In order to improve the accuracy and real-time of all kinds of information in the cash business, and solve the problem which accuracy and stability is not high of the data linkage between cash inventory forecasting and cash management information in the commercial bank, a hybrid learning algorithm is proposed based on adaptive population activity particle swarm optimization (APAPSO) algorithm c...