Preface: Swarm Intelligence, Focus on Ant and Particle Swarm Optimization

In the era globalisation the emerging technologies are governing engineering industries to a multifaceted state. The escalating complexity has demanded researchers to find the possible ways of easing the solution of the problems. This has motivated the researchers to grasp ideas from the nature and implant it in the engineering sciences. This way of thinking led to emergence of many biologically inspired algorithms that have proven to be efficient in handling the computationally complex problems with competence such as Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), etc. Motivated by the capability of the biologically inspired algorithms the present book on ""Swarm Intelligence: Focus on Ant and Particle Swarm Optimization"" aims to present recent developments and applications concerning optimization with swarm intelligence techniques. The papers selected for this book comprise a cross-section of topics that reflect a variety of perspectives and disciplinary backgrounds. In addition to the introduction of new concepts of swarm intelligence, this book also presented some selected representative case studies covering power plant maintenance scheduling; geotechnical engineering; design and machining tolerances; layout problems; manufacturing process plan; job-shop scheduling; structural design; environmental dispatching problems; wireless communication; water distribution systems; multi-plant supply chain; fault diagnosis of airplane engines; and process scheduling. I believe these 27 chapters presented in this book adequately reflect these topics

An interactive product development model in remanufacturing environment: a chaos-based artificial bee colony approach

This research presents an interactive product development model in re-manufacturing environment. The product development model defined a quantitative value model considering product design and development tasks and their value attributes responsible to describe functions of the product. At the last stage of the product development process, re-manufacturing feasibility of used components is incorporated. The consummate feature of this consideration lies in considering variability in cost, weight, and size of the constituted components depending on its types and physical states. Further, this research focuses on reverse logistics paradigm to drive environmental management and economic concerns of the manufacturing industry after the product launching and selling in the market. Moreover, the model is extended by integrating it with RFID technology. This RFID embedded model is aimed at analyzing the economical impact on the account of having advantage of a real time system with reduced inventory shrinkage, reduced processing time, reduced labor cost, process accuracy, and other directly measurable benefits. Consideration the computational complexity involved in product development process reverse logistics, this research proposes; Self-Guided Algorithms & Control (S-CAG) approach for the product development model, and Chaos-based Interactive Artificial Bee Colony (CI-ABC) approach for re-manufacturing model. Illustrative Examples has been presented to test the efficacy of the models. Numerical results from using the S-CAG and CI-ABC for optimal performance are presented and analyzed. The results clearly reveal the efficacy of proposed algorithms when applied to the underlying problems. --Abstract, page iv

Knowledge management overview of feature selection problem in high-dimensional financial data: Cooperative co-evolution and Map Reduce perspectives

The term big data characterizes the massive amounts of data generation by the advanced technologies in different domains using 4Vs volume, velocity, variety, and veracity-to indicate the amount of data that can only be processed via computationally intensive analysis, the speed of their creation, the different types of data, and their accuracy. High-dimensional financial data, such as time-series and space-Time data, contain a large number of features (variables) while having a small number of samples, which are used to measure various real-Time business situations for financial organizations. Such datasets are normally noisy, and complex correlations may exist between their features, and many domains, including financial, lack the al analytic tools to mine the data for knowledge discovery because of the high-dimensionality. Feature selection is an optimization problem to find a minimal subset of relevant features that maximizes the classification accuracy and reduces the computations. Traditional statistical-based feature selection approaches are not adequate to deal with the curse of dimensionality associated with big data. Cooperative co-evolution, a meta-heuristic algorithm and a divide-And-conquer approach, decomposes high-dimensional problems into smaller sub-problems. Further, MapReduce, a programming model, offers a ready-To-use distributed, scalable, and fault-Tolerant infrastructure for parallelizing the developed algorithm. This article presents a knowledge management overview of evolutionary feature selection approaches, state-of-The-Art cooperative co-evolution and MapReduce-based feature selection techniques, and future research directions

Optimization of attribute selection model using bio-inspired algorithms

Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. Hence, reducing the consistency of the accuracy rate. The aim of this paper is to investigate the use of bio-inspired search algorithms in producing optimal attribute set. This is achieved in two stages; 1) create attribute selection models by combining search method and feature selection algorithms, and 2) determine an optimized attribute set by employing bio-inspired algorithms.Classification performance of the produced attribute set is analyzed based on accuracy and number of selected attributes. Experimental results conducted on six (6) public real datasets reveal that the feature selection model with the implementation of bio-inspired search algorithm consistently performs good classification (i.e higher accuracy with fewer numbers of attributes) on the selected data set. Such a finding indicates that bio-inspired algorithms can contribute in identifying the few most important features to be used in data mining model construction

A memory-integrated artificial bee algorithm for heuristic optimisation

A thesis submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Master of Science by ResearchAccording to studies about bee swarms, they use special techniques for foraging and they are always able to find notified food sources with exact coordinates. In order to succeed in food source exploration, the information about food sources is transferred between employed bees and onlooker bees via waggle dance. In this study, bee colony behaviours are imitated for further search in one of the common real world problems. Traditional solution techniques from literature may not obtain sufficient results; therefore other techniques have become essential for food source exploration. In this study, artificial bee colony (ABC) algorithm is used as a base to fulfil this purpose. When employed and onlooker bees are searching for better food sources, they just memorize the current sources and if they find better one, they erase the all information about the previous best food source. In this case, worker bees may visit same food source repeatedly and this circumstance causes a hill climbing in search. The purpose of this study is exploring how to embed a memory system in ABC algorithm to avoid mentioned repetition. In order to fulfil this intention, a structure of Tabu Search method -Tabu List- is applied to develop a memory system. In this study, we expect that a memory system embedded ABC algorithm provides a further search in feasible area to obtain global optimum or obtain better results in comparison with classic ABC algorithm. Results show that, memory idea needs to be improved to fulfil the purpose of this study. On the other hand, proposed memory idea can be integrated other algorithms or problem types to observe difference

Shadow Price Guided Genetic Algorithms

The Genetic Algorithm (GA) is a popular global search algorithm. Although it has been used successfully in many fields, there are still performance challenges that prevent GA’s further success. The performance challenges include: difficult to reach optimal solutions for complex problems and take a very long time to solve difficult problems. This dissertation is to research new ways to improve GA’s performance on solution quality and convergence speed. The main focus is to present the concept of shadow price and propose a two-measurement GA. The new algorithm uses the fitness value to measure solutions and shadow price to evaluate components. New shadow price Guided operators are used to achieve good measurable evolutions. Simulation results have shown that the new shadow price Guided genetic algorithm (SGA) is effective in terms of performance and efficient in terms of speed