Solving Many-Objective Car Sequencing Problems on Two-Sided Assembly Lines Using an Adaptive Differential Evolutionary Algorithm

The car sequencing problem (CSP) is addressed in this paper. The original environment of the CSP is modified to reflect real practices in the automotive industry by replacing the use of single-sided straight assembly lines with two-sided assembly lines. As a result, the problem becomes more complex caused by many additional constraints to be considered. Six objectives (i.e. many objectives) are optimised simultaneously including minimising the number of colour changes, minimising utility work, minimising total idle time, minimising the total number of ratio constraint violations and minimising total production rate variation. The algorithm namely adaptive multi-objective evolutionary algorithm based on decomposition hybridised with differential evolution algorithm (AMOEA/D-DE) is developed to tackle this problem. The performances in Pareto sense of AMOEA/D-DE are compared with COIN-E, MODE, MODE/D and MOEA/D. The results indicate that AMOEA/D-DE outperforms the others in terms of convergence-related metrics

Optimal Ship Maintenance Scheduling Under Restricted Conditions and Constrained Resources

The research presented in this dissertation addresses the application of evolution algorithms, i.e. Genetic Algorithm (GA) and Differential Evolution algorithm (DE) to scheduling problems in the presence of restricted conditions and resource limitations. This research is motivated by the scheduling of engineering design tasks in shop scheduling problems and ship maintenance scheduling problems to minimize total completion time. The thesis consists of two major parts; the first corresponds to the first appended paper and deals with the computational complexity of mixed shop scheduling problems. A modified Genetic algorithm is proposed to solve the problem. Computational experiments, conducted to evaluate its performance against known optimal solutions for different sized problems, show its superiority in computation time and the high applicability in practical mixed shop scheduling problems. The second part considers the major theme in the second appended paper and is related to the ship maintenance scheduling problem and the extended research on the multi-mode resource-constrained ship scheduling problem. A heuristic Differential Evolution is developed and applied to solve these problems. A mathematical optimization model is also formulated for the multi-mode resource-constrained ship scheduling problem. Through the computed results, DE proves its effectiveness and efficiency in addressing both single and multi-objective ship maintenance scheduling problem

Composite Differential Evolution for Constrained Evolutionary Optimization

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the ϵ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods

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 species-area relationship and evolution

Models relating to the Species-Area curve are usually defined at the species level, and concerned only with ecological timescales. We examine an individual-based model of co-evolution on a spatial lattice based on the Tangled Nature model, and show that reproduction, mutation and dispersion by diffusion in an interacting system produces power-law Species-Area Relations as observed in ecological measurements at medium scales. We find that co-evolutionary habitats form, allowing high diversity levels in a spatially homogenous system, and these are maintained for exponentially increasing time when increasing system size.Comment: 21 pages, 5 figures. This is the final, accepted draf

Genome engineering in the study of APOBEC3 gene function

The “apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like” (APOBEC) gene family defines a set of related Cytidine Deaminases. The best characterised of these are ones that function in the process of mRNA editing. Members of the family are also known to use DNA as their preferred substrate, acting in a consensus sequence, and mediating Cytidine deamination leading to a C to T transition. Recently, this process has been recognised as a major mutational mechanism in several cancer types, where it has been further suggested that members of the APOBEC3 family, and potentially APOBEC3H, that cause this. In order to understand the role of APOBEC3H further, we have used CRISPR-Cas9 gene editing to show that APOBEC3H is a p53 regulated target gene. This has involved making a p53 knockout version of the MCF7 breast cancer cell line, using CRISPR-Cas9 to delete part of the third coding exon of the gene. With this line, it has been possible to confirm that APOBEC3H expression is dependent on p53. In further work, CRISPR-Cas9 was used to make an APOBEC3H knockout in the HCT116 colon cancer line. Having established two independent APOBEC3H knockout lines in HCT116, RNAseq analysis was carried out to compare gene expression of mutant and wild-type cells in both the control and p53 activated setting. Analysis of the RNAseq data has shown that knockout of APOBE3H leads to an attenuated p53 response, suggesting the possibility that APOBEC 3H is co-regulator of p53 regulated gene expression. This is further indicated by the demonstration that over-expression of APOBEC3H in HCT116 wild-type cells enhances the expression of p53 target genes following p53 activation. Taken together, these data identify APOBEC3H as a p53 regulated gene, with a potential role in modulating the p53 response, and potentially identify a new mechanism for APOBEC3H action in cancer.Open Acces

Non-lethal exposure to H2O2 boosts bacterial survival and evolvability against oxidative stress

Unicellular organisms have the prevalent challenge to survive under oxidative stress of reactive oxygen species (ROS) such as hydrogen peroxide (H2O2). ROS are present as by-products of photosynthesis and aerobic respiration. These reactive species are even employed by multicellular organisms as potent weapons against microbes. Although bacterial defences against lethal and sub-lethal oxidative stress have been studied in model bacteria, the role of fluctuating H2O2 concentrations remains unexplored. It is known that sub-lethal exposure of Escherichia coli to H2O2 results in enhanced survival upon subsequent exposure. Here we investigate the priming response to H2O2 at physiological concentrations. The basis and the duration of the response (memory) were also determined by time-lapse quantitative proteomics. We found that a low level of H2O2 induced several scavenging enzymes showing a long half-life, subsequently protecting cells from future exposure. We then asked if the phenotypic resistance against H2O2 alters the evolution of resistance against oxygen stress. Experimental evolution of H2O2 resistance revealed faster evolution and higher levels of resistance in primed cells. Several mutations were found to be associated with resistance in evolved populations affecting different loci but, counterintuitively, none of them was directly associated with scavenging systems. Our results have important implications for host colonisation and infections where microbes often encounter reactive oxygen species in gradients