Search results for "Memetic algorithm"
showing 10 items of 38 documents
Hierarchical Evolutionary Algorithms and Noise Compensation via Adaptation
2007
Hierarchical Evolutionary Algorithms (HEAs) are Nested Algorithms composed by two or more Evolutionary Algorithms having the same fitness but different populations. More specifically, the fitness of a Higher Level Evolutionary Algorithm (HLEA) is the optimal fitness value returned by a Lower Level Evolutionary Algorithm (LLEA). Due to their algorithmic formulation, the HEAs can be efficiently implemented in Min-Max problems. In this chapter the application of the HEAs is shown for two different Min-Max problems in the field of Structural Optimization. These two problems are the optimal design of an electrical grounding grid and an elastic structure. Since the fitness of a HLEA is given by a…
Multi-objective memetic optimization for the bi-objective obnoxious p -median problem
2018
Abstract Location problems have been studied extensively in the optimization literature, the p-median being probably one of the most tackled models. The obnoxious p-median is an interesting variant that appears in the context of hazardous location. The aim of this paper is to formally introduce a bi-objective optimization model for this problem, in which a solution consists of a set of p locations, and two conflicting objectives arise. On the one hand, the sum of the minimum distance between each client and their nearest open facility and, on the other hand, the dispersion among facilities. Both objective values should be kept as large as possible for a convenient location of dangerous faci…
Towards Multilevel Ant Colony Optimisation for the Euclidean Symmetric Traveling Salesman Problem
2015
Ant Colony Optimization ACO metaheuristic is one of the best known examples of swarm intelligence systems in which researchers study the foraging behavior of bees, ants and other social insects in order to solve combinatorial optimization problems. In this paper, a multilevel Ant Colony Optimization MLV-ACO for solving the traveling salesman problem is proposed, by using a multilevel process operating in a coarse-to-fine strategy. This strategy involves recursive coarsening to create a hierarchy of increasingly smaller and coarser versions of the original problem. The heart of the approach is grouping the variables that are part of the problem into clusters, which is repeated until the size…
An Island Strategy for Memetic Discrete Tomography Reconstruction
2014
In this paper we present a parallel island model memetic algorithm for binary discrete tomography reconstruction that uses only four projections without any further a priori information. The underlying combination strategy consists in separated populations of agents that evolve by means of different processes. Agents progress towards a possible solution by using genetic operators, switch and a particular compactness operator. A guided migration scheme is applied to select suitable migrants by considering both their own and their sub-population fitness. That is, from time to time, we allow some individuals to transfer to different subpopulations. The benefits of this paradigm were tested in …
Lower and upper bounds for the mixed capacitated arc routing problem
2006
This paper presents a linear formulation, valid inequalities, and a lower bounding procedure for the mixed capacitated arc routing problem (MCARP). Moreover, three constructive heuristics and a memetic algorithm are described. Lower and upper bounds have been compared on two sets of randomly generated instances. Computational results show that the average gaps between lower and upper bounds are 0.51% and 0.33%, respectively.
Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography
2009
In this article, various metaheuristics for a numerical optimization problem with application to Electric Impedance Tomography are tested and compared. The experimental setup is composed of a real valued Genetic Algorithm, the Differential Evolution, a self adaptive Differential Evolution recently proposed in literature, and two novel Memetic Algorithms designed for the problem under study. The two proposed algorithms employ different algorithmic philosophies in the field of Memetic Computing. The first algorithm integrates a local search into the operations of the offspring generation, while the second algorithm applies a local search to individuals already generated in the spirit of life-…
Memetic Algorithms in Engineering and Design
2012
When dealing with real-world applications, one often faces non-linear and nondifferentiable optimization problems which do not allow the employment of exact methods. In addition, as highlighted in [104], popular local search methods (e.g. Hooke-Jeeves, Nelder Mead and Rosenbrock) can be ill-suited when the real-world problem is characterized by a complex and highly multi-modal fitness landscape since they tend to converge to local optima. In these situations, population based meta-heuristics can be a reasonable choice, since they have a good potential in detecting high quality solutions. For these reasons, meta-heuristics, such as Genetic Algorithms (GAs), Evolution Strategy (ES), Particle …
A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows
2010
In this paper, we present an effective memetic algorithm for the vehicle routing problem with time windows (VRPTW). The paper builds upon an existing edge assembly crossover (EAX) developed for the capacitated VRP. The adjustments of the EAX operator and the introduction of a novel penalty function to eliminate violations of the time window constraint as well as the capacity constraint from offspring solutions generated by the EAX operator have proven essential to the heuristic's performance. Experimental results on Solomon's and Gehring and Homberger benchmarks demonstrate that our algorithm outperforms previous approaches and is able to improve 184 best-known solutions out of 356 instance…
Differential Evolution with Scale Factor Local Search for Large Scale Problems
2010
This chapter proposes the integration of fitness diversity adaptation techniques within the parameter setting of Differential Evolution (DE). The scale factor and crossover rate are encoded within each genotype and self-adaptively updated during the evolution by means of a probabilistic criterion which takes into account the diversity properties of the entire population. The population size is also adaptively controlled by means of a novel technique based on a measurement of the fitness diversity. An extensive experimental setup has been implemented by including multivariate problems and hard to solve fitness landscapes. A comparison of the performance has been conducted by considering a st…
A Memetic Algorithm for Binary Image Reconstruction
2008
This paper deals with a memetic algorithm for the reconstruction of binary images, by using their projections along four directions. The algorithm generates by network flows a set of initial images according to two of the input projections and lets them evolve toward a solution that can be optimal or close to the optimum. Switch and compactness operators improve the quality of the reconstructed images which belong to a given generation, while the selection of the best image addresses the evolution to an optimal output.