Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations

The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves the adaptation schemes for the normalized taboo distances of the archived solutions and the covariance matrix of the subpopulation, the termination criteria for the subpopulations, and the way in which the infeasible solutions are treated. It also improves the time complexity of RS-CMSA-ES by updating the initialization procedure of a subpopulation and developing a more accurate metric for determining critical taboo regions. The effects of these modifications are illustrated by designing controlled numerical simulations. RS-CMSA-ESII is then compared with the most successful and recent niching methods for MMO on a widely adopted test suite. The results obtained reveal the superiority of RS-CMSA-ESII over these methods, including the winners of the competition on niching methods for MMO in previous years. Besides, this study extends RS-CMSA-ESII to dynamic MMO and compares it with a few recently proposed methods on the modified moving peak benchmark functions

A Survey of Evolutionary Continuous Dynamic Optimization Over Two Decades:Part B

Many real-world optimization problems are dynamic. The field of dynamic optimization deals with such problems where the search space changes over time. In this two-part paper, we present a comprehensive survey of the research in evolutionary dynamic optimization for single-objective unconstrained continuous problems over the last two decades. In Part A of this survey, we propose a new taxonomy for the components of dynamic optimization algorithms, namely, convergence detection, change detection, explicit archiving, diversity control, and population division and management. In comparison to the existing taxonomies, the proposed taxonomy covers some additional important components, such as convergence detection and computational resource allocation. Moreover, we significantly expand and improve the classifications of diversity control and multi-population methods, which are under-represented in the existing taxonomies. We then provide detailed technical descriptions and analysis of different components according to the suggested taxonomy. Part B of this survey provides an indepth analysis of the most commonly used benchmark problems, performance analysis methods, static optimization algorithms used as the optimization components in the dynamic optimization algorithms, and dynamic real-world applications. Finally, several opportunities for future work are pointed out

Uncertainty evaluation of reservoir simulation models using particle swarms and hierarchical clustering

History matching production data in finite difference reservoir simulation models has been and always will be a challenge for the industry. The principal hurdles that need to be overcome are finding a match in the first place and more importantly a set of matches that can capture the uncertainty range of the simulation model and to do this in as short a time as possible since the bottleneck in this process is the length of time taken to run the model. This study looks at the implementation of Particle Swarm Optimisation (PSO) in history matching finite difference simulation models. Particle Swarms are a class of evolutionary algorithms that have shown much promise over the last decade. This method draws parallels from the social interaction of swarms of bees, flocks of birds and shoals of fish. Essentially a swarm of agents are allowed to search the solution hyperspace keeping in memory each individual’s historical best position and iteratively improving the optimisation by the emergent interaction of the swarm. An intrinsic feature of PSO is its local search capability. A sequential niching variation of the PSO has been developed viz. Flexi-PSO that enhances the exploration and exploitation of the hyperspace and is capable of finding multiple minima. This new variation has been applied to history matching synthetic reservoir simulation models to find multiple distinct history 3 matches to try to capture the uncertainty range. Hierarchical clustering is then used to post-process the history match runs to reduce the size of the ensemble carried forward for prediction. The success of the uncertainty modelling exercise is then assessed by checking whether the production profile forecasts generated by the ensemble covers the truth case

Stochastic and deterministic algorithms for continuous black-box optimization

Continuous optimization is never easy: the exact solution is always a luxury demand and the theory of it is not always analytical and elegant. Continuous optimization, in practice, is essentially about the efficiency: how to obtain the solution with same quality using as minimal resources (e.g., CPU time or memory usage) as possible? In this thesis, the number of function evaluations is considered as the most important resource to save. To achieve this goal, various efforts have been implemented and applied successfully. One research stream focuses on the so-called stochastic variation (mutation) operator, which conducts an (local) exploration of the search space. The efficiency of those operator has been investigated closely, which shows a good stochastic variation should be able to generate a good coverage of the local neighbourhood around the current search solution. This thesis contributes on this issue by formulating a novel stochastic variation that yields good space coverage. Algorithms and the Foundations of Software technolog

Mining a Small Medical Data Set by Integrating the Decision Tree and t-test

[[abstract]]Although several researchers have used statistical methods to prove that aspiration followed by the injection of 95% ethanol left in situ (retention) is an effective treatment for ovarian endometriomas, very few discuss the different conditions that could generate different recovery rates for the patients. Therefore, this study adopts the statistical method and decision tree techniques together to analyze the postoperative status of ovarian endometriosis patients under different conditions. Since our collected data set is small, containing only 212 records, we use all of these data as the training data. Therefore, instead of using a resultant tree to generate rules directly, we use the value of each node as a cut point to generate all possible rules from the tree first. Then, using t-test, we verify the rules to discover some useful description rules after all possible rules from the tree have been generated. Experimental results show that our approach can find some new interesting knowledge about recurrent ovarian endometriomas under different conditions.[[journaltype]]國外[[incitationindex]]EI[[booktype]]紙本[[countrycodes]]FI

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Palm tree detection in UAV images: a hybrid approach based on multimodal particle swarm optimisation

In recent years, there has been a surge of interest in palm tree detection using unmanned aerial vehicle (UAV) images, with implications for sustainability, productivity, and profitability. Similar to other object detection problems in the field of computer vision, palm tree detection typically involves classifying palm trees from non-palm tree objects or background and localising every palm tree instance in an image. Palm tree detection in large-scale high-resolution UAV images is challenging due to the large number of pixels that need to be visited by the object detector, which is computationally costly. In this thesis, we design a novel hybrid approach based on multimodal particle swarm optimisation (MPSO) algorithm that can speed up the localisation process whilst maintaining optimal accuracy for palm tree detection in UAV images. The proposed method uses a feature-extraction-based classifier as the MPSO's objective function to seek multiple positions and scales in an image that maximise the detection score. The feature-extraction-based classifier was carefully selected through empirical study and was proven seven times faster than the state-of-the-art convolutional neural network (CNN) with comparable accuracy. The research goes on with the development of a new k-d tree-structured MPSO algorithm, which is called KDT-SPSO that significantly speeds up MPSO's nearest neighbour search by only exploring the subspaces that most likely contain the query point's neighbours. KDT-SPSO was demonstrated effective in solving multimodal benchmark functions and outperformed other competitors when applied on UAV images. Finally, we devise a new approach that utilises a 3D digital surface model (DSM) to generate high confidence proposals for KDT-SPSO and existing region-based CNN (R-CNN) for palm tree detection. The use of DSM as prior information about the number and location of palm trees reduces the search space within images and decreases overall computation time. Our hybrid approach can be executed in non-specialised hardware without long training hours, achieving similar accuracy as the state-of-the-art R-CNN

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Palm tree detection in UAV images: a hybrid approach based on multimodal particle swarm optimisation

In recent years, there has been a surge of interest in palm tree detection using unmanned aerial vehicle (UAV) images, with implications for sustainability, productivity, and profitability. Similar to other object detection problems in the field of computer vision, palm tree detection typically involves classifying palm trees from non-palm tree objects or background and localising every palm tree instance in an image. Palm tree detection in large-scale high-resolution UAV images is challenging due to the large number of pixels that need to be visited by the object detector, which is computationally costly. In this thesis, we design a novel hybrid approach based on multimodal particle swarm optimisation (MPSO) algorithm that can speed up the localisation process whilst maintaining optimal accuracy for palm tree detection in UAV images. The proposed method uses a feature-extraction-based classifier as the MPSO's objective function to seek multiple positions and scales in an image that maximise the detection score. The feature-extraction-based classifier was carefully selected through empirical study and was proven seven times faster than the state-of-the-art convolutional neural network (CNN) with comparable accuracy. The research goes on with the development of a new k-d tree-structured MPSO algorithm, which is called KDT-SPSO that significantly speeds up MPSO's nearest neighbour search by only exploring the subspaces that most likely contain the query point's neighbours. KDT-SPSO was demonstrated effective in solving multimodal benchmark functions and outperformed other competitors when applied on UAV images. Finally, we devise a new approach that utilises a 3D digital surface model (DSM) to generate high confidence proposals for KDT-SPSO and existing region-based CNN (R-CNN) for palm tree detection. The use of DSM as prior information about the number and location of palm trees reduces the search space within images and decreases overall computation time. Our hybrid approach can be executed in non-specialised hardware without long training hours, achieving similar accuracy as the state-of-the-art R-CNN

Metaheuristic Optimization Techniques for Articulated Human Tracking

Four adaptive metaheuristic optimization algorithms are proposed and demonstrated: Adaptive Parameter Particle Swarm Optimization (AP-PSO), Modified Artificial Bat (MAB), Differential Mutated Artificial Immune System (DM-AIS) and hybrid Particle Swarm Accelerated Artificial Immune System (PSO-AIS). The algorithms adapt their search parameters on the basis of the fitness of obtained solutions such that a good fitness value favors local search, while a poor fitness value favors global search. This efficient feedback of the solution quality, imparts excellent global and local search characteristic to the proposed algorithms. The algorithms are tested on the challenging Articulated Human Tracking (AHT) problem whose objective is to infer human pose, expressed in terms of joint angles, from a continuous video stream. The Particle Filter (PF) algorithms, widely applied in generative model based AHT, suffer from the 'curse of dimensionality' and 'degeneracy' challenges. The four proposed algorithms show stable performance throughout the course of numerical experiments. DM-AIS performs best among the proposed algorithms followed in order by PSO-AIS, AP-PSO, and MBA in terms of Most Appropriate Pose (MAP) tracking error. The MAP tracking error of the proposed algorithms is compared with four heuristic approaches: generic PF, Annealed Particle Filter (APF), Partitioned Sampled Annealed Particle Filter (PSAPF) and Hierarchical Particle Swarm Optimization (HPSO). They are found to outperform generic PF with a confidence level of 95%, PSAPF and HPSO with a confidence level of 85%. While DM-AIS and PSO-AIS outperform APF with a confidence level of 80%. Further, it is noted that the proposed algorithms outperform PSAPF and HPSO using a significantly lower number of function evaluations, 2500 versus 7200. The proposed algorithms demonstrate reduced particle requirements, hence improving computational efficiency and helping to alleviate the 'curse of dimensionality'. The adaptive nature of the algorithms is found to guide the whole swarm towards the optimal solution by sharing information and exploring a wider solution space which resolves the 'degeneracy' challenge. Furthermore, the decentralized structure of the algorithms renders them insensitive to accumulation of error and allows them to recover from catastrophic failures due to loss of image data, sudden change in motion pattern or discrete instances of algorithmic failure. The performance enhancements demonstrated by the proposed algorithms, attributed to their balanced local and global search capabilities, makes real-time AHT applications feasible. Finally, the utility of the proposed algorithms in low-dimensional system identification problems as well as high-dimensional AHT problems demonstrates their applicability in various problem domains