The Cooperative Particle Swarm Optimiser (CPSO) is a variant of the Particle Swarm Optimiser (PSO) that splits the problem vector, for example a neural network weight vector, across several swarms. This paper investigates the influence that the number of swarms used (also called the split factor) has on the training performance of a Product Unit Neural Network. Results are presented, comparing ...

Particle swarm optimization is a population based optimization technique that is based on probability rules. In this technique each particle moves toward their best individual and group experience had occurred. Fundamental problems of standard PSO algorithm are the falling into the trap of local optimum and its low speed of convergence. One approach for solving the above problems is to combine ...

Particle swarm optimization (PSO) is a population-based optimization technique and it has been used to solve many optimization problems successfully. However, more efficient strategies are still needed to control the tradeoff between exploitation exploration in the search process for solving tasks with high complexity. In this work, we present a new hybrid PSO approach to overcome the search di...

This paper addresses the issue of swarm robots cooperative search. A swarm intelligence based algorithm, mechanical Particle Swarm Optimization (PSO), is first conducted which takes into account the robot mechanical properties and guiding the robots searching for a target. In order to avoid the robot localization and to avoid noise due to feedback and measurements, a new scheme which uses Extre...

Inspired by the phenomenon of symbiosis in natural ecosystems a multi-swarm cooperative particle swarm optimizer (MCPSO) is proposed as a new fuzzy modeling strategy for identification and control of non-linear dynamical systems. In MCPSO, the population consists of one master swarm and several slave swarms. The slave swarms execute particle swarm optimization (PSO) or its variants independentl...

The present work is a discussion on the performance analysis of Modified Cooperative Subchannel Allocation (CSA) Algorithms which is used in Alamouti Decoded and Forward (Alamouti DF) Relaying Protocol for wireless multi-user Orthogonal Frequency Division Multiplexing Access (OFDMA) systems. In addition, the performance of approximate Symbol Error Rate (SER) for the Alamouti DF Relaying Protoco...

This research focuses on continuous dimensional affect recognition from bodily expressions using feature optimization and adaptive regression. Both static posture and dynamic motion bodily features are extracted in this research. A hybrid particle swarm optimization (PSO) algorithm is proposed for feature selection, which overcomes premature convergence and local optimum trap encountered by con...

Particle Swarm Optimization (PSO), an evolutionary algorithm for optimization is extended to determine if natural selection, or survival-of-thefittest, can enhance the ability of the PSO algorithm to escape from local optima. To simulate selection, many simultaneous, parallel PSO algorithms, each one a swarm, operate on a test problem. Simple rules are developed to implement selection. The abil...