Multi Objective PSO with Passive Congregation for Load Balancing Problem

High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA.

Passive Congregation Theory for Particle Swarm Optimization (PSO): an Application in Reservoir System Operation

Particle swarm optimisation (PSO) is a very well-known method and has a strong background in optimisation filed to solve different non-linear, complex problems especially in creating the reservoir release policies. This research modified the particle updating process of the standard PSO algorithm by including the passive congregation (PC) theory. The passive congregation theory of natural being’s social behaviour is adopted to updated the standard PSO algorithm and used to develop and optimise a reservoir release policy for monthly basis. The inflow data to the dam/reservoir has categorised into three different categories (High, medium and low). The problem is formulated on correspondence to the release and capacity constraints. Water deficit from the release is aimed to be minimised and formulated as the main objective function. Monthly releases are taken as the main objective variables and are essentially control the water deficit of the process. The standard form of PSO then compared with the updated version and the results is analysed by adopting different performance measuring indicators such as reliability, vulnerability and resilience. The results showed that the updated PSO-PC is more capable of the standard PSO (5% more reliable; 0.02 less vulnerable and 1.5 more resilience) in providing optimum results for a reservoir system.

SHORT TERM WIND SPEED PREDICTION USING A NEW HYBRID MODEL WITH PASSIVE CONGREGATION

Short term wind speed predicting is essential in using wind energy as an alternative source of electrical power generation, thus the improvement of wind speed prediction accuracy becomes an important issue. Although many prediction models have been developed during the last few years, they suffer a poor performance because their dependency on performing only the local search without the capability in performing the global search in the whole search space. To overcome this problem, we propose a new passive congregation term to the standard hybrid Genetic Algorithm / Particle Swarm Optimization (GA/ PSO) model in training Neural Network (NN) wind speed predictor. This term is based on the mutual cooperation between different particles in determining new positions rather than their selfish thinking. Experiment study shows significantly the influence of the passive congregation  term in improving the performance accuracy compared to the standard model.