The Barrier Tree Benchmark: Many Basins and Double Funnels

The Barrier Tree Benchmark (BTB) is a principled generator of continuous real-valued landscapes: problems of known topography/critical point structure can be systematically designed and deployed in algorithm comparison studies. A previous BTB study focused on a single funnel and a double basin. This work demonstrates algorithm performance on BTB instances with many basins, and on double funnels. A methodology for principled algorithm comparison on families of problems of similar complexity and structure is proposed. It is hoped that the BTB will address a parameter tuning pathology of current problem benchmarks, namely, that common optimisation algorithms require widely different control parameter settings for optimal performance on differing problem classes. This pathology is traced to the irregular and arbitrary composition of standard benchmarks

An Investigation Into the use of Swarm Intelligence for an Evolutionary Algorithm Optimisation; The Optimisation Performance of Differential Evolution Algorithm Coupled with Stochastic Diffusion Search

The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS) -- a swarm intelligence algorithm -- to empower the Differential Evolution (DE) -- an evolutionary algorithm -- over a set of optimisation problems. The results reported herein suggest that the powerful resource allocation mechanism deployed in SDS has the potential to improve the optimisation capability of the classical evolutionary algorithm used in this experiment. Different performance measures and statistical analyses were utilised to monitor the behaviour of the final coupled algorithm

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

Measuring optimiser performance on a conical barrier tree benchmark

The common method for testing metaheuristic optimisation algorithms is to benchmark against problem test suites. However, existing benchmark problems limit the ability to analyse algorithm performance due to their inherent complexity. This paper proposes a novel benchmark, BTB, whose member functions have known geometric properties and critical point topologies. A given function in the benchmark is a realisation of a specified barrier tree in which funnel and basin geometries, and values and locations of all critical points are predetermined. We investigate the behaviour of two metaheuristics, PSO and DE, on the simplest manifestations of the framework, ONECONE and TWOCONES, and relate algorithm performance to a downhill walker reference algorithm. We study success rate, defined as the probability of optimal basin attainment, and inter-basin mobility. We find that local PSO is the slowest optimiser on the unimodal ONECONE but surpasses global PSO in all TWOCONES problems instances below 70 dimensions. DE is the best optimiser when basin difference depths are large but performance degrades as the differences become smaller. LPSO is the superior algorithm in the more difficult case where basins have similar depth. DE consistently finds the optimum basin when the basins have equal size and a large depth difference in all dimensions below 100D; the performance of LPSO falls away abruptly beyond 70D

A sound you can touch

Jefferies and Blackwell collaborate on an on going practice based research named as A Sound you Can Touch, "woven sound" refers to the weaving of images from live sound. Incoming sound is digitised by the computer into a stream of left and right audio samples. In performance, sound is woven in real time; each image representing several seconds of sound. Woven sound emanating from saxophone multiphonics and bristles is projected so that the players' and the audience can see (and hear) the unfolding texture

Self-Organised Music

Self-organisation, as manifest, for example, by swarms, flock, herds and other collectives, is a powerful natural force, capable of generating large and sustained structures. Yet the individuals who participate in these social groups may not even be aware of the structures that they are creating. Almost certainly, these structures emerge through the application of simple, local interactions. Improvised music is an uncertain activity, characterised by a lack of top-down organisation and busy, local activity between improvisers. Emerging structures may only be perceivable at a (temporal) distance. The development of higher-level musical structure arises from interactions at lower levels, and we propose here that the self-organisation of social animals provides a very suggestive analogy. This paper builds a model of interactivity based on stigmergy, the process by which social insects communicate indirectly by environment modification. The improvisational element of our model arises from the dynamics of a particle swarm. A process called interpretation extracts musical parameters from the aural sound environment, and uses these parameters to place attractors in the environment of the swarm, after which stigmergy can take place. The particle positions are reinterpreted as parameterised audio events. This paper describes this model and two applications, Swarm Music and Swarm Granulator

Impact of communication topology in particle swarm optimization

Particle Swarm Optimisation has two salient components: a dynamical rule governing particle motion and an inter- particle communication topology. Recent practice has focused on the fully connected topology (Gbest) despite earlier indications on the superiority of local particle neighborhoods. This paper seeks to address the controversy with empirical trials with canonical PSO on a large benchmark of functions, categorized into fourteen properties. This paper confirms the early lore that Gbest is the overall better algorithm for unimodal and separable problems and that a ring neighborhood of connectivity two (Lbest) is the preferred choice for multimodal, non-separable and composition functions. Topologies of intermediate particle connectivity were also tested and the difference in global/local performance was found to be even more marked. A measure of significant improvement is introduced in order to distinguish major improvements from refinements. Lbest, according to the experiments on the 84 test functions and a bi-modal problem of adjustable severity, is found to have significant improvements later in the run, and to be more diverse at termination. A mobility study shows that Lbest is better able to jump between optimum basins. Indeed Gbest was unable to switch basins in the bi-modal trial. The implication is that Lbest’s larger terminal diversity, its better ability to basin hop and its later significant improvement account for the performance enhancement. In several cases where Lbest was not the better algorithm, the trials show that Lbest was not stuck but would have continued to improve with an extended evaluation budget. Canonical PSO is a baseline algorithm and the ancestor of all contemporary PSO variants. These variants build on the basic structure of baseline PSO and the broad conclusions of this study are expected to follow through. In particular, research that fails to consider local topologies risks underplaying the success of the promoted algorithm

A Simplified Recombinant PSO

Simplified forms of the particle swarm algorithm are very beneficial in contributing to understanding how a particle swarm optimization (PSO) swarm functions. One of these forms, PSO with discrete recombination, is extended and analyzed, demonstrating not just improvements in performance relative to a standard PSO algorithm, but also significantly different behavior, namely, a reduction in bursting patterns due to the removal of stochastic components from the update equations