Honey Bees Inspired Optimization Method: The Bees Algorithm

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem

On the effects of lamination artificial faults in A 15 kVA three-phase transformer core

Cutting and punching of the steel used in power transformer core may cause edge burrs. This, along with the degradation of the lamination insulation, can lead to interlaminar short circuits. Analysing these faults helps understanding their effect on the transformer reliability and performance. In this light, the actual paper aims to experimentally simulate and analyse both faults using a 15 kVA three phase power transformer. Effects produced from both selected faults are experimentally investigated in this paper where different scenarios are considered such as the area of the affected regions and the number of short-circuited laminations. Various flux densities are considered ranging from 0.5 to 1.8 T. Of interest, the current at no load is recorded and the test is repeated for any given scenario. The obtained results are presented and discussed to study the effect of each fault on the transformer performance. Overall, the transformer current increases with the number of short-circuits between laminations for both faults. This increase is related to the flux density, which is dependent and sensitive to the short circuit location. Such findings represent a good indication of the severity of short circuits relative to their position in the transformer core, and can be exploited to discuss the power losses in the transformer core

Optimizing the number of acoustic emission sensors using the bees algorithm for detecting surface fractures

Non-destructive testing methods have gained popularity as they become more widely available. Although there are several techniques that could be used for this purpose, this paper focuses on acoustic emission sensors for detecting surface fractures and the use of the Bees Algorithm, a swarm-based technique, for optimizing the number of sensors required to reliably detect surface fractures. The paper describes the approach that has been used in this study where the dimension of the surface is specified by the user. The results show that, in theory and through simulation, that the Bees Algorithm is capable of determining the minimum number of sensors needed to locate the surface fracture with an acceptable level of accuracy. The method described could be used for the purpose of optimization in other engineering as well as non-engineering applications

Data mining techniques applied to a manufacturing SME

This paper examines how data mining, an aspect of analytical science, can be applied to assist a Small to Medium Enterprise (SME) industry using unsupervised learning techniques, association rules and time-series analysis. Whilst recent developments have meant it is now possible for SME to compile large amounts of commercial data, this information is rarely utilised effectively. The study builds on a number of standard data mining techniques to produce a tailored set of analyses that provide maximum benefit to the company. Self-Organising Maps were utilised to visualise the core characteristics of the firm's customers. The study outlines a new technique to determine associations between customer variables using the arules package available within RStudios. Finally, time-series forecasting was conducted highlighting the seasonal variations and trends for potential growth in the coming year

Green machining for the dry milling process of stainless steel 304

Dry machining represents an eco-friendly method that reduces the environmental impacts, saves energy costs, and protects operator health. This paper presents a multi-response optimization which aims to enhance the power factor and decrease the energy consumption as well as the surface roughness for the dry machining of a stainless steel 304. The cutting speed (V), depth of cut (a), feed rate (f), and nose radius (r) were the processing conditions. The outputs of the optimization are the power factor, energy consumption, and surface roughness. The relationships between inputs and outputs were established using the radial basis function models. The experimental data were normalized, with the use of the grey relational analysis. The principal component analysis is applied to calculate the weight values of technical responses. The desirability approach is used to observe the optimal values. The results showed that the technical outputs are primarily influenced by the feed rate and cutting speed. The reductions of energy consumption and surface roughness are approximately 34.85 % and 57.65 %, respectively, and the power factor improves around 28.83 %, compared to the initial process parameter settings. The outcomes and findings of the investigated work can be used for further research in sustainable design and manufacturing as well as directly used in the knowledge-based and expert systems for dry milling applications in industrial practices

Predictive maintenance in a manufacturing environment through FIT manufacturing and discrete event simulation

The focus of this paper is to introduce the use of FIT manufacturing principles and discrete event simulation in a manufacturing environment for productivity increase and predictive maintenance. FIT manufacturing primarily deals with lean, agile and sustainable concepts to achieve the best process flow while maintaining productivity, profitability and waste at their optimum levels. In this study, a manufacturing process has been modelled by process mapping and utilised by Discrete Event Simulation under FIT manufacturing constraints to study the behaviour of the manufacturing system. The simulation results were successfully used to identify any delays in the process flow caused by machine breakdowns. The study showed that this information could be used for the purpose of predictive maintenance which in turn will reduce machine down times and increase the process flow and the overall efficiency

A System of Systems Approach to Supply Chain Design

This paper proposes a novel System of Systems (SoSs) framework in order to design and optimise Supply Chains (SCs). In this paper the characteristics of System of Systems and Supply Chains have been discussed and a similarity match has been made between the two. It is interesting to note that although some of these SoSs characteristics are intrinsic in nature of the SCs others such as evolutionary behaviour and self-organization need to be modelled. In this paper, an adaptive supply chain multi-level multi-objective optimisation framework has been proposed in order to have both evolutionary and self-organized behaviour. This framework is capable of performing both local and global optimisation and adaptation to different scenarios

Bee for mining (B4M) - a novel rule discovery method using the Bees algorithm with quality-weight and coverage-weight

This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the Bees Algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight and coverage-weight have been added to the BA to avoid any ambiguous situations during the prediction phase. The contributions of the proposed B4M algorithm are two-fold: the first novel contribution is in the field of swarm intelligence, using a new version of BA for automatic rule discovery, and the second novel contribution is the formulation of a weight metric based on quailty and coverage of the rules discovered from the dataset to carry out Meta-Pruning and making it suitable for any classification problem in the real world. The proposed algorithm was implemented and tested using five different datasets from University of California, at Irvine (UCI Machine Learning Repository) and was compared with other well-known classification algorithms. The results obtained using the proposed B4M show that it was capable of achieving better classification accuracy and at the same time reduce the number of rules in four out of five UCI datasets. Furthermore, the results show that it was not only effective and more robust, but also more efficient, making it at least as good as other methods such as C5.0, C4.5, Jrip and other evolutionary algorithms, and in some cases even better

Multi-objective invasive weed optimization of the LQR controller

The Robogymnast is a triple link underactuated pendulum that mimics a human gymnast hanging from a horizontal bar. In this paper, two multi-objective optimization methods are developed using invasive weed optimization (IWO). The first method is the weighted criteria method IWO (WCMIWO) and the second method is the fuzzy logic IWO hybrid (FLIWOH). The two optimization methods were used to investigate the optimum diagonal values for the Q matrix of the linear quadratic regulator (LQR) controller that can balance the Robogymnast in an upright configuration. Two LQR controllers were first developed using the parameters obtained from the two optimization methods. The same process was then repeated, but this time with disturbance applied to the Robogymnast states to develop another set of two LQR controllers. The response of the controllers was then tested in different scenarios using simulation and their performance evaluated. The results show that all four controllers are able to balance the Robogymnast with varying accuracies. It has also been observed that the controllers trained with disturbance achieve faster settling time

Optimisation of the replenishment problem in the Fashion Retail Industry using Tabu-Bees algorithm

The problem of properly allocating several different products in the stores network is one of the crucial processes in the Retail Industry. This means trying to meet market demand and at the same time reducing logistics costs. In this context, main aims of the presented work are to (a) otpimise the replenishment process for the retailer's network of companies with multi-product multi-store configuration and (b) propose a new algorithm which combines the Basic Bees algorithm, a swarm based optimization technique, with the Tabu-Search principles. Attention is focused on the particular case of lost sales and budget constraints for a fashion company. Simulation results show that the use of the Tabu-Bees algorithm can guarantee an average 10% improvement in results allowing the model to overcome local minima