An Enhanced Differential Evolution Algorithm Based on Multiple Mutation Strategies

Differential evolution algorithm is a simple yet efficient metaheuristic for global optimization over continuous spaces. However, there is a shortcoming of premature convergence in standard DE, especially in DE/best/1/bin. In order to take advantage of direction guidance information of the best individual of DE/best/1/bin and avoid getting into local trap, based on multiple mutation strategies, an enhanced differential evolution algorithm, named EDE, is proposed in this paper. In the EDE algorithm, an initialization technique, opposition-based learning initialization for improving the initial solution quality, and a new combined mutation strategy composed of DE/current/1/bin together with DE/pbest/bin/1 for the sake of accelerating standard DE and preventing DE from clustering around the global best individual, as well as a perturbation scheme for further avoiding premature convergence, are integrated. In addition, we also introduce two linear time-varying functions, which are used to decide which solution search equation is chosen at the phases of mutation and perturbation, respectively. Experimental results tested on twenty-five benchmark functions show that EDE is far better than the standard DE. In further comparisons, EDE is compared with other five state-of-the-art approaches and related results show that EDE is still superior to or at least equal to these methods on most of benchmark functions

Estratégias evolucionárias de optimização de parâmetros reais

Mestrado em Engenharia MecânicaActualmente, existem diversos problemas de engenharia cujas propriedades podem ser expressas através de uma função, denominada função objectivo. Existem diversos métodos que possuem como principal objectivo minimizar a referida função. Os métodos baseados no gradiente são métodos nos quais a direcção e tamanho do passo são calculados a partir do declive da função objectivo. Apesar destes métodos necessitarem de reduzidos tempos de computação, estes podem convergir prematuramente ou ficar estagnados em mínimos locais. Os métodos de optimização baseados na teoria evolucionária são aproximações que possuem como principal desvantagem elevados tempos de computação. No entanto, estes apresentam uma grande flexibilidade na modelação de problemas de engenharia. Neste grupo, os algoritmos mais conhecidos e aplicados em problemas de optimização são os Algoritmos Evolucionários (EA’s), Algoritmos Genéticos (GA’s) e Evolução Diferencial (DE). Existem ainda algoritmos baseados em processos naturais tais como o algoritmo de Optimização por Bandos de Partículas (PSO), que reproduz o comportamento de bandos de animais. Neste trabalho é desenvolvido um algoritmo de optimização de procura directa baseado em métodos diferenciais, evolucionários e no comportamento de animais. O algoritmo desenvolvido é aplicado a problemas de engenharia inversa. Numa primeira fase, o algoritmo desenvolvido é validado e comparado com algoritmos existentes recorrendo a um conjunto de funções compostas especialmente criadas para este fim. Numa segunda fase, o algoritmo desenvolvido é aplicado a problemas de Engenharia Mecânica e Mecânica Computacional. Nesta secção, os problemas das três barras e da cúpula de 120 barras são analisados recorrendo ao Método dos Elementos Finitos (MEF). Seguidamente, os problemas de compressão de um provete cilíndrico e da placa com furo central são analisados. Nestes problemas a função a minimizar é dada por um programa do MEF comercial. Finalmente, o algoritmo é aplicado a um problema de identificação de parâmetros de um modelo constitutivo. O algoritmo desenvolvido apresenta bons resultados e uma boa taxa de convergência. ABSTRACT: Nowadays, there are many inverse engineering problems whose properties can be expressed by a function, called objective function. There are several methods whose main goal is to minimize the value of that function. The gradient-based methods are optimization methods in which the step direction and length are calculated in terms of the objective function's slope. Although these methods require little computation time, they may converge prematurely or get trapped in a local minima. The optimization methods based on the evolutionary theory are approaches that need, as a main disadvantage, high computation times. However, they have a great flexibility in modeling engineering problems. In this class of methods, the ones that are best known and more often applied in optimization problem are the Evolutionary Algorithms (EA's), Genetic Algorithms (GA's) and Differential Evolution (DE). There are also nature-inspired algorithms such as the Particle Swarm Optimization method (PSO) that mimics the behavior of animal swarms. In this work a direct search optimization algorithm based on differential and evolutionary methods as well as in the behavior of animals is developed. This algorithm is applied to inverse engineering problems. In a first stage, the developed algorithm is validated and compared with existing optimization algorithms using a set of composite functions specially design for that purpose. In a second phase, the algorithm is applied to Engineering and Computational Mechanics problems. In this section, the three-truss bar problem and the 120-bar dome truss problem that are solved using the Finite Element Method (FEM) are analyzed. Subsequently, the compression of a cylindrical billet and the plate with a central cut-out problems are analyzed. In these problems, the function to minimize is given by a commercial FEM code. Finally, the algorithm is applied to a constitutive model parameter identification problem. The develop algorithm obtains good results and a good convergence rate

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment