Advances in global optimization of high brightness beams

High brightness electron beams play an important role in accelerator-based applications such as driving X-ray free electron laser (FEL) radiation. In this paper, we report on advances in global beam dynamics optimization of an accelerator design using start-to-end simulations and a new parallel multi-objective differential evolution optimization method. The global optimization results in significant improvement of the final electron beam brightness

Multi-objective routing optimization using evolutionary algorithms

Wireless ad hoc networks suffer from several limitations, such as routing failures, potentially excessive bandwidth requirements, computational constraints and limited storage capability. Their routing strategy plays a significant role in determining the overall performance of the multi-hop network. However, in conventional network design only one of the desired routing-related objectives is optimized, while other objectives are typically assumed to be the constraints imposed on the problem. In this paper, we invoke the Non-dominated Sorting based Genetic Algorithm-II (NSGA-II) and the MultiObjective Differential Evolution (MODE) algorithm for finding optimal routes from a given source to a given destination in the face of conflicting design objectives, such as the dissipated energy and the end-to-end delay in a fully-connected arbitrary multi-hop network. Our simulation results show that both the NSGA-II and MODE algorithms are efficient in solving these routing problems and are capable of finding the Pareto-optimal solutions at lower complexity than the ’brute-force’ exhaustive search, when the number of nodes is higher than or equal to 10. Additionally, we demonstrate that at the same complexity, the MODE algorithm is capable of finding solutions closer to the Pareto front and typically, converges faster than the NSGA-II algorithm

Differential Evolution Algorithm based Hyper-Parameters Selection of Transformer Neural Network Model for Load Forecasting

Accurate load forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of dynamic power systems remains a challenge for traditional statistical models. For these reasons, time-series models (ARIMA) and deep-learning models (ANN, LSTM, GRU, etc.) are commonly deployed and often experience higher success. In this paper, we analyze the efficacy of the recently developed Transformer-based Neural Network model in Load forecasting. Transformer models have the potential to improve Load forecasting because of their ability to learn long-range dependencies derived from their Attention Mechanism. We apply several metaheuristics namely Differential Evolution to find the optimal hyperparameters of the Transformer-based Neural Network to produce accurate forecasts. Differential Evolution provides scalable, robust, global solutions to non-differentiable, multi-objective, or constrained optimization problems. Our work compares the proposed Transformer based Neural Network model integrated with different metaheuristic algorithms by their performance in Load forecasting based on numerical metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). Our findings demonstrate the potential of metaheuristic-enhanced Transformer-based Neural Network models in Load forecasting accuracy and provide optimal hyperparameters for each model.Comment: 6 Pages, 6 Figures, 2 Table

Multi-tissue RNA-Seq Analysis and Long-read-based Genome Assembly Reveal Complex Sex-specific Gene Regulation and Molecular Evolution in the Manila Clam

The molecular factors and gene regulation involved in sex determination and gonad differentiation in bivalve molluscs are unknown. It has been suggested that doubly uniparental inheritance (DUI) of mitochondria may be involved in these processes in species such as the ubiquitous and commercially relevant Manila clam, Ruditapes philippinarum. We present the first long-read-based de novo genome assembly of a Manila clam, and a RNA-Seq multi-tissue analysis of 15 females and 15 males. The highly contiguous genome assembly was used as reference to investigate gene expression, alternative splicing, sequence evolution, tissue-specific co-expression networks, and sexual contrasting SNPs. Differential expression (DE) and differential splicing (DS) analyses revealed sex-specific transcriptional regulation in gonads, but not in somatic tissues. Co-expression networks revealed complex gene regulation in gonads, and genes in gonad-associated modules showed high tissue specificity. However, male gonad-associated modules showed contrasting patterns of sequence evolution and tissue specificity. One gene set was related to the structural organization of male gametes and presented slow sequence evolution but high pleiotropy, whereas another gene set was enriched in reproduction-related processes and characterized by fast sequence evolution and tissue specificity. Sexual contrasting SNPs were found in genes overrepresented in mitochondrial-related functions, providing new candidates for investigating the relationship between mitochondria and sex in DUI species. Together, these results increase our understanding of the role of DE, DS, and sequence evolution of sex-specific genes in an understudied taxon. We also provide resourceful genomic data for studies regarding sex diagnosis and breeding in bivalves

Free Search of real value or how to make computers think

This book introduces in detail Free Search - a novel advanced method for search and optimisation. It also deals with some essential questions that have been raised in a strong debate following the publication of this method in journal and conference papers. In the light of this debate, Free Search deserves serious attention, as it appears to be superior to other competitive methods in the context of the experimental results obtained. This superiority is not only quantitative in terms of the actual optimal value found but also qualitative in terms of independence from initial conditions and adaptation capabilities in an unknown environment