Abstract. Despite the many features that the behaviour of the standard particle swarm algorithm shares with grouping behaviour in animals (e.g. social attraction and communication between individuals), this biologically inspired technique has been mainly used in classical optimisation problems (i.e. finding the optimal value in a fitness landscape). We present here a novel application for parti...

Operating at absolute minimum cost can no longer be the only criterion for dispatching electric power due to increasing concern the environmental consideration. The emission controlled economic dispatch (ECED) problem which accounts for minimization of both fuel cost and emission is a multiple objective function problem. This paper proposed the particle swarm optimisation (PSO) metaheuristic me...

In this paper, we propose a particle swarm-based extreme learning machine (ELM) to classify datasets with varying number of classes. This work emphasises on a couple of important parameters, like maximisation of classification accuracy and minimisation of training time. As a machine classifier, an ELM has been chosen, which is an improvement over back propagation network. For each of the input ...

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SO...

The particle swarm optimisation algorithm is proposed as a new method to design a model based predictive controller subject to restrictions. Its performance is compared with the one obtained by using a genetic algorithm for the environmental temperature control of a greenhouse. Controller outputs are computed in order to optimise future behaviour of the greenhouse environment, regarding set-poi...

In this paper, we examine the application of Multi-Agent Reinforcement Learning (MARL) to a Dynamic Economic Emissions Dispatch problem. This is a multi-objective problem domain, where the conflicting objectives of fuel cost and emissions must be minimised. We evaluate the performance of several different MARL credit assignment structures in this domain, and our experimental results show that M...

We describe three recurrent neural architectures inspired by the proprioceptive system found in mammals; Exo-sensing, Ego-sensing, and Composite. Through the use of Particle Swarm Optimisation the robot controllers are adapted to perform the task of identifying motion dynamics within their environment. We highlight the effect of sensory-motor coordination on the performance in the task when app...

An alternative approach, between much others, for mathematical representation of dynamics systems with complex or chaotic behaviour, is a radial basis function neural network using k-means for clustering and optimized by pseudo-inverse and particle swarm optimisation. This paper presents the implementation and study to identify a dynamic system, with nonlinear and chaotic behaviour, called Röss...

a gyroscope is a device for measuring or maintaining orientation , based on the principles of conservation of angular momentum . the device is a spinning wheel or disk whose axle is free to take any orientation. this orientation changes much less in response to a given external torque than it would without the large angular momentum associated with the gyroscope's high rate of spin. since exter...

The tail of the particle swarm optimisation (PSO) position distribution at stagnation is shown to be describable by a power law. This tail fattening is attributed to particle bursting on all length scales. The origin of the power law is concluded to lie in multiplicative randomness, previously encountered in the study of first-order stochastic difference equations, and generalised here to secon...