A model for characterising the collective dynamic behaviour of evolutionary algorithms

Exploration and exploitation are considered essential notions in evolutionary algorithms. However, a precise interpretation of what constitutes exploration or exploitation is clearly lacking and so are specific measures for characterising such notions. In this paper, we start addressing this issue by presenting new measures that can be used as indicators of the exploitation behaviour of an algorithm. These work by characterising the extent to which available information guides the search. More precisely, they quantify the dependency of a population's activity on the observed fitness values and genetic material, utilising an empirical model that uses a coarse-grained representation of population dynamics and records information about it. The model uses the k-means clustering algorithm to identify the population's "basins of activity". The exploitation behaviour is then captured by an entropy-based measure based on the model that quantifies the strength of the association between a population's activity distribution and the observed fitness landscape information. In experiments, we analysed the effects of the search operators and their parameter settings on the collective dynamic behaviour of populations. We also analysed the effect of using different problems on algorithm behaviours.We define a behavioural landscape for each problem to identify the appropriate behaviour to achieve good results and point out possible applications for the proposed model

Flow injection analysis for the photometric determination of promethazine-HCl in pure and pharmaceutical preparation via oxidation by persulphate using Ayah 3SX3-3D solar micro photometer

The first flow injection spectrophotometric method is characterized by its speed and sensitivity which have been developed for the determination of promethazine-HCl in pure and pharmaceutical preparation. It is based on the in situ detection of colored cationic radicals formed via oxidation of the drug with sodium persulphate to pinkish-red species and the same species was determined by using homemade Ayah 3SX3-3D solar flow injection photometer. Optimum conditions were obtained by using the high intensive green light emitted diode as a source. Linear dynamic range for the absorbance versus promethazine-HCl concentration was 0-7 mmol.L-1, with the correlation coefficient (r) was 0.9904 while the percentage linearity (r2%) was 98.09%. the L.O.Q was 3.97 µg/sample, while L.O.D (S/N=3) = 0.2407µg/sample (5 µmol.L-1) from the stepwise dilution for the minimum concentration of lowest concentration in the linear dynamic range of the calibration graph. The R.S.D% at 2 mmol.L-1 promethazine-HCl is less than 1% (eight replicates) using 150 µL sample volume. Throughput 30 sample.hr-1. The method was applied successfully for the determination of promethazine-HCl in pharmaceutical preparation. By using paired t-test it was shown that there was no significant difference between the proposed method and official method and on that basis the new method can be accepted as an alternative analytical method

Iterated Local Search Algorithm for Clustering Wireless Sensor Networks.

In this paper, a new clustering protocol employing an iterated local search (ILS) to solve cluster head selection problem is proposed. ILS uses a perturbation operator to change an initial random solution to produce a new point in the vicinity of the solution. Using a combination operator, this new point is mated with the random solution producing a new solution. A move from the current solution to the new solution is considered acceptable only for higher fitness value. If a move is rejected after a predetermined search length, the change rate of the current solution is increased in order to explore a wider search space for quality solutions. In each round, this search process continues until good solution that ensures balanced energy consumption is obtained for the network. Furthermore, we propose a sleep scheduling scheme inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed mechanism of inactive node and cluster head selection protocols are performed sequentially at every round and they form part of the main algorithm proposed, namely the Dynamic Local Search-Based Algorithm for Clustering Hierarchy (DLSACH). The ultimate goal of the DLSACH protocol is to extends the network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows an improved performance compared to state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployment

A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network

© Springer International Publishing Switzerland 2016.In this paper, a Heuristic-Crossover Enhanced Evolutionary Algorithm for Cluster Head Selection is proposed. The algorithm uses a novel heuristic crossover operator to combine two different solutions in order to achieve a high quality solution that distributes the energy load evenly among the sensor nodes and enhances the distribution of cluster head nodes in a network. Additionally, we propose the Stochastic Selection of Inactive Nodes, a mechanism inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed selection of inactive node mechanisms and cluster head selections protocol are performed sequentially at every round and are part of the main algorithm proposed, namely the Heuristic Algorithm for Clustering Hierarchy (HACH). The main goal of HACH is to extend network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows improved performance compared with state-of-the-art protocols like LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployments

Unsupervised Learning Techniques for HVAC Terminal Unit Behaviour Analysis

In the pursuit of improved energy ef ciency, older and new buildings are being tted with Building Energy Management System (BEMS). BEMS can be used to extract valuable building data that can be further analysed to discover problems related to user comfort, building maintenance and energy wastage in buildings. The main focus of this paper is to demonstrate and effective method to remotely analyse and categorise the different Heating, Ventilation and Air-Conditioning (HVAC) Terminal Unit (TU) behaviours using BEMS data. Using a data-driven, unsupervised learning strategy to identify anomalous behaviours enabling noti cations to the building manager regarding faulty TUs can go a long way in providing energy savings and improving building performance. A novel feature extraction method based on event discovery from TU data is proposed and applied to multidimensional data streams retrieved from a building based in the city of London. Further, X-means clustering has been performed over the extracted features to group the different TU behaviours. The clustering results, validated through established statistical methods, successfully yield several distinct TU behaviour patterns in addition to the outliers. The clustering behaviour has been further veri ed across daily and weekly TUs

A new approach for event detection using k-means clustering and neural networks.

In composite event detection systems such as fire alarms, the two foremost goals are speed and accuracy. One way to achieve these goals is by performing data aggregation at central nodes. This helps reduce energy consumption and redundancy. In this paper we present a new hybrid approach that involves the use of k-means algorithm with neural networks, an efficient supervised learning algorithm that extracts patterns and detects trends that are hidden in complex data. Previous research on event detection concentrates majorly on the use of feed forward neural network and other classifiers such as naive Bayes and decision tree alone for modern fire detection applications. In our approach presented here, we combine k-means with neural networks and other classifiers in order to improve the detection rate of event detection applications. To demonstrate our approach, we perform data aggregation on normalized multi-dimensional fire datasets in order to remove redundant data. The aggregated data forms two clusters which represent the two class labels (actual outputs) with the aid of k-means clustering. The resulting data outputs are trained by the Feed Forward Neural Network, Naive Bayes, and Decision Trees. This approach was found to significantly improve fire detection performance