Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Improving the performance of deep learning techniques using nature inspired algorithms and applying them in porosity prediction

Within the field of Artificial Intelligence (AI), Deep Learning (DL) based on Convolutional Neural Network (CNN) can be used for analysing images. However, the performance of the DL models depends on the design of the CNN topology to achieve their best performance. Hence, firstly, this work addresses this problem by proposing a novel nature inspired hybrid algorithm called BA-CNN where a swarm based Bees Algorithm (BA) is used to optimize the CNN parameters. In addition, another algorithm called BABO-CNN is proposed that combines the BA with Bayesian Optimization (BO) to increase the CNN performance and that of BA-CNN and BO-CNN. This study shows that applying the hybrid BA-CNN to the ‘Cifar10DataDir’ benchmark image did not improve the validation and testing accuracy compared to the existing CNN and BO-CNN. However, the hybrid BA-BO-CNN achieved better validation accuracy of 82.22% compared to 80.34% and 80.72% for the CNN and BO-CNN, and also with a better testing accuracy of 80.74% compared to 80.54% and 80.69% for the CNN and BO-CNN respectively. The BA-BOCNN achieved lower computational time than the BO-CNN algorithm by 2 minutes and 11 seconds. Although applying both algorithms to the ‘digits’ dataset produced almost similar accuracies with a difference of 0.01% between BA-CNN and BO-CNN, the BA-CNN achieved a computational time reduction of 4 minutes and 14 seconds compared to the BOCNN, making it the best algorithm in terms of cost-effectiveness. Applying BA-CNN and BA-BO-CNN to identify ‘concrete cracks’ images produced almost similar results to some of the other existing algorithms with a difference of 0.02% between BA-CNN and original CNN. Finally, applying them to the ‘ECG’ images improved the testing accuracy from 90% for the BO-CNN to 92.50% for the BA-CNN and 95% for the BA-BO-CNN with a similar trend for validation accuracy and computational time. Secondly, the CNN that was adopted for the purpose of regression which is called RCNN was applied in the manufacturing context, particularly to predict the percent of porosity in the finished Selective Laser Melting (SLM) parts. Because testing the performance of the RCNN algorithm requires a large amount of experimental data which is generally difficult to obtain, in this study an artificial porosity image creation method is proposed where 3000 artificial porosity images were created mimicking real CT scan slices of the SLM part with a similarity index of 0.9976. Applying the RCNN to the 3000 artificial ii porosity images slices showed the porosity prediction accuracy to improve from 68.60% for the image binarization method to 75.50% for the RCNN, while the proposed novel hybrid BA-BO-RCNN and BA-RCNN yielded better prediction accuracies of 83% and 85.33% respectively. Thirdly, in order to improve the performance even further, this study proposes to add Long Short Term Memory (LSTM) to BA-CNN because of their ability to deal with sequential data to produce another novel hybrid algorithm called BA-CNN-LSTM and the results showed an increase in the prediction accuracy reaching 95.50

Pattern Recognition

A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC