Suzumushi : the silent swarm

This silent swarm of stylized crickets is downloading data from Internet and catalogue searches being undertaken by the public at the State Library Queensland. These searches are being displayed on the screen on their backs. Each cricket downloads the searches and communicates this information with other crickets. Commonly found searches spread like a meme through the swarm. In this work memes replace the crickets’ song, washing like a wave through the swarm and changing on the whim of Internet users. When one cricket begins calling others, the swarm may respond to produce emergent patterns of text. When traffic is slow or of now interest to the crickets, they display onomatopoeia. The work is inspired by R. Murray Schafer’s research into acoustic ecologies. In the 1960’s Schafer proposed that many species develop calls that fit niches within their acoustic environment. An increasing background of white noise dominates the acoustic environment of urban human habitats, leaving few acoustic niches for other species to communicate. The popularity of headphones and portable music may be seen as an evolution of our acoustic ecology driven by our desire to hear expressive, meaningful sound, above the din of our cities. Similarly, the crickets in this work are hypothetical creatures that have evolved to survive in a noisy human environment. This speculative species replaces auditory calls with onomatopoeia and information memes, communicating with the swarm via radio frequency chirps instead of sound. Whilst these crickets cannot make sound, each individual has been programmed respond to sound generated by the audience, by making onomatopoeia calls in text. Try talking to a cricket, blowing on its tail, or making other sounds to trigger a call.

Train service timetabling in railway open markets by particle swarm optimisation

Railway timetabling is an important process in train service provision as it matches the transportation demand with the infrastructure capacity while customer satisfaction is also considered. It is a multi-objective optimisation problem, in which a feasible solution, rather than the optimal one, is usually taken in practice because of the time constraint. The quality of services may suffer as a result. In a railway open market, timetabling usually involves rounds of negotiations among a number of self-interested and independent stakeholders and hence additional objectives and constraints are imposed on the timetabling problem. While the requirements of all stakeholders are taken into consideration simultaneously, the computation demand is inevitably immense. Intelligent solution-searching techniques provide a possible solution. This paper attempts to employ a particle swarm optimisation (PSO) approach to devise a railway timetable in an open market. The suitability and performance of PSO are studied on a multi-agent-based railway open-market negotiation simulation platform.

Coupling hybrid-game strategies with particle swarm optimisation for multi-objective high lift systems design optimisation

This paper investigates the High Lift System (HLS) application of complex aerodynamic design problem using Particle Swarm Optimisation (PSO) coupled to Game strategies. Two types of optimization methods are used; the first method is a standard PSO based on Pareto dominance and the second method hybridises PSO with a well-known Nash Game strategies named Hybrid-PSO. These optimization techniques are coupled to a pre/post processor GiD providing unstructured meshes during the optimisation procedure and a transonic analysis software PUMI. The computational efficiency and quality design obtained by PSO and Hybrid-PSO are compared. The numerical results for the multi-objective HLS design optimisation clearly shows the benefits of hybridising a PSO with the Nash game and makes promising the above methodology for solving other more complex multi-physics optimisation problems in Aeronautics.

Particle Swarm algorithm with Fuzzy decision making for a multi-objective economic and environmental optimization of design of a thermal system

Multi-Objective optimization for designing of a benchmark cogeneration system known as CGAM cogeneration system has been performed. In optimization approach, the thermoeconomic and Environmental aspects have been considered, simultaneously. The environmental objective function has been defined and expressed in cost terms. One of the most suitable optimization techniques developed using a particular class of search algorithms known as; Multi-Objective Particle Swarm Optimization (MOPSO) algorithm has been used here. This approach has been applied to find the set of Pareto optimal solutions with respect to the aforementioned objective functions. An example of fuzzy decision-making with the aid of Bellman-Zadeh approach has been presented and a final optimal solution has been introduced.

A modified particle swarm optimization for correlated phenomena

The wide applicability of correlation analysis inspired the development of this paper. In this paper, a new correlated modified particle swarm optimization (COM-PSO) is developed. The Correlation Adjustment algorithm is proposed to recover the correlation between the considered variables of all particles at each of iterations. It is shown that the best solution, the mean and standard deviation of the solutions over the multiple runs as well as the convergence speed were improved when the correlation between the variables was increased. However, for some rotated benchmark function, the contrary results are obtained. Moreover, the best solution, the mean and standard deviation of the solutions are improved when the number of correlated variables of the benchmark functions is increased. The results of simulations and convergence performance are compared with the original PSO. The improvement of results, the convergence speed, and the ability to simulate the correlated phenomena by the proposed COM-PSO are discussed by the experimental results.

Distribution state estimation based on particle swarm and doubly loop mutant optimization (DLM-PSO)

This paper presents a novel algorithm based on particle swarm optimization (PSO) to estimate the states of electric distribution networks. In order to improve the performance, accuracy, convergence speed, and eliminate the stagnation effect of original PSO, a secondary PSO loop and mutation algorithm as well as stretching function is proposed. For accounting uncertainties of loads in distribution networks, pseudo-measurements is modeled as loads with the realistic errors. Simulation results on 6-bus radial and 34-bus IEEE test distribution networks show that the distribution state estimation based on proposed DLM-PSO presents lower estimation error and standard deviation in comparison with algorithms such as WLS, GA, HBMO, and original PSO.

Reconstruction of a multi-vent kimberlite eruption from deposit and host rock characteristics: Jericho kimberlite, Nunavut, Canada

The Jericho kimberlite (173.1. ±. 1.3. Ma) is a small (~. 130. ×. 70. m), multi-vent system that preserves products from deep (>. 1. km?) portions of kimberlite vents. Pit mapping, drill core examination, petrographic study, image analysis of olivine crystals (grain size distributions and shape studies), and compositional and mineralogical studies, are used to reconstruct processes from near-surface magma ascent to kimberlite emplacement and alteration. The Jericho kimberlite formed by multiple eruptions through an Archean granodiorite batholith that was overlain by mid-Devonian limestones ~. 1. km in thickness. Kimberlite magma ascended through granodiorite basement by dyke propagation but ascended through limestone, at least in part, by locally brecciating the host rocks. After the first explosive breakthrough to surface, vent deepening and widening occurred by the erosive forces of the waxing phase of the eruption, by gravitationally induced failures as portions of the vent margins slid into the vent and, in the deeper portions of the vent (>. 1. km), by scaling, as thin slabs burst from the walls into the vent. At currently exposed levels, coherent kimberlite (CK) dykes (<. 40. cm thick) are found to the north and south of the vent complex and represent the earliest preserved in-situ products of Jericho magmatism. Timing of CK emplacement on the eastern side of the vent complex is unclear; some thick CK (15-20. m) may have been emplaced after the central vent was formed. Explosive eruptive products are preserved in four partially overlapping vents that are roughly aligned along strike with the coherent kimberlite dyke. The volcaniclastic kimberlite (VK) facies are massive and poorly sorted, with matrix- to clast-supported textures. The VK facies fragmented by dry, volatile-driven processes and were emplaced by eruption column collapse back into the volcanic vents. The first explosive products, poorly preserved because of partial destruction by later eruptions, are found in the central-east vent and were formed by eruption column collapse after the vent was largely cleared of country rock debris. The next active vent was either the north or south vent. Collapse of the eruption column, linked to a vent widening episode, resulted in coeval avalanching of pipe margin walls into the north vent, forming interstratified lenses of country rock-rich boulder breccias in finer-grained volcaniclastic kimberlite. South vent kimberlite has similar characteristics to kimberlite of the north vent and likely formed by similar processes. The final eruptive phase formed olivine-rich and moderately sorted deposits of the central vent. Better sorting is attributed to recycling of kimberlite debris by multiple eruptions through the unconsolidated volcaniclastic pile and associated collapse events. Post-emplacement alteration varies in intensity, but in all cases, has overprinted the primary groundmass and matrix, in CK and VK, respectively. Erosion has since removed all limestone cover.

A survey: Particle Swarm Optimization based algorithms to solve premature convergence problem

Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method based on the social behaviors of birds flocking or fish schooling. Although, PSO is represented in solving many well-known numerical test problems, but it suffers from the premature convergence. A number of basic variations have been developed due to solve the premature convergence problem and improve quality of solution founded by the PSO. This study presents a comprehensive survey of the various PSO-based algorithms. As part of this survey, the authors have included a classification of the approaches and they have identify the main features of each proposal. In the last part of the study, some of the topics within this field that are considered as promising areas of future research are listed.

Balancing exploration and exploitation in particle swarm optimization on search tasking

In this study we present a combinatorial optimization method based on particle swarm optimization and local search algorithm on the multi-robot search system. Under this method, in order to create a balance between exploration and exploitation and guarantee the global convergence, at each iteration step if the distance between target and the robot become less than specific measure then a local search algorithm is performed. The local search encourages the particle to explore the local region beyond to reach the target in lesser search time. Experimental results obtained in a simulated environment show that biological and sociological inspiration could be useful to meet the challenges of robotic applications that can be described as optimization problems.