Simulation of BOID type behaviours in Unity environment

The study describes and characterises BOID (bird-oid object) type behaviours, consisting of joint movement of a cluster of objects with the same properties. Authors presented Reynolds’ model, which takes into account 3 rules: separation, alignment and consistency, as well as the control procedures of a cluster of objects suggested by Parker, considering such variables as wind, aim, speed, order and the occurring forces. The test method was to conduct simulation experiments with different configurations of coefficients of the forces controlling the model. For each simulation the time of moving from the start point to the end point was measured. A hundred simulations were carried out for each individual group of coefficients, and then, using the described statistics methods, generalised time values were determined. This allowed a comparison of the results and made a conclusions. The numerical simulations carried out were implemented in Unity environment. Calculating the time required for travelling the same route was done by changing the value of the separation force, cohesion, alignment and avoidance. From the values obtained, it can be seen that the biggest influence on the increase of the time of moving BOID objects, is increasing value of the coefficient of separation and levelling forces. Unity environment is well suited to conduct such simulations, since it allows to obtain both numerical values and process visualization as a 3D image. In addition, Unity allows to create individual scripts to manage simulation in individual IDEs, and consists reliable documentation, which simplifies their writing

Predicting peptides binding to MHC class II molecules using multi-objective evolutionary algorithms

<p>Abstract</p> <p>Background</p> <p>Peptides binding to Major Histocompatibility Complex (MHC) class II molecules are crucial for initiation and regulation of immune responses. Predicting peptides that bind to a specific MHC molecule plays an important role in determining potential candidates for vaccines. The binding groove in class II MHC is open at both ends, allowing peptides longer than 9-mer to bind. Finding the consensus motif facilitating the binding of peptides to a MHC class II molecule is difficult because of different lengths of binding peptides and varying location of 9-mer binding core. The level of difficulty increases when the molecule is promiscuous and binds to a large number of low affinity peptides.</p> <p>In this paper, we propose two approaches using multi-objective evolutionary algorithms (MOEA) for predicting peptides binding to MHC class II molecules. One uses the information from both binders and non-binders for self-discovery of motifs. The other, in addition, uses information from experimentally determined motifs for guided-discovery of motifs.</p> <p>Results</p> <p>The proposed methods are intended for finding peptides binding to MHC class II I-A^g7molecule – a promiscuous binder to a large number of low affinity peptides. Cross-validation results across experiments on two motifs derived for I-A^g7datasets demonstrate better generalization abilities and accuracies of the present method over earlier approaches. Further, the proposed method was validated and compared on two publicly available benchmark datasets: (1) an ensemble of qualitative HLA-DRB1*0401 peptide data obtained from five different sources, and (2) quantitative peptide data obtained for sixteen different alleles comprising of three mouse alleles and thirteen HLA alleles. The proposed method outperformed earlier methods on most datasets, indicating that it is well suited for finding peptides binding to MHC class II molecules.</p> <p>Conclusion</p> <p>We present two MOEA-based algorithms for finding motifs, one for self-discovery and the other for guided-discovery by experimentally determined motifs, and thereby predicting binding peptides to I-A^g7molecule. Our experiments show that the proposed MOEA-based algorithms are better than earlier methods in predicting binding sites not only on I-A^g7but also on most alleles of class II MHC benchmark datasets. This shows that our methods could be applicable to find binding motifs in a wide range of alleles.</p

Application of Metamodel-Assisted Multiple-Gradient Descent Algorithm (MGDA) to Air-Cooling Duct Shape Optimization

International audienceMGDA stands for Multiple-Gradient Descent Algorithm, which was introduced in a previous report. MGDA was tested on several analytical test cases and also compared with a well-known Evolution Strategy algorithm, Pareto Archived Evolution Strategy (PAES). Using MGDA in a multi-objective optimization problem requires the evaluation of a substantial number of points with regard to criteria, and their gradients. In industrial test cases, in which computing the objective functions is CPU demanding, a variant of the method was to be found. Here, a metamodel-assisted MGDA is proposed and tested. The MGDA is assisted by a Kriging surrogate model construction. A first database is computed as an Latin Hypercube Sampling (LHS) distribution in the admissible design space, which is problem-dependent. Then, MGDA leads each database point to a non dominated set of the surrogate model. In this way, each function computation is made on the surrogate model at a negligible computational cost

Comparison between two multi objective optimization algorithms : PAES and MGDA. Testing MGDA on Kriging metamodels

Book dedicated to Professor P. Neittaanmaki on His 60th BithdayInternational audienceIn multi-objective optimization, the knowledge of the Pareto set provides valuable information on the reachable optimal performance. A number of evolutionary strategies (PAES [4], NSGA-II [3], etc), have been proposed in the literature and proved to be successful to identify the Pareto set. However, these derivative-free algorithms are very demanding in computational time. Today, in many areas of computational sciences, codes are developed that include the calculation of the gradient, cautiously validated and calibrated. Thus, an alternate method applicable when the gradients are known is introduced presently. Using a clever combination of the gradients, a descent direction common to all criteria is identified. As a natural outcome, the Multiple Gradient Descent Algorithm (MGDA) is defined as a generalization of the steepest-descent method and compared with PAES by numerical experiments. Using MGDA on a multi objective optimization problem requires the evaluation of a large number of points with regard to criteria, and their gradients. In the particular case of CFD problems, each point evaluation is very costly. Thus here we also propose to construct metamodels and to calculate approximate gradients by local finite differences

Çok amaçlı metasezgisel optimizasyon algoritmalarının performans karşılaştırması

Optimizasyon, karşılaşılan kısıtlı veya kısıtsız problemlerin amaçları doğrultusunda uygun çözümler üretme işlemidir. Optimizasyon işlemini gerçekleştirmek için çeşitli optimizasyon algoritmaları geliştirilmiştir. Tek amaçlı optimizasyon algoritmaları, günlük hayatta karşılaştığımız birden fazla amacı bulunan problemler için yetersiz kaldığından dolayı çok amaçlı optimizasyon algoritmaları geliştirilmiştir. Geliştirilen algoritmalarda, problemler üzerinden en uygun çözüm kümesini bulmak için çeşitli yöntemler kullanılmıştır. Bu yöntemlerden en etkilisi, yaygın olarak kullanılmakta olan pareto optimal yöntemidir. Pareto optimal yönteminde çok amaçlı optimizasyonun ulaştığı çözümlerden oluşan pareto optimal kümesi, problemlerin tek bir noktadaki çözümlerini değil belli aralıklardaki tüm en iyi çözümlerini içermektedir. Bu çalışmada, literatürde bulunan metasezgisel çok amaçlı optimizasyon algoritmalardan, Çok Amaçlı Karınca Aslanı Optimizasyon Algoritması ile Çok Amaçlı Yusufçuk Algoritması’ nın, güncel kıyaslama fonksiyonları ve mühendislik problemleri üzerindeki başarımını görmek için performans karşılaştırılması yapılmıştır

Chose the right mutation rate for better evolve combinational logic circuits

Evolvable hardware (EHW) is a developing field that applies evolutionary algorithm (EA) to automatically design circuits, antennas, robot controllers etc. A lot of research has been done in this area several different EAs have been introduced to tackle numerous problems, as scalability, evolvability etc. However every time a specific EA is chosen for solving a particular task, all its components, such as population size, initialization, selection mechanism, mutation rate, and genetic operators, should be selected in order to achieve the best results. In the last three decade the selection of the right parameters for the EA’s components for solving different “testproblems” has been investigated. In this paper the behaviour of mutation rate for designing logic circuits, which has not been done before, has been deeply analyzed. The mutation rate for an EHW system modifies the number of inputs of each logic gates, the functionality (for example from AND to NOR) and the connectivity between logic gates. The behaviour of the mutation has been analyzed based on the number of generations, genotype redundancy and number of logic gates for the evolved circuits. The experimental results found provide the behaviour of the mutation rate during evolution for the design and optimization of simple logic circuits. The experimental results propose the best mutation rate to be used for designing combinational logic circuits. The research presented is particular important for those who would like to implement a dynamic mutation rate inside the evolutionary algorithm for evolving digital circuits. The researches on the mutation rate during the last 40 years are also summarized.Dr Hemantha Kodikara-Arachch

Blended wing body (BWB) planform multidisciplinary optimisation (MDO) for early stage aircraft design using model based engineering

A model-based engineering (MBE) framework has been developed for Multi-Disciplinary Optimisation (MDO) of a Blended Wing Body (BWB) configuration during early design stages. Specifically, a planform optimisation has been performed by focusing on three objective functions, namely, aerodynamic efficiency (Eff), drag coefficient (CD) and Operational Empty Weight (OEW). Particle Swarm Optimisation (PSO) has been used as algorithm for the optimisation, an open-source Vortex Lattice Method (VLM), with empirical corrections for compressibility, as aerodynamic module, along with a mass estimation model with respect to BWB considerations. A successful multidisciplinary optimisation has been performed for the BWB-11 configuration flying at cruise condition, specifically at Mach 0.85 and at an altitude of 10 km. Increment in Eff and decrement in CD and OEW compared to the baseline BWB has been achieved. The OEW has been calculated from a newly developed mass estimation model and successfully validated via statistical methods. The paper presents a rapid MDO framework for efficient BWB planform optimisation to be used at the early design stage, providing useful guidance to the designers. A detailed analysis of the integrated design system, the methods as well as the optimisation results are provided. In addition, further research to the current framework is also presented

Design of human-like posture prediction for inverse kinematic posture control of a humanoid robot

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2005.Includes bibliographical references (p. 53-54).A method and system has been developed to solve the kinematic redundancy for a humanoid redundant manipulator based on forward kinematic equation and the optimization of human-like constraints. The Multiple Objective Optimization (MOO) is preformed using a Genetic Algorithms (GA) and implemented using the Genetic and Evolutionary Algorithm Matlab Toolbox. The designed system is illustrated on a simple redundant 3 degree of freedom (dof) manipulator and is set up for a more complicated redundant 7 dof manipulator. The 7 dof manipulator is modeled from the Stan Winston studio's Leonardo, an 61 dof expressive humanoid robot. It has been found that the inverse kinematic solution to a 3dof model arm converged within 1% error of the solution within .05 mins processor time using the discomfort human-like constraint in 2d space. Similarly, the inverse kinematic solution to a 7 dof model arm consisting of Leonardo's right arm geometry was found to converge within 1% error within .20 mins processor time using the discomfort human-like constraint in 3D space. The full kinematic model of Leonardo is developed and future efficiency optimizations are posed to move towards the real-time motion control of a redundant humanoid robot by way of human-like posture prediction.by Derik Thomann.S.B

Optimisation of racing car suspensions featuring inerters

Racing car suspensions are a critical system in the overall performance of the vehicle. They must be able to accurately control ride dynamics as well as influencing the handling characteristics of the vehicle and providing stability under the action of external forces. This work is a research study on the design and optimisation of high performance vehicle suspensions using inerters. The starting point is a theoretical investigation of the dynamics of a system fitted with an ideal inerter. This sets the foundation for developing a more complex and novel vehicle suspension model incorporating real inerters. The accuracy and predictability of this model has been assessed and validated against experimental data from 4- post rig testing. In order to maximise overall vehicle performance, a race car suspension must meet a large number of conflicting objectives. Hence, suspension design and optimisation is a complex task where a compromised solution among a set of objectives needs to be adopted. The first task in this process is to define a set of performance based objective functions. The approach taken was to relate the ride dynamic behaviour of the suspension to the overall performance of the race car. The second task of the optimisation process is to develop an efficient and robust optimisation methodology. To address this, a multi-stage optimisation algorithm has been developed. The algorithm is based on two stages, a hybrid surrogate model based multiobjective evolutionary algorithm to obtain a set of non-dominated optimal suspension solutions and a transient lap-time simulation tool to incorporate external factors to the decision process and provide a final optimal solution. A transient lap-time simulation tool has been developed. The minimum time manoeuvring problem has been defined as an Optimal Control problem. A novel solution method based on a multi-level algorithm and a closed-loop driver steering control has been proposed to find the optimal lap time. The results obtained suggest that performance gains can be obtained by incorporating inerters into the suspension system. The work suggests that the use of inerters provides the car with an optimised aerodynamic platform and the overall stability of the vehicle is improved