Tag: Soft Computing – A Fusion of Foundations, Methodologies and Applications

Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling
salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are
replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious
challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on
the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on
the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature
convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments
on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms
when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.

In this paper, we show that free algebras in the variety of residuated lattices and some of its subvarieties are directly
indecomposable and show, as a consequence, the direct indecomposability of free algebras for some classes of their bounded
implicative subreducts.

This paper provides a survey of the research done on optimization in dynamic environments over the past decade. We show an
analysis of the most commonly used problems, methods and measures together with the newer approaches and trends, as well as
their interrelations and common ideas. The survey is supported by a public web repository, located at http://www.dynamic-optimization.org where the collected bibliography is manually organized and tagged according to different categories.

Linear discriminant analysis (LDA) is one of the most effective feature extraction methods in statistical pattern recognition,
which extracts the discriminant features by maximizing the so-called Fisher’s criterion that is defined as the ratio of between-class
scatter matrix to within-class scatter matrix. However, classification of high-dimensional statistical data is usually not
amenable to standard pattern recognition techniques because of an underlying small sample size (SSS) problem. A popular approach
to the SSS problem is the removal of non-informative features via subspace-based decomposition techniques. Motivated by this
viewpoint, many elaborate subspace decomposition methods including Fisherface, direct LDA (D-LDA), complete PCA plus LDA (C-LDA),
random discriminant analysis (RDA) and multilinear discriminant analysis (MDA), etc., have been developed, especially in the
context of face recognition. Nevertheless, how to search a set of complete optimal subspaces for discriminant analysis is
still a hot topic of research in area of LDA. In this paper, we propose a novel discriminant criterion, called optimal symmetrical
null space (OSNS) criterion that can be used to compute the Fisher’s maximal discriminant criterion combined with the minimal
one. Meanwhile, by the reformed criterion, the complete symmetrical subspaces based on the within-class and between-class
scatter matrices are constructed, respectively. Different from the traditional subspace learning criterion that derives only
one principal subspace, in our approach two null subspaces and their orthogonal complements were all obtained through the
optimization of OSNS criterion. Therefore, the algorithm based on OSNS has the potential to outperform the traditional LDA
algorithms, especially in the cases of small sample size. Experimental results conducted on the ORL, FERET, XM2VTS and NUST603
face image databases demonstrate the effectiveness of the proposed method.

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive
resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process
of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The
EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process
of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using
more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated.
The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking
pattern classification problems.

In the literature of multi-objective problem, there are different algorithms to solve different optimization problems. This
paper presents a min–max multi-objective procedure for a dual-objective, namely make span, and sum of the earliness and tardiness
of jobs in due window machine scheduling problems, simultaneously. In formulation of min–max method when this method is combined
with the weighting method, the decision maker can have the flexibility of mixed use of weights and distance parameter to yield
a set of Pareto-efficient solutions. This research extends the new hybrid metaheuristic (HMH) to solve parallel machines scheduling
problems with sequence-dependent setup time that comprises three components: an initial population generation method based
on an ant colony optimization (ACO), a simulated annealing (SA) as an evolutionary algorithm employs certain probability to
avoid becoming trapped in a local optimum, and a variable neighborhood search (VNS) which involves three local search procedures
to improve the population. In addition, two VNS-based HMHs, which are a combination of two methods, SA/VNS and ACO/VNS, are
also proposed to solve the addressed scheduling problems. A design of experiments approach is employed to calibrate the parameters.
The non-dominated sets obtained from HMH and two best existing bi-criteria scheduling algorithms are compared in terms of
various indices and the computational results show that the proposed algorithm is capable of producing a number of high-quality
Pareto optimal scheduling plans. Aside, an extensive computational experience is carried out to analyze the different parameters
of the algorithm.