Multi-population-based differential evolution algorithm for optimization problems

A differential evolution (DE) algorithm is an evolutionary algorithm for optimization problems over a continuous domain. To solve high dimensional global optimization problems, this work investigates the performance of differential evolution algorithms under a multi-population strategy. The original DE algorithm generates an initial set of suitable solutions. The multi-population strategy divides the set into several subsets. These subsets evolve independently and connect with each other according to the DE algorithm. This helps in preserving the diversity of the initial set. Furthermore, a comparison of combination of different mutation techniques on several optimization algorithms is studied to verify their performance. Finally, the computational results on the arbitrarily generated experiments, reveal some interesting relationship between the number of subpopulations and performance of the DE. Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. In this problem, the above algorithm is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed algorithm is one of effective and promising methods for optimal EV centralized charging

The SOS Platform: Designing, Tuning and Statistically Benchmarking Optimisation Algorithms

open access articleWe present Stochastic Optimisation Software (SOS), a Java platform facilitating the algorithmic design process and the evaluation of metaheuristic optimisation algorithms. SOS reduces the burden of coding miscellaneous methods for dealing with several bothersome and time-demanding tasks such as parameter tuning, implementation of comparison algorithms and testbed problems, collecting and processing data to display results, measuring algorithmic overhead, etc. SOS provides numerous off-the-shelf methods including: (1) customised implementations of statistical tests, such as the Wilcoxon rank-sum test and the Holm–Bonferroni procedure, for comparing the performances of optimisation algorithms and automatically generating result tables in PDF and formats; (2) the implementation of an original advanced statistical routine for accurately comparing couples of stochastic optimisation algorithms; (3) the implementation of a novel testbed suite for continuous optimisation, derived from the IEEE CEC 2014 benchmark, allowing for controlled activation of the rotation on each testbed function. Moreover, we briefly comment on the current state of the literature in stochastic optimisation and highlight similarities shared by modern metaheuristics inspired by nature. We argue that the vast majority of these algorithms are simply a reformulation of the same methods and that metaheuristics for optimisation should be simply treated as stochastic processes with less emphasis on the inspiring metaphor behind them

MAT: Genetic Algorithms Based Multi-Objective Adversarial Attack on Multi-Task Deep Neural Networks

Vulnerability to adversarial attacks is a recognized deficiency of not only deep neural networks (DNNs) but also multi-task deep neural networks (MT-DNNs) that attracted much attention in the past few years. To the best of our knowledge, all multi-task deep neural network adversarial attacks currently present in the literature are non-targeted attacks that use gradient descent to optimize a single loss function generated by aggregating all loss functions into one. On the contrary, targeted attacks are sometimes preferred since they give more control over the attack. Hence, this paper proposes a novel targeted multi-objective adversarial ATtack (MAT) based on genetic algorithms (GA)s that can create an adversarial image capable of affecting only targeted loss functions of the MT-DNN system. MAT is trained on the Taskonomy dataset using a novel training algorithm GAMAT that consists of five specific stages. The superiority of the proposed attack is demonstrated in terms of the fitness-distance metric --Abstract, p. i

Enhanced Version of Multi-algorithm Genetically Adaptive for Multiobjective optimization

Abstract: Multi-objective EAs (MOEAs) are well established population-based techniques for solving various search and optimization problems. MOEAs employ different evolutionary operators to evolve populations of solutions for approximating the set of optimal solutions of the problem at hand in a single simulation run. Different evolutionary operators suite different problems. The use of multiple operators with a self-adaptive capability can further improve the performance of existing MOEAs. This paper suggests an enhanced version of a genetically adaptive multi-algorithm for multi-objective (AMAL-GAM) optimisation which includes differential evolution (DE), particle swarm optimization (PSO), simulated binary crossover (SBX), Pareto archive evolution strategy (PAES) and simplex crossover (SPX) for population evolution during the course of optimization. We examine the performance of this enhanced version of AMALGAM experimentally over two different test suites, the ZDT test problems and the test instances designed recently for the special session on MOEA?s competition at the Congress of Evolutionary Computing of 2009 (CEC?09). The suggested algorithm has found better approximate solutions on most test problems in terms of inverted generational distance (IGD) as the metric indicator. - See more at: http://thesai.org/Publications/ViewPaper?Volume=6&Issue=12&Code=ijacsa&SerialNo=37#sthash.lxkuyzEf.dpu

Enhanced Deep Network Designs Using Mitochondrial DNA Based Genetic Algorithm And Importance Sampling

Machine learning (ML) is playing an increasingly important role in our lives. It has already made huge impact in areas such as cancer diagnosis, precision medicine, self-driving cars, natural disasters predictions, speech recognition, etc. The painstakingly handcrafted feature extractors used in the traditional learning, classification and pattern recognition systems are not scalable for large-sized datasets or adaptable to different classes of problems or domains. Machine learning resurgence in the form of Deep Learning (DL) in the last decade after multiple AI (artificial intelligence) winters and hype cycles is a result of the convergence of advancements in training algorithms, availability of massive data (big data) and innovation in compute resources (GPUs and cloud). If we want to solve more complex problems with machine learning, we need to optimize all three of these areas, i.e., algorithms, dataset and compute. Our dissertation research work presents the original application of nature-inspired idea of mitochondrial DNA (mtDNA) to improve deep learning network design. Additional fine-tuning is provided with Monte Carlo based method called importance sampling (IS). The primary performance indicators for machine learning are model accuracy, loss and training time. The goal of our dissertation is to provide a framework to address all these areas by optimizing network designs (in the form of hyperparameter optimization) and dataset using enhanced Genetic Algorithm (GA) and importance sampling. Algorithms are by far the most important aspect of machine learning. We demonstrate the application of mitochondrial DNA to complement the standard genetic algorithm for architecture optimization of deep Convolution Neural Network (CNN). We use importance sampling to reduce the dataset variance and sample more often from the instances that add greater value from the training outcome perspective. And finally, we leverage massive parallel and distributed processing of GPUs in the cloud to speed up training. Thus, our multi-approach method for enhancing deep learning combines architecture optimization, dataset optimization and the power of the cloud to drive better model accuracy and reduce training time

Energy Management Systems for Smart Electric Railway Networks: A Methodological Review

Energy shortage is one of the major concerns in today’s world. As a consumer of electrical energy, the electric railway system (ERS), due to trains, stations, and commercial users, intakes an enormous amount of electricity. Increasing greenhouse gases (GHG) and CO2 emissions, in addition, have drawn the regard of world leaders as among the most dangerous threats at present; based on research in this field, the transportation sector contributes significantly to this pollution. Railway Energy Management Systems (REMS) are a modern green solution that not only tackle these problems but also, by implementing REMS, electricity can be sold to the grid market. Researchers have been trying to reduce the daily operational costs of smart railway stations, mitigating power quality issues, considering the traction uncertainties and stochastic behavior of Renewable Energy Resources (RERs) and Energy Storage Systems (ESSs), which has a significant impact on total operational cost. In this context, the first main objective of this article is to take a comprehensive review of the literature on REMS and examine closely all the works that have been carried out in this area, and also the REMS architecture and configurations are clarified as well. The secondary objective of this article is to analyze both traditional and modern methods utilized in REMS and conduct a thorough comparison of them. In order to provide a comprehensive analysis in this field, over 120 publications have been compiled, listed, and categorized. The study highlights the potential of leveraging RERs for cost reduction and sustainability. Evaluating factors including speed, simplicity, efficiency, accuracy, and ability to handle stochastic behavior and constraints, the strengths and limitations of each optimization method are elucidated

Genomic insights into fine-scale recombination variation in adaptively diverging threespine stickleback fish (Gasterosteus aculeatus)

Meiotic recombination is one of the major molecular mechanisms generating genetic diversity and influencing genome evolution. By shuffling allelic combinations, it can directly influence the patterns and efficacy of natural selection. Studies in various organisms have shown that the rate and placement of recombination varies substantially within the genome, among individuals, between sexes and among different species. It is hypothesized that this variation plays an important role in genome evolution. In this PhD thesis, I investigated the extent and molecular basis of recombination variation in adaptively diverging threespine stickleback fish (Gasterosteus aculeatus) to further understand its evolutionary implications. I used both ChIP-sequencing and whole genome sequencing of pedigrees to empirically identify and quantify double strand breaks (DSBs) and meiotic crossovers (COs). Whole genome sequencing of large nuclear families was performed to identify meiotic crossovers in 36 individuals of diverging marine and freshwater ecotypes and their hybrids. This produced the first genome-wide high-resolution sex-specific and ecotype-specific map of contemporary recombination events in sticklebacks. The results show striking differences in crossover number and placement between sexes. Females recombine nearly 1.76 times more than males and their COs are distributed all over the chromosome while male COs predominantly occur near the chromosomal periphery. When compared among ecotypes a significant reduction in overall recombination rate was observed in hybrid females compared to pure forms. Even though the known loci underlying marine-freshwater adaptive divergence tend to fall in regions of low recombination, considerable female recombination is observed in the regions between adaptive loci. This suggests that the sexual dimorphism in recombination phenotype may have important evolutionary implications. At the fine-scale, COs and male DSBs are nonrandomly distributed involving ‘semi-hot’ hotspots and coldspots of recombination. I report a significant association of male DSBs and COs with functionally active open chromatin regions like gene promoters, whereas female COs did not show an association more than expected by chance. However, a considerable number of COs and DSBs away from any of the tested open chromatin marks suggests possibility of additional novel mechanisms of recombination regulation in sticklebacks. In addition, we developed a novel method for constructing individualized recombination maps from pooled gamete DNA using linked read sequencing technology by 10X Genomics®. We tested the method by contrasting recombination profiles of gametic and somatic tissue from a hybrid mouse and stickleback fish. Our pipeline faithfully detects previously described recombination hotspots in mice at high resolution and identify many novel hotspots across the genome in both species and thereby demonstrate the efficiency of the novel method. This method could be employed for large scale QTL mapping studies to further understand the genetic basis of recombination variation reported in this thesis. By bridging the gap between natural populations and lab organisms with large clutch sizes and tractable genetic tools, this work shows the utility of the stickleback system and provides important groundwork for further studies of heterochiasmy and divergence in recombination during adaptation to differing environments

Network Algorithms for Complex Systems with Applications to Non-linear Oscillators and Genome Assembly

Network and complex system models are useful for studying a wide range of phenomena, from disease spread to traffic flow. Because of the broad applicability of the framework it is important to develop effective simulations and algorithms for complex networks. This dissertation presents contributions to two applied problems in this area First, we study an electro-optical, nonlinear, and time-delayed feedback loop commonly used in applications that require a broad range of chaotic behavior. For this system we detail a discrete-time simulation model, exploring the model's synchronization behavior under specific coupling conditions. Expanding upon already published results that investigated changes in feedback strength, we explore how both time-delay and nonlinear sensitivity impact synchronization. We also relax the requirement of strictly identical systems components to study how synchronization regions are affected when coupled systems have non-identical components (parameters). Last, we allow wider variance in coupling strengths, including unique strengths to each system, to identify a rich synchronization region not previously seen. In our second application, we take a complex networks approach to improving genome assembly algorithms. One key part of sequencing a genome is solving the orientation problem. The orientation problem is finding the relative orientations for each data fragment generated during sequencing. By viewing the genomic data as a network we can apply standard analysis techniques for community finding and utilize the significantly modular structure of the data. This structure informs development and application of two new heuristics based on (A) genetic algorithms and (B) hierarchical clustering for solving the orientation problem. Genetic algorithms allow us to preserve some internal structure while quickly exploring a large solution space. We present studies using a multi-scale genetic algorithm to solve the orientation problem. We show that this approach can be used in conjunction with currently used methods to identify a better solution to the orientation problem. Our hierarchical algorithm further utilizes the modular structure of the data. By progressively solving and merging sub-problems together we pick optimal `local' solutions while allowing more global corrections to occur later. Our results show significant improvements over current techniques for both generated data and real assembly data