Simulation-Based Optimisation for Autonomous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation

This work presents a novel approach to the simulation-based optimisation for Autonomous Transportation Systems (ATS) with the use of the proposed parallel genetic algorithm. The system being developed uses GPUs for the implementation of a massive agent-based model of Autonomous Vehicle (AV) behaviour in an Artificial Multi-Connected Road Network (AMСRN) consisting of the “Manhattan Grid” and the “Circular Motion Area” that are crossed. A new parallel Real-Coded Genetic Algorithm with a Scalable Nonuniform Mutation (RCGA-SNUM) is developed. The proposed algorithm (RCGA-SNUM) has been examined with the use of known test instances and compared with parallel RCGAs used with other mutation operators (e.g., standard mutation, Power Mutation (PM), mutation with Dynamic Rates (DMR), Scalable Uniform Mutation (SUM), etc.). As a result, RCGA-SNUM demonstrates superiority in solving large-scale optimisation problems when decision variables have wide feasible ranges and multiple local extrema are observed. Following this, RCGA-SNUM is applied to minimising the number of potential traffic accidents in the AMСRN.

A Novel Parent Centric Crossover with the Log-Logistic Probabilistic Approach Using Multimodal Test Problems for Real-Coded Genetic Algorithms

In this paper, a comprehensive empirical study is conducted to evaluate the performance of a new real-coded crossover operator called Fisk crossover (FX) operator. The basic aim of the proposed study is to preserve population diversity as well as to avoid local optima. In this context, a new crossover operator is designed and developed which is linked with Log-logistic probability distribution. For its global performance, a realistic comparison is made between FX versus double Pareto crossover (DPX), Laplace crossover (LX), and simulated binary crossover (SBX) operators. Moreover, these crossover operators are also used in conjunction with three mutation operators called power mutation (PM), Makinen, Periaux, and Toivanen mutation (MPTM), and nonuniform mutation (NUM) for inclusive evaluation. The performance of probabilistic-based algorithms is tested on a set of twenty-one well-known nonlinear optimization benchmark functions with diverse features. The empirical results show a substantial dominance of FX over other crossover operators with authentication of performance index (PI). Moreover, we also examined the significance of the proposed crossover scheme by administrating ANOVA and Gabriel pairwise multiple comparison test. Finally, the statistically significant results of the proposed crossover scheme have a definite edge over the other schemes, and it is also expected that FX has a great potential to solve complex optimization problems.

SwrD (YlzI) Promotes Swarming in Bacillus subtilis by Increasing Power to Flagellar Motors

ABSTRACTThe bacteriumBacillus subtilisis capable of two kinds of flagellum-mediated motility: swimming, which occurs in liquid, and swarming, which occurs on a surface. Swarming is distinct from swimming in that it requires secretion of a surfactant, an increase in flagellar density, and perhaps additional factors. Here we report a new gene,swrD, located within the 32 genefla-cheoperon dedicated to flagellar biosynthesis and chemotaxis, which when mutated abolished swarming motility. SwrD was not required for surfactant production, flagellar gene expression, or an increase in flagellar number. Instead, SwrD was required to increase flagellar power. Mutation ofswrDreduced swimming speed and torque of tethered flagella, and allswrD-related phenotypes were restored when the stator subunits MotA and MotB were overexpressed either by spontaneous suppressor mutations or by artificial induction. We conclude that swarming motility requires flagellar power in excess of that which is needed to swim.IMPORTANCEBacteria swim in liquid and swarm over surfaces by rotating flagella, but the difference between swimming and swarming is poorly understood. Here we report that SwrD ofBacillus subtilisis necessary for swarming because it increases flagellar torque and cells mutated forswrDswim with reduced speed. How flagellar motors generate power is primarily studied inEscherichia coli, and SwrD likely increases power in other organisms, like theFirmicutes,Clostridia,Spirochaetes, and theDeltaproteobacteria.

Apply Roof-Broken the Blasting Technique to Control Rock Burst

In working mining process of No.108 coal seam in Shengli Mine, the lower triangular goaf area hanging arch caused shock pressure. For this, The theoretical analysis do this weaken the roof through the roof-broken blasting technology, to optimize blasting design parameters, combined with the actual situation of the working surface to improve the blasting process, improve the top relief solve risk technology and solve the problem of rock burst. The application shows: successful implementation of advanced deep hole pre-splitting blasting to address a key challenge for charge and blasting technology, effective control of the roof power mutation occurs, improve the safety factor of the mining area, and provides an important mining work under similar conditions.

Hybrid Genetic Algorithm

Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle (namely LX-PM) efficiently for solving both constrained and unconstrained optimization problems. In this chapter, a local search technique, namely Quadratic Approximation (QA) is discussed. QA is hybridized with LX-PM in order to improve its efficiency and efficacy. The generated hybrid system is named H-LX-PM. The supremacy of H-LX-PM over LX-PM is validated through a test bed of 22 unconstrained and 15 constrained typical benchmark problems. In the later part of this chapter, a few applications of GA in networking optimization are highlighted as the scope for future research.

New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem

Trim loss minimization is the most common problem that arises during the cutting process, when products with variable width or length are to be produced in bulk to satisfy customer demands from limited available/stocked materials. The aim is to minimize inevitable waste material. Under various environmental and physical constraints, the trim loss problem is highly constrained, non convex, nonlinear, and with integer restriction on all variables. Due to the highly complex nature of trim loss problem, it is not easy for manufacturers to select an appropriate method that provides a global optimal solution, satisfying all restrictions. This paper proposes a discrete variant of PSO, which embeds a mutation operator, namely power mutation during the position update stage. The proposed variant is named as Hybrid Discrete PSO (HDPSO). Binary variables in HDPSO are generated using sigmoid function with its domain derived from position update equation. Four examples with different levels of complexity are solved and results are compared with two recently developed GA and PSO variants. The computational studies indicate the competitiveness of proposed variant over other considered methods.

New Non-Equidistant Optimum GM(1,1) of Line-Drawing Data Processing in Computer Aided Design

Based on the exponential trait of grey model and the definition of integral, the reconstruction method of GM(1,1) model’s background value of non-equal distance sequence was put forward and a kind of non-equidistant optimum grey model GM(1,1) to line-drawing data processing in computer aided design was proposed. The mean relative error is taken as the optimum objective function. The power mutation particle swarm optimization program PMPSO1.0 was compiled with Matlab 7.6 software to make optimization. Two examples were given, their results were compared with the results based other Grey models, respectively. The method can be used for model establishing on equal interval, as well as on non-interval. Moreover, GM(1,1) model’s fitting precision and prediction is advanced and the scope of application is enlarged. The model is simple and practical, and has a generalizing value in the field of CAD.