Constraint-Handling in Evolutionary Optimization; Efrén Mezura-Montes (Editor) : Springer, Studies in Computational Intelligence Series Vol. 198, 1st Edition, 2009. ISBN: 978-3-642-00618-0

The use of evolutionary and swarm intelligence algorithms, has become a very popular option to solve complex real-world optimization problems. However, in their original versions, these algorithms lack a mechanism to handle the constraints of the problem i.e. they were designed to deal with unconstrained search spaces. Therefore, the design of constraint-handling mechanism is nowadays considered a research area within natureinspired computation for optimization.Facultad de Informátic

DE with Random Vector based Mutatiton for High Dimensional Problems

Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

DE with Random Vector based Mutatiton for High Dimensional Problems

Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

DE with Random Vector based Mutatiton for High Dimensional Problems

Metaheuristic techniques are the current standard for solving optimization problems. Differential Evolution (DE) is one of the most used because all operations are on real floating point numbers and does not require extra coding. However, the performance shown by DE could decay when applied in problems of high dimensionality. In this paper we present RLSDE, a modified version of DE, based on a random vector as a scaling factor for the differential mutation and the application of a local search operator. These modifications constitute an algorithm capable of solving 100D problems using few computational resources. RLSDE is compared against the results obtained with the classic version of DE and ELSDE (Enchanced Local Search Differential Evolution), showing the performance of the proposal.XX Workshop Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

A Feature-Based Comparison of Evolutionary Computing Techniques for Constrained Continuous Optimisation

Evolutionary algorithms have been frequently applied to constrained continuous optimisation problems. We carry out feature based comparisons of different types of evolutionary algorithms such as evolution strategies, differential evolution and particle swarm optimisation for constrained continuous optimisation. In our study, we examine how sets of constraints influence the difficulty of obtaining close to optimal solutions. Using a multi-objective approach, we evolve constrained continuous problems having a set of linear and/or quadratic constraints where the different evolutionary approaches show a significant difference in performance. Afterwards, we discuss the features of the constraints that exhibit a difference in performance of the different evolutionary approaches under consideration.Comment: 16 Pagesm 2 Figure

Looking Inside Particle Swarm Optimization in Constrained Search Spaces

Abstract. In this paper, the behavior of different Particle Swarm Optimization (PSO) variants is analyzed when solving a set of well-known numerical constrained optimization problems. After identifying the most competitive one, some improvements are proposed to this variant regarding the parameter control and the constraint-handling mechanism. Furthermore, the on-line behavior of the improved PSO and some of the most competitive original variants are studied. Two performance measures are used to analyze the capabilities of each PSO to generate feasible solutions and to improve feasible solutions previously found i.e. how able is to move inside the feasible region of the search space. Finally, the performance of this improved PSO is compared against state-of-the-art PSO-based algorithms. Some conclusions regarding the behavior of PSO in constrained search spaces and the improved results presented by the modified PSO are given and the future work is established

Hibridación de Evolución Diferencial utilizando Hill Climbing para resolver problemas de optimización con restricciones

La Evolución Diferencial es una metaheurística de mucha utilidad cuando se requiere optimizar numéricamente funciones o problemas multidimensionales que no pueden ser resueltos por algún método tradicional de optimización global. A su vez, si se agregan condiciones de frontera, será necesario utilizar alguna técnica de manejo de restricciones. Para mejorar el desempeño de ED, una posible alternativa es combinarlo con algún algoritmo de búsqueda local. En este trabajo se presenta la hibridación de Evolución Diferencial y Hill Climbing, obteniendo resultados de calidad similar o superior a los conseguidos por métodos ya testeados.Eje: Workshop Agentes y sistemas inteligentes (WASI)Red de Universidades con Carreras en Informática (RedUNCI

Global and local selection in differential evolution for constrained numerical optimization

The performance of two selection mechanisms used in the most popular variant of differential evolution, known as DE/rand/1/bin, are compared in the solution of constrained numerical optimization problems. Four performance measures proposed in the specialized literature are used to analyze the capabilities of each selection mechanism to reach the feasible region of the search space, to find the vicinity of the feasible global optimum and the computational cost (measured by the number of evaluations) required. Two parameters of the differential evolution algorithm are varied to determine the most convenient values. A set of problems with different features is chosen to test both selection mechanisms and some findings are extracted from the results obtained.Facultad de Informátic