Improving exploration in Ant Colony Optimisation with antennation

Ant Colony Optimisation (ACO) algorithms use two heuristics to solve computational problems: one long-term (pheromone) and the other short-term (local heuristic). This paper details the development of antennation, a mid-term heuristic based on an analogous process in real ants. This is incorporated into ACO for the Travelling Salesman Problem (TSP). Antennation involves sharing information of the previous paths taken by ants, including information gained from previous meetings. Antennation was added to the Ant System (AS), Ant Colony System (ACS) and Ant Multi-Tour System (AMTS) algorithms. Tests were conducted on symmetric TSPs of varying size. Antennation provides an advantage when incorporated into algorithms without an inbuilt exploration mechanism and a disadvantage to those that do. AS and AMTS with antennation have superior performance when compared to their canonical form, with the effect increasing as problem size increases.IEEE Computational Intelligence Societ

Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

Routing packets from the source node to the destination node in wireless sensor networks WSN is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed three (3) other common algorithms in static WSN environment in terms of throughput, energy consumption and energy efficiency which will result to reduction of packet loss rate during packet routing and increase of network lifetime

Enhanced ant-based routing for improving performance of wireless sensor network

Routing packets from the source node to the destination node in wireless sensor network is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed five (5) other common algorithms in static WSN environment in terms of throughput, success rate, packet loss rate, energy consumption and energy efficiency. Overall, the proposed algorithm can lead to reduction of packet loss rate during packet routing and increase of network lifetime

Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

The influence of semiochemicals on the co-occurrence patterns of a global invader and native species

The success of invasive species in their introduced range is often influenced by interactions with resident species communities. Chemical communication is one the factors which contributes to a variety of aspects of a species life cycle, ranging from mating, to food localization and interactions with members of the same and other species. In my thesis, I investigate the effects of venoms and semiochemicals on interactions between the invasive Argentine ant (Linepethima humile) with other resident ant species and demonstrate how pheromones can potentially be utilized as an area wide control mechanism of this species, by disrupting their foraging success. I studied the effects of venom composition, their toxicity and utilization on the outcome of aggressive interactions between the Argentine ant and the four Monomorium species in New Zealand occurring. The toxicity of the venom of the two species co-occurring with Argentine ants was significantly higher than the toxicity of the species which do not. However, no correlation between venom toxicity and Monomorium survival was found. For M. antipodum a significant relationship between venom utilization and its mortality was found, indicating that the way venom is used might be an important aspect of these interactions. Physical Aggression between Monomorium and Argentine ants also had strong effects on Monomorium worker mortality, which provided evidence that a variety of factors and strategies contribute to the ability of interacting organisms to withstand the pressure of a dominant invader at high abundance. I conducted bioassays with food sources and synthetic trail pheromones, providing a proof of concept on disrupting the foraging ability of Argentine ants. Other resident species benefited from the reduced success of Argentine ants, but to a varying degree. Behavioural variations between the resident species provided an explanation for observed differences in foraging success and how much each of these individual competitors was able to increase their foraging. The mechanism for the observed increase in resource acquisition of resident species appeared to be a decrease in aggressive behaviour displayed by Argentine ants. I expanded the usage of the synthetic pheromone to a commercial vineyard, were Argentine ants can have negative effects on crop development by dispersing and tending to homopteran pest species. Argentine ants’ access to the crop canopy could be significantly reduced by placing pheromone dispensers at the base of the vine plant, while dispensers in the plant canopy had little effect on Argentine ant numbers. Doubling the amount of pheromone did not result in an additional reduction of ant activity. Lastly incorporating the knowledge gained in the previous chapter, I extended the application of the pheromone to a large field trial over a three month period. Argentine ant activity and foraging success was significantly supressed compared to untreated control plots, providing evidence that this form of large scale application might be a possible way to control large invasive ant populations by disrupting their trail following and foraging behaviour for a prolonged period of time. While initial calculations have suggested that the treatment is currently not feasible (13.3 US$/mg/ha), I found a significant reduction in body fat in workers collected from treated plots compared with untreated plots, suggesting adverse effects on nest fitness. My findings provide new insights into chemical communication between invasive and resident species, support existing dominance hierarchy models in ant populations, and help to establish a target specific potential management technique of wide-spread invasive ant species

Swarm intelligence and its applications to wireless ad hoc and sensor networks.

Swarm intelligence, as inspired by natural biological swarms, has numerous powerful properties for distributed problem solving in complex real world applications such as optimisation and control. Swarm intelligence properties can be found in natural systems such as ants, bees and birds, whereby the collective behaviour of unsophisticated agents interact locally with their environment to explore collective problem solving without centralised control. Recent advances in wireless communication and digital electronics have instigated important changes in distributed computing. Pervasive computing environments have emerged, such as large scale communication networks and wireless ad hoc and sensor networks that are extremely dynamic and unreliable. The network management and control must be based on distributed principles where centralised approaches may not be suitable for exploiting the enormous potential of these environments. In this thesis, we focus on applying swarm intelligence to the wireless ad hoc and sensor networks optimisation and control problems. Firstly, an analysis of the recently proposed particle swarm optimisation, which is based on the swarm intelligence techniques, is presented. Previous stability analysis of the particle swarm optimisation was restricted to the assumption that all of the parameters are non random since the theoretical analysis with the random parameters is difficult. We analyse the stability of the particle dynamics without these restrictive assumptions using Lyapunov stability and passive systems concepts. The particle swarm optimisation is then used to solve the sink node placement problem in sensor networks. Secondly, swarm intelligence based routing methods for mobile ad hoc networks are investigated. Two protocols have been proposed based on the foraging behaviour of biological ants and implemented in the NS2 network simulator. The first protocol allows each node in the network to choose the next node for packets to be forwarded on the basis of mobility influenced routing table. Since mobility is one of the most important factors for route changes in mobile ad hoc networks, the mobility of the neighbour node using HELLO packets is predicted and then translated into a pheromone decay as found in natural biological systems. The second protocol uses the same mechanism as the first, but instead of mobility the neighbour node remaining energy level and its drain rate are used. The thesis clearly shows that swarm intelligence methods have a very useful role to play in the management and control iv problems associated with wireless ad hoc and sensor networks. This thesis has given a number of example applications and has demonstrated its usefulness in improving performance over other existing methods

Sociogenetic investigation of the southern harvester termite, Microhodotermes viator, via genetic and behavioural bioassays

Includes bibliographical references.This thesis presents the first investigation of the population structure of the southern harvester termite, Microhodotermes viator (Family: Hodotermitidae), by assessing the genetic state and behavioural interactions within and between twelve colonies, from four areas across the Western Cape of South Africa. This study also critically debates the relationship between M. viator and heuweltjies (small Mima-like earth mounds), with regards to their origins and age. By critically analysing what is known, and debating the merits and shortcomings of various published hypotheses, this thesis concludes that heuweltjies are unequivocally attributable to the constructions and foraging activities of M. viator. However, the age and longevity of heuweltjies remains contentious. Several studies have attempted to ascertain age, using radiometric carbon dating on the basal calcrete layer found below mature heuweltjies, but there is disparity between results, primarily due to the challenges associated with dating calcrete. Therefore, an alternative method better equipped to mitigate these challenges, such as U-series isochron dating, is suggested for future research

The effects of forest cover change and polydomous colony organisation on the wood ant Formica lugubris

Anthropogenic land use changes, such as deforestation, generally have negative effects on ecosystems. However, in Europe recently, historic trends in deforestation are being reversed due to increases in planted forests, and it is becoming much rarer to replace native forests with plantations. British forests have undergone centuries of degradation and fragmentation, and increases in forest cover due to plantations represent a potential positive for forest specialist species struggling in isolated fragments. In this thesis, I assess forest cover change and the demographic and genetic health of populations of the wood ant Formica lugubris, a forest specialist, in the North York Moors National Park, UK. I show that, contrary to expectations, non-native conifer plantations have had incredibly beneficial effects on this forest specialist species. Populations of F. lugubris have expanded from historically isolated fragments, and show no evidence of this expansion ceasing. Furthermore expanded populations are genetically diverse in both nuclear and mitochondrial DNA, and show evidence of commercial forests connecting previously isolated population fragments. There is strong divergence within mitochondrial DNA across the landscape in F. lugubris, which suggests either a cryptic species within the study population, or an historic hybridisation event. Formica lugubris exhibits polydomous colony organisation, whereby multiple spatially separate nests display social and cooperative connections, and are therefore one colony. I show that socially connected nests are socially and cooperatively distinct from their neighbouring colony, but show no equivalent genetic distinction. The findings within this thesis support growing evidence that non-native conifer plantations can have positive effects on forest biodiversity, and that some wood ant populations within the UK are healthy and under no threat of extinction. Furthermore polydomous colonies are cooperative but not genetic units, and division of colonies in this species may be ecologically, rather than genetically determined

Fuzzy Rules from Ant-Inspired Computation

Centre for Intelligent Systems and their ApplicationsThis research identifies and investigates major issues in inducing accurate and comprehensible fuzzy rules from datasets.A review of the current literature on fuzzy rulebase induction uncovers two significant issues: A. There is a tradeoff between inducing accurate fuzzy rules and inducing comprehensible fuzzy rules; and, B. A common strategy for the induction of fuzzy rulebases, that of iterative rule learning where the rules are generated one by one and independently of each other, may not be an optimal one.FRANTIC, a system that provides a framework for exploring the claims above is developed. At the core lies a mechanism for creating individual fuzzy rules. This is based on a significantly modified social insect-inspired heuristic for combinatorial optimisation -- Ant Colony Optimisation. The rule discovery mechanism is utilised in two very different strategies for the induction of a complete fuzzy rulebase: 1. The first follows the common iterative rule learning approach for the induction of crisp and fuzzy rules; 2. The second has been designed during this research explicitly for the induction of a fuzzy rulebase, and generates all rules in parallel.Both strategies have been tested on a number of classification problems, including medical diagnosis and industrial plant fault detection, and compared against other crisp or fuzzy induction algorithms that use more well-established approaches. The results challenge statement A above, by presenting evidence to show that one criterion need not be met at the expense of the other. This research also uncovers the cost that is paid -- that of computational expenditure -- and makes concrete suggestions on how this may be resolved.With regards to statement B, until now little or no evidence has been put forward to support or disprove the claim. The results of this research indicate that definite advantages are offered by the second simultaneous strategy, that are not offered by the iterative one. These benefits include improved accuracy over a wide range of values for several key system parameters. However, both approaches also fare well when compared to other learning algorithms. This latter fact is due to the rule discovery mechanism itself -- the adapted Ant Colony Optimisation algorithm -- which affords several additional advantages. These include a simple mechanism within the rule construction process that enables it to cope with datasets that have an imbalanced distribution between the classes, and another for controlling the amount of fit to the training data.In addition, several system parameters have been designed to be semi-autonomous so as to avoid unnecessary user intervention, and in future work the social insect metaphor may be exploited and extended further to enable it to deal with industrial-strength data mining issues involving large volumes of data, and distributed and/or heterogeneous databases