An Investigation into the Merger of Stochastic Diffusion Search and Particle Swarm Optimisation

This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs

Creative or Not? Birds and Ants Draw with Muscle

In this work, a novel approach of merging two swarm intelligence algorithms is considered – one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined. Next, the novel behaviour of the hybrid algorithm is reflected through a cooperative attempt to make a drawing, followed by a discussion about creativity in general and the ’computational creativity’ of the swarm

State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Comparison of Weighted Sum Fitness Functions for PSO Optimization of Wideband Medium-gain Antennas

In recent years PSO (Particle Swarm Optimization) has been successfully applied in antenna design. It is well-known that the cost function has to be carefully chosen in accordance with the requirements in order to reach an optimal result. In this paper, two different wideband medium-gain arrays are chosen as benchmark structures to test the performance of four PSO fitness functions that can be considered in such a design. The first one is a planar 3 element, the second one a linear 4 element antenna. A MoM (Method of Moments) solver is used in the design. The results clearly show that the fitness functions achieve a similar global best candidate structure. The fitness function based on realized gain however converges slightly faster than the others

Optimisation of Likelihood for Bernoulli Mixture Models

The use of mixture models in statistical analysis is increasing for datasets with heterogeneity and/or redundancy in the data. They are likelihood based models, and maximum likelihood estimates of parameters are obtained by the use of the expectation maximization (EM) algorithm. Multi-modality of the likelihood surface means that the EM algorithm is highly dependent on starting points and poorly chosen initial points for the optimization may lead to only a local maximum, not the global maximum. In this thesis, different methods of choosing initialising points in the EM algorithm will be evaluated and two procedures which make intelligent choices of possible starting points and fast evaluations of their usefulness will be presented. Furthermore, several approaches to measure the best model to fit from a set of models for a given dataset, will be investigated and some lemmas and theorems are presented to illustrate the information criterion. This work introduces two novel and heuristic methods to choose the best starting points for the EM algorithm that are named Combined method and Hybrid PSO (Particle Swarm Optimisation). Combined method is based on a combination of two clustering methods that leads to finding the best starting points in the EM algorithm in comparison with the different initialisation point methods. Hybrid PSO is a hybrid method of Particle Swarm Optimization (PSO) as a global optimization approach and the EM algorithm as a local search to overcome the EM algorithm’s problem that makes it independent to starting points. Finally it will be compared with different methods of choosing starting points in the EM algorithm

Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties

Model-based approaches to 3D hand tracking have been shown to perform well in a wide range of scenarios. However, they require initialisation and cannot recover easily from tracking failures that occur due to fast hand motions. Data-driven approaches, on the other hand, can quickly deliver a solution, but the results often suffer from lower accuracy or missing anatomical validity compared to those obtained from model-based approaches. In this work we propose a hybrid approach for hand pose estimation from a single depth image. First, a learned regressor is employed to deliver multiple initial hypotheses for the 3D position of each hand joint. Subsequently, the kinematic parameters of a 3D hand model are found by deliberately exploiting the inherent uncertainty of the inferred joint proposals. This way, the method provides anatomically valid and accurate solutions without requiring manual initialisation or suffering from track losses. Quantitative results on several standard datasets demonstrate that the proposed method outperforms state-of-the-art representatives of the model-based, data-driven and hybrid paradigms.Comment: BMVC 2015 (oral); see also http://lrs.icg.tugraz.at/research/hybridhape

Efficient Global Optimisation of Microwave Antennas Based on a Parallel Surrogate Model-assisted Evolutionary Algorithm

Computational efficiency is a major challenge for evolutionary algorithm (EA)-based antenna optimisation methods due to the computationally expensive electromagnetic simulations. Surrogate model-assisted EAs considerably improve the optimisation efficiency, but most of them are sequential methods, which cannot benefit from parallel simulation of multiple candidate designs for further speed improvement. To address this problem, a new method, called parallel surrogate model-assisted hybrid differential evolution for antenna optimisation (PSADEA), is proposed. The performance of PSADEA is demonstrated by a dielectric resonator antenna, a Yagi-Uda antenna, and three mathematical benchmark problems. Experimental results show high operational performance in a few hours using a normal desktop 4-core workstation. Comparisons show that PSADEA possesses significant advantages in efficiency compared to a state-of-the-art surrogate model-assisted EA for antenna optimisation, the standard parallel differential evolution algorithm, and parallel particle swarm optimisation. In addition, PSADEA also shows stronger optimisation ability compared to the above reference methods for challenging design cases