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Accurate system modelling is an important prerequisite for optimized process control in modern industrial scenarios. The task of parameter identification for a model can be considered as an optimization problem of searching for a set of continuous parameters to minimize the discrepancy between the model outputs and true output values. Differential Evolution (DE), as a class of population-based and...
Multimodal optimization (MMO) is the problem of finding many or all global and local optima. In recent years many efficient nature-inspired techniques (based on ES, PSO, DE and others) have been proposed for real-valued problems. Many real-world problems contain variables of many different types, including integer, rank, binary and others. In this case, the weakest representation (namely binary representation)...
Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) are well known optimization tools. PSO advantage is its capability for fast convergence to the promising solutions. On the other hand GAs are able to process schemata thanks to the use of crossover operator. However, both methods have also their drawbacks - PSO may fall into the trap of preconvergence, while GA capability of fast finding...
In the paper the novel feature selection method, using wrapper model and ensemble approach, is presented. In the proposed method features are selected dynamically, i.e. separately for each classified object. First, a set of identical one-feature classifiers using different single feature is created and next the ensemble of features (classifiers) is selected as a solution of optimization problem using...
This paper studies two parallelization techniques for the implementation of a SPSO algorithm applied to optimize electromagnetic field devices, GPGPU and Pthreads for multiprocessor architectures. The GPGPU and Pthreads implementations are compared in terms of solution quality and speed up. The electromagnetic optimization problems chosen for testing the efficiency of the parallelization techniques...
In line with the theory of schema sampling, a hypothesis could be made that sufficient supply of low-order building blocks (BBs) was one of the necessary conditions for a genetic algorithm(GA) to work. A consequential question of this hypothesis regards, when a certain fitness function is optimized with a commonly used GA, whether it is rare or common that there are plenty of low-order BBs. It is...
Clustering is a significant data mining task which partitions datasets based on similarities among data. In this study, partitional clustering is considered as an optimization problem and an improved ant-based algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), is applied to automatic grouping of large unlabeled datasets. The proposed algorithm employs Opposition-Based...
The literature highlights the effectiveness of emulating processes from nature to solve complex optimization problems. Two processes in particular that have been investigated are evolution and development. Evolution is achieved by genetic algorithms and the developmental approach was introduced to achieve development. The developmental approach differs from other metaheuristics in that it does not...
This paper proposes a new solution for Traveling Salesman Problem (TSP) using genetic algorithm. A combinational crossover technique is employed in the search for optimal or near-optimal TSP solutions. It is based upon chromosomes that utilise the concept of heritable building blocks. Moreover, generation of a single offspring, rather than two, per pair of parents, allows the system to generate high...
Microbial Genetic Algorithm (MGA) is a simple variant of genetic algorithm and is inspired by bacterial conjugation for evolution. In this paper we have discussed and analyzed variants of this less exploited algorithm on known benchmark testing functions to suggest a suitable choice of mutation operator. We also proposed a simple adaptive scheme to adjust the impact of mutation according to the diversity...
Computational devices with significant computing power are pervasive yet often under-exploited since they are frequently idle or performing non-demanding tasks. Exploiting this power can be a cost-effective solution for solving complex computational tasks. Device-wise, this computational power can some times comprise a stable, long-lasting availability windows but it will more frequently take the...
In this paper, an implementation of vantage point local search procedure for the Bees Algorithm (BA) in combinatorial domains is presented. In its basic version, the BA employs a local search combined with random search for both continuous and combinatorial domains. In this paper, a more robust local searching strategy namely, vantage point procedure is exploited along with random search to deal with...
This paper presents a GISMOO algorithm adaptation to solve a multi-objective permutation flowshop with sequence-dependent setup times. The makespan and the total tardiness are the two objectives studied. Numerical experiments on various benchmarks from the literature were performed, to compare the performance of the adapted GISMOO algorithm with the NSGA-II algorithm. The results indicate that our...
Evolutionary-based algorithms play an important role in finding solutions to many problems that are not solved by classical methods, and particularly so for those cases where solutions lie within extreme non-convex multidimensional spaces. The intrinsic parallel structure of evolutionary algorithms are amenable to the simultaneous testing of multiple solutions; this has proved essential to the circumvention...
Today technological progress makes scientific community to challenge more and more complex issues related to computational organization in distributed heterogeneous environments, which usually include cloud computing systems, grids, clusters, PCs and even mobile phones. In such environments, traditionally, one of the most frequently used mechanisms of computational organization is the Workflow approach...
Noise or uncertainty appear in many optimization processes when there is not a single measure of optimality or fitness but a random variable representing it. These kind of problems have been known for a long time, but there has been no investigation of the statistical distribution those random variables follow, assuming in most cases that it is distributed normally and, thus, it can be modelled via...
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that are based on building and sampling a probability model. Copula theory provides methods that simplify the estimation of a probability model. An island-based version of copula-based EDA with probabilistic model migration (mCEDA) was tested on a set of well-known standard optimization benchmarks in the continuous...
The design of high quality electron generators is important for a variety of applications including materials processing systems (including welding, cutting and additive manufacture), X-ray tubes for medical, scientific and industrial applications, microscopy, and lithography for integrated circuit manufacture. The many variants of electron gun required, and the increasing demands for highly optimised...
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