An Evaluation of Performance Enhancements to Particle Swarm Optimisation on Real-World Data

Swarm Computation is a relatively new optimisation paradigm. The basic premise is to model the collective behaviour of self-organised natural phenomena such as swarms, flocks and shoals, in order to solve optimisation problems. Particle Swarm Optimisation (PSO) is a type of swarm computation inspired by bird flocks or swarms of bees by modelling their collective social influence as they search for optimal solutions. In many real-world applications of PSO, the algorithm is used as a data pre-processor for a neural network or similar post processing system, and is often extensively modified to suit the application. The thesis introduces techniques that allow unmodified PSO to be applied successfully to a range of problems, specifically three extensions to the basic PSO algorithm: solving optimisation problems by training a hyperspatial matrix, using a hierarchy of swarms to coordinate optimisation on several data sets simultaneously, and dynamic neighbourhood selection in swarms. Rather than working directly with candidate solutions to an optimisation problem, the PSO algorithm is adapted to train a matrix of weights, to produce a solution to the problem from the inputs. The search space is abstracted from the problem data. A single PSO swarm optimises a single data set and has difficulties where the data set comprises disjoint parts (such as time series data for different days). To address this problem, we introduce a hierarchy of swarms, where each child swarm optimises one section of the data set whose gbest particle is a member of the swarm above in the hierarchy. The parent swarm(s) coordinate their children and encourage more exploration of the solution space. We show that hierarchical swarms of this type perform better than single swarm PSO optimisers on the disjoint data sets used. PSO relies on interaction between particles within a neighbourhood to find good solutions. In many PSO variants, possible interactions are arbitrary and fixed on initialisation. Our third contribution is a dynamic neighbourhood selection: particles can modify their neighbourhood, based on the success of the candidate neighbour particle. As PSO is intended to reflect the social interaction of agents, this change significantly increases the ability of the swarm to find optimal solutions. Applied to real-world medical and cosmological data, this modification is and shows improvements over standard PSO approaches with fixed neighbourhoods

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

Enhancing the bees algorithm using the traplining metaphor

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms of simplicity, accuracy, and convergence. Three improvements were made in this study as a result of bees’ traplining behaviour. The first improvement was the parameter reduction of the Bees Algorithm. This strategy recruits and assigns worker bees to exploit and explore all patches. Both searching processes are assigned using the Triangular Distribution Random Number Generator. The most promising patches have more workers and are subject to more exploitation than the less productive patches. This technique reduced the original parameters into two parameters. The results show that the Bi-BA is just as efficient as the basic BA, although it has fewer parameters. Following that, another improvement was proposed to increase the diversification performance of the Combinatorial Bees Algorithm (CBA). The technique employs a novel constructive heuristic that considers the distance and the turning angle of the bees’ flight. When foraging for honey, bees generally avoid making a sharp turn. By including this turning angle as the second consideration, it can control CBA’s initial solution diversity. Third, the CBA is strengthened to enable an intensification strategy that avoids falling into a local optima trap. The approach is based on the behaviour of bees when confronted with threats. They will keep away from re-visiting those flowers during the next bout for reasons like predators, rivals, or honey run out. The approach will remove temporarily threatened flowers from the whole tour, eliminating the sharp turn, and reintroduces them again to the habitual tour’s nearest edge. The technique could effectively achieve an equilibrium between exploration and exploitation mechanisms. The results show that the strategy is very competitive compared to other population-based nature-inspired algorithms. Finally, the enhanced Bees Algorithms are demonstrated on two real-world engineering problems, namely, Printed Circuit Board insertion sequencing and vehicles routing problem

Genetic structure and colonisation history of European and UK population of Gammarus pulex

The structure of populations has been studied for many years and there have been three main factors that have been suggested as the cause for present-day distributions of species, those being environment, biology and history. With the use of molecular data and advanced phylogeographic approaches it is now possible to distinguish between the main causes of population structuring. The present study considers the extent of population structure in G. pulex on regional (UK) and large geographic (Europe) scales using studies of molecular genetic (allozymes, mtDNA sequencing and microsatellites) and morphological variation.Molecular analysis of Gammarus pulex in Europe revealed more diversity than previously thought. This was thought to be a consequence of two separate waves of colonisation after the formation of the major drainages in the Miocene. The UK appears to have been colonised once from either the Elbe, Mosel and Rhine drainages separately or cumulatively across the drainage basins late in the Pleistocene before a land bridge connection to mainland Europe was submerged. Limited molecular variation in the UK is thought to be a result of reduced genetic variation in the colonising individuals. This in turn was caused by repeated founder events during population expansion and contraction from European refugia.A detailed analysis of a transplantation experiment in 1950 in the Isle of Man revealed little genetic impoverishment of the introduced population when compared to the source. In contrast, morphological variation increased in the introduced population. Unlike in mainland Europe there was no historical explanation for the diversity recorded (as the introduced population was so young) and, in the absence of fragmentation, speciation and colonisation the contemporary forces of gene flow, selection and limited genetic drift are thought to be the determining factors in population structure

Inter-individual variability and phenotypic plasticity : the effect of the environment on the biogeography, population structure, ecophysiology and reproduction of the sandhoppers Talorchestia capensis and Africorchestia quadrispinosa

Climatic envelope models focus on the climatic variables affecting species or species assemblages, and are important tools to investigate the effect of climate change on their geographical ranges. These models have largely been proposed in order to make successful predictions on species‘ persistence, determining which variables are likely to induce range expansion, contraction, or shifting. More recent models, including the ability and the cost for individuals to respond promptly to an environmental stimulus, have revealed that species may express phenotypic plasticity able to induce adaptation to the new environment. Consequently, understanding how species evolve to a changing climate is fundamental. From this perspective, investigating intraspecific responses to an environmental variable may contribute to better understanding and prediction of the effect of climate change on the geographical range and evolution of species, particularly in the case of widespread species. In this context, the present study aimed at establishing how environmental variables (focussing mainly on temperature) may have contributed to shape the spatial distribution, physiology, reproductive biology and connectivity of two species of Southern African sandhoppers (Talorchestia capensis and Africorchestia quadrispinosa, Amphipoda, Talitridae). Most of the work was carried out on T. capensis, due to its widespread spatial distribution. A first investigation of the biogeography of T. capensis and A. quadrispinosa, revealed that, for both species, spatial patterns of abundance, size and sex ratio were not explained by the Abundant Centre Hypothesis (greater abundance at the core of a spatial range), but rather guided by bio-physical forces. Precisely, the abundance of sandhoppers was driven by the morphodynamic state of the beach, salinity and temperatures, with strong differentiation among sites that reflected local environmental conditions. In support of these findings, strong population structure in the genetics of T. capensis was found (three main groups) when investigating its phylogeography and genetic connectivity. Although such defined structure may suggests cryptic speciation, the concomitant within-population variation in the COX1 region of mtDNA, also highlighted the importance of individual genetic variability. High individual variability was also found in the response of T. capensis to temperature, both in its physiology (thermal plasticity) and its reproductive biology (maternal effects). Since temperature is one of the main variables affecting the coastal marine systems of southern Africa and the metabolism of animals in general, its effect on the physiology and reproduction of T. capensis was therefore investigated. Thermal responses to increasing/decreasing temperatures were assessed for separated populations of T. capensis. Individual variability was reported in the oxygen consumption of T. capensis in response to temperature (high variation around the means, especially for increasing temperatures). Among population differences in thermal sensitivity were significantly correlated with air temperature variability experienced over the past 23 years, highlighting the importance of historical temperature fluctuations to the current thermal physiology of these sandhoppers. Temperature also had an important effect on the reproductive plasticity of T. capensis. Different temperatures induced mothers to adjust the size of their offspring (i.e. egg size), with larger eggs produced at lower temperatures. Interestingly, females showed strongly significant among individual variation in the size of the eggs. Given the importance of understanding rapid responses of organisms to climate change and considering the fundamental role played by phenotypic plasticity in evolution, the overall study revealed the significance of individual plasticity and variability in response to the environment and highlighted its importance. Particularly, studying the thermal physiology of separated populations and understanding within population reproductive plasticity in response to temperature, helped to clarify how differences among individual responses have important consequences at the population level, possibly explaining the widespread distribution of T. capensis

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

The Functioning of Ecosystems

The ecosystems present a great diversity worldwide and use various functionalities according to ecologic regions. In this new context of variability and climatic changes, these ecosystems undergo notable modifications amplified by domestic uses of which it was subjected to. Indeed the ecosystems render diverse services to humanity from their composition and structure but the tolerable levels are unknown. The preservation of these ecosystemic services needs a clear understanding of their complexity. The role of the research is not only to characterise the ecosystems but also to clearly define the tolerable usage levels. Their characterisation proves to be important not only for the local populations that use it but also for the conservation of biodiversity. Hence, the measurement, management and protection of ecosystems need innovative and diverse methods. For all these reasons, the aim of this book is to bring out a general view on the biogeochemical cycles, the ecological imprints, the mathematical models and theories applicable to many situations

Selected Papers from the 2018 IEEE International Workshop on Metrology for the Sea

This Special Issue is devoted to recent developments in instrumentation and measurement techniques applied to the marine field. ¶The sea is the medium that has allowed people to travel from one continent to another using vessels, even today despite the use of aircraft. It has also been acting as a great reservoir and source of food for all living beings. However, for many generations, it served as a landfill for depositing conventional and nuclear wastes, especially in its deep seabeds, and we are assisting in a race to exploit minerals and resources, different from foods, encompassed in it. Its health is a great challenge for the survival of all humanity since it is one of the most important environmental components targeted by global warming. ¶ As everyone may know, measuring is a step that generates substantial knowledge about a phenomenon or an asset, which is the basis for proposing correct solutions and making proper decisions. However, measurements in the sea environment pose unique difficulties and opportunities, which is made clear from the research results presented in this Special Issue