Reducing the Computational Effort Associated with Evolutionary Optimisation in Single Component Design

The dissertation presents innovative Evolutionary Search (ES) methods for the reduction in computational expense associated with the optimisation of highly dimensional design spaces. The objective is to develop a semi-automated system which successfully negotiates complex search spaces. Such a system would be highly desirable to a human designer by providing optimised design solutions in realistic time. The design domain represents a real-world industrial problem concerning the optimal material distribution on the underside of a flat roof tile with varying load and support conditions. The designs utilise a large number of design variables (circa 400). Due to the high computational expense associated with analysis such as finite element for detailed evaluation, in order to produce "good" design solutions within an acceptable period of time, the number of calls to the evaluation model must be kept to a minimum. The objective therefore is to minimise the number of calls required to the analysis tool whilst also achieving an optimal design solution. To minimise the number of model evaluations for detailed shape optimisation several evolutionary algorithms are investigated. The better performing algorithms are combined with multi-level search techniques which have been developed to further reduce the number of evaluations and improve quality of design solutions. Multi-level techniques utilise a number of levels of design representation. The solutions of the coarse representations are injected into the more detailed designs for fine grained refinement. The techniques developed include Dynamic Shape Refinement (DSR), Modified Injection Island Genetic Algorithm (MiiGA) and Dynamic Injection Island Genetic Algorithm (DiiGA). The multi-level techniques are able to handle large numbers of design variables (i.e. > 100). Based on the performance characteristics of the individual algorithms and multi-level search techniques, distributed search techniques are proposed. These techniques utilise different evolutionary strategies in a multi-level environment and were developed as a way of further reducing computational expense and improve design solutions. The results indicate a considerable potential for a significant reduction in the number of evaluation calls during evolutionary search. In general this allows a more efficient integration with computationally intensive analytical techniques during detailed design and contribute significantly to those preliminary stages of the design process where a greater degree of analysis is required to validate results from more simplistic preliminary design models

Spontaneous Fruit Fly Optimisation for truss weight minimisation:Performance evaluation based on the no free lunch theorem

Over the past decade, several researchers have presented various optimisation algorithms for use in truss design. The no free lunch theorem implies that no optimisation algorithm fits all problems; therefore, the interest is not only in the accuracy and convergence rate of the algorithm but also the tuning effort and population size required for achieving the optimal result. The latter is particularly crucial for computationally intensive or high-dimensional problems. Contrast-based Fruit-fly Optimisation Algorithm (c-FOA) proposed by Kanarachos et al. in 2017 is based on the efficiency of fruit flies in food foraging by olfaction and visual contrast. The proposed Spontaneous Fruit Fly Optimisation (s-FOA) enhances c-FOA and addresses the difficulty in solving nonlinear optimisation algorithms by presenting standard parameters and lean population size for use on all optimisation problems. Six benchmark problems were studied to assess the performance of s-FOA. A comparison of the results obtained from documented literature and other investigated techniques demonstrates the competence and robustness of the algorithm in truss optimisation.Comment: Presented at the International conference for sustainable materials, energy and technologies, 201

Teaching-learning based optimization for parameter estimation of double tuned mass dampers

The classical methods for parameter estimation of tuned mass dampers are well known simple formulations, but these formulations are only suitable for multiple degree of freedom structures by considering a single mode. If special range limitation of tuned mass dampers and inherent damping of the main structure are considered, the best way to estimate the parameters is to use a numerical method. The numerical method must have a good convergence and computation time. In that case, metaheuristic methods are effective on the problem. Generally, metaheuristic method is inspired from a process of life and it is formulated for several steps in order to reach an optimal goal. Differently from the single tuned mass dampers, double tuned mass dampers can be also used for the reduction of vibrations. In civil structures, earthquake excitation is a major source of vibrations. In this study, optimum double tuned mass dampers are investigated for seismic structures by using a wide range of earthquake records for global optimum. As an optimization algorithm, teaching learning based optimization is employed. In this algorithm, the teaching and learning phases of a class are modified for optimization problems. The optimization of double tuned mass damper is more challenging than the single ones since the number of design variable is doubled and the design constraint about the stroke of the both masses must be considered. The proposed method is compared with the existing approaches and the methodology is feasible for parameter estimation of double tuned mass dampers

Proposal and Comparative Study of Evolutionary Algorithms for Optimum Design of a Gear System

This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.Fil: Méndez Babey, Máximo. Universidad de Las Palmas de Gran Canaria; EspañaFil: Rossit, Daniel Alejandro. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaFil: González, Begoña. Universidad de Las Palmas de Gran Canaria; EspañaFil: Frutos, Mariano. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Investigaciones Económicas y Sociales del Sur. Universidad Nacional del Sur. Departamento de Economía. Instituto de Investigaciones Económicas y Sociales del Sur; Argentin

Application of Metaheuristics in Signal Optimisation of Transportation Networks: A Comprehensive Survey

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.With rapid population growth, there is an urgent need for intelligent traffic control techniques in urban transportation networks to improve the network performance. In an urban transportation network, traffic signals have a significant effect on reducing congestion, improving safety, and improving environmental pollution. In recent years, researchers have been applied metaheuristic techniques for signal timing optimisation as one of the practical solution to enhance the performance of the transportation networks. Current study presents a comprehensive survey of such techniques and tools used in signal optimisation of transportation networks, providing a categorisation of approaches, discussion, and suggestions for future research

Multipopulation-based multi-level parallel enhanced Jaya algorithms

To solve optimization problems, in the field of engineering optimization, an optimal value of a specific function must be found, in a limited time, within a constrained or unconstrained domain. Metaheuristic methods are useful for a wide range of scientific and engineering applications, which accelerate being able to achieve optimal or near-optimal solutions. The metaheuristic method called Jaya has generated growing interest because of its simplicity and efficiency. We present Jaya-based parallel algorithms to efficiently exploit cluster computing platforms (heterogeneous memory platforms). We propose a multi-level parallel algorithm, in which, to exploit distributed-memory architectures (or multiprocessors), the outermost layer of the Jaya algorithm is parallelized. Moreover, in internal layers, we exploit shared-memory architectures (or multicores) by adding two more levels of parallelization. This two-level internal parallel algorithm is based on both a multipopulation structure and an improved heuristic search path relative to the search path of the sequential algorithm. The multi-level parallel algorithm obtains average efficiency values of 84% using up to 120 and 135 processes, and slightly accelerates the convergence with respect to the sequential Jaya algorithm.This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-66972-C5-4-R and Grant TIN2017-89266-R, co-financed by FEDER funds (MINECO/FEDER/UE)

Metaheuristic Optimization of Power and Energy Systems: Underlying Principles and Main Issues of the `Rush to Heuristics'

In the power and energy systems area, a progressive increase of literature contributions that contain applications of metaheuristic algorithms is occurring. In many cases, these applications are merely aimed at proposing the testing of an existing metaheuristic algorithm on a specific problem, claiming that the proposed method is better than other methods that are based on weak comparisons. This ‘rush to heuristics’ does not happen in the evolutionary computation domain, where the rules for setting up rigorous comparisons are stricter but are typical of the domains of application of the metaheuristics. This paper considers the applications to power and energy systems and aims at providing a comprehensive view of the main issues that concern the use of metaheuristics for global optimization problems. A set of underlying principles that characterize the metaheuristic algorithms is presented. The customization of metaheuristic algorithms to fit the constraints of specific problems is discussed. Some weaknesses and pitfalls that are found in literature contributions are identified, and specific guidelines are provided regarding how to prepare sound contributions on the application of metaheuristic algorithms to specific problems

Genetic Programming to Optimise 3D Trajectories

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesTrajectory optimisation is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on non-intersection with any obstacles as well as predefined performance metrics. In the context of UAVs, the goal is to minimise the cost of the route, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques including evolutionary computation have been applied to trajectory optimisation with various degrees of success. This thesis explores the use of genetic programming (GP) to optimise trajectories in 3D space, by encoding 3D geographic trajectories as syntax trees representing a curve. A comprehensive review of the relevant literature is presented, covering the theory and techniques of GP, as well as the principles and challenges of 3D trajectory optimisation. The main contribution of this work is the development and implementation of a novel GP algorithm using function trees to encode 3D geographical trajectories. The trajectories are validated and evaluated using a realworld dataset and multiple objectives. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness. Finally, insights and recommendations for future research in this area are provided, highlighting the potential for GP to be applied to other complex optimisation problems in engineering and science