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State transition algorithm (STA) is an efficient and powerful metaheuristic method for solving global optimization problems, and it has been successfully applied in many engineering fields in the past few years. However, the basic STA has weak local search capability and shows slow convergence rate and low convergence accuracy in the later search stage. In view of the above shortcomings, an adaptive state transition algorithm (ASTA) with local enhancement is proposed in this paper. Firstly, the order of using state transformation operators and the optimal parameters of the operators are considered in each iteration of ASTA, and a statistical method is employed to adaptively select the optimal transformation operator and the parameter values of the optimal operator to speed up the search process. Then, an adaptive call strategy is adopted to determine its convergence to the neighborhood of the optimal solution and to decide whether to perform the quasi-Newton operator for local enhancement. Finally, the degree to which the current solution is close to the optimal solution is judged by the information of historical solutions, and an analytical solution is quickly obtained by calling the quadratic interpolation operator. The effectiveness of the proposed ASTA is checked, through a comparison with other metaheuristic methods, on 15 benchmark functions and several real-world optimization problems. Experimental results show that ASTA has a stronger search capability than the basic STA, STA variants, and some state-of-the-art metaheuristic methods. • An adaptive state transition algorithm is proposed. • Local enhancement techniques are employed to speed up the search efficiency. • The proposed algorithm is verified on some practical engineering problems. [ABSTRACT FROM AUTHOR]

Abstract: DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. Microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and information from these microarrays has attracted the attention of many biologists and computer scientists. Outlier is defined as an observation that deviates too much from other observations. The identification of outliers can lead to the discovery of useful and meaningful knowledge. In this paper we propose hybrid approach for outlier detection by studying the behavior of projections from the data set. We first identify patterns with abnormally low presence. Once such patterns have been identified, then the outliers are defined as those records which have such patterns present in them. We have compared time and quality aspects of our results and it is found to be better than both brute force and evolutionary algorithms. For the same data set, brute force algorithm takes more time with good quality results as compared to evolutionary algorithm, where as Evolutionary Algorithm takes less time with less quality compared to brute force algorithm. [Copyright &y& Elsevier]

A lower bound (LB) axisymmetric limit analysis formulation is presented for Mohr-Coulomb materials using finite elements and semidefinite programming (SDP). The details of the application of SDP to the axisymmetric LB finite element method for Mohr-Coulomb materials are described in this paper. Through the use of the SDP, the axisymmetric Mohr-Coulomb yield criterion can be directly applied in its native form in terms of principal stresses without any smoothing of the parent yield surface, and no assumption needs to be made about σ θ. A node-stress-based adaptive mesh algorithm is also introduced into the proposed method to obtain an accurate high-accuracy LB solution. The proposed method is then applied to estimate the collapse load of circular footings and the stability of an unlined vertical circular excavation. The results are also compared with those reported in the literature; the results obtained from the proposed method are accurate and can provide failure patterns with greater clarity. [ABSTRACT FROM AUTHOR]

Artificial bee colony (ABC) algorithm is an efficient bio-inspired optimizer proposed recently. Though it has gained great popularity, ABC suffers from its slow convergence and poor generalization on various problem landscapes. To address the issues, an augmented ABC with adaptive heterogeneous competition (ABC-AHC) is proposed in this study. In ABC-AHC, two bee swarms with each conducting heterogeneous but complementary capabilities are implemented to improve the search capabilities on various problem spaces. To dynamically adjust the search behaviors, an adaptive mechanism is developed to trigger the competition and migration between the bee swarms. Comparative studies are conducted for parameter tuning and the heterogeneous searching (HST). Existing algorithms including ABC variants and non-ABC variants are adopted to validate the performance of ABC-AHC. Numerical comparisons are conducted on 30D and 100D benchmark functions, CEC 2014 test function, random function and the real-world problems. Numerical results demonstrate that the proposed strategies significantly enhance ABC's search capability and convergence speed on the various benchmark functions. • We proposed an efficient ABC optimizer that performs well on a diverse set of global optimization problems. • Two swarms with each conducting heterogeneous but complementary behaviors are implemented. • An adaptive mechanism is developed to dynamically balance the exploration and exploitation. • Adaptive mechanism is triggered by individuals' competition and migration between the swarms. [ABSTRACT FROM AUTHOR]