Boosting Atomic Orbit Search Using Dynamic-Based Learning for Feature Selection

Feature selection (FS) is a well-known preprocess step in soft computing and machine learning algorithms. It plays a critical role in different real-world applications since it aims to determine the relevant features and remove other ones. This process (i.e., FS) reduces the time and space complexity of the learning technique used to handle the collected data. The feature selection methods based on metaheuristic (MH) techniques established their performance over all the conventional FS methods. So, in this paper, we presented a modified version of new MH techniques named Atomic Orbital Search (AOS) as FS technique. This is performed using the advances of dynamic opposite-based learning (DOL) strategy that is used to enhance the ability of AOS to explore the search domain. This is performed by increasing the diversity of the solutions during the searching process and updating the search domain. A set of eighteen datasets has been used to evaluate the efficiency of the developed FS approach, named AOSD, and the results of AOSD are compared with other MH methods. From the results, AOSD can reduce the number of features by preserving or increasing the classification accuracy better than other MH techniques.

Design Principles of Large Cation Incorporation in Halide Perovskites

Perovskites have stood out as excellent photoactive materials with high efficiencies and stabilities, achieved via cation mixing techniques. Overcoming challenges to the stabilization of Perovskite solar cells calls for the development of design principles of large cation incorporation in halide perovskite to accelerate the discovery of optimal stable compositions. Large fluorinated organic cations incorporation is an attractive method for enhancing the intrinsic stability of halide perovskites due to their high dipole moment and moisture-resistant nature. However, a fluorinated cation has a larger ionic size than its non-fluorinated counterpart, falling within the upper boundary of the mixed-cation incorporation. Here, we report on the intrinsic stability of mixed Methylammonium (MA) lead halides at different concentrations of large cation incorporation, namely, ehtylammonium (EA; [CH3CH2NH3]+) and 2-fluoroethylammonium (FEA; [CH2FCH2NH3]+). Density functional theory (DFT) calculations of the enthalpy of the mixing and analysis of the perovskite structural features enable us to narrow down the compositional search domain for EA and FEA cations around concentrations that preserve the perovskite structure while pointing towards the maximal stability. This work paves the way to developing design principles of a large cation mixture guided by data analysis of DFT data. Finally, we present the automated search of the minimum enthalpy of mixing by implementing Bayesian optimization over the compositional search domain. We introduce and validate an automated workflow designed to accelerate the compositional search, enabling researchers to cut down the computational expense and bias to search for optimal compositions.

A generalized multivariable Newton method

It is well known that the Newton method may not converge when the initial guess does not belong to a specific quadratic convergence region. We propose a family of new variants of the Newton method with the potential advantage of having a larger convergence region as well as more desirable properties near a solution. We prove quadratic convergence of the new family, and provide specific bounds for the asymptotic error constant. We illustrate the advantages of the new methods by means of test problems, including two and six variable polynomial systems, as well as a challenging signal processing example. We present a numerical experimental methodology which uses a large number of randomized initial guesses for a number of methods from the new family, in turn providing advice as to which of the methods employed is preferable to use in a particular search domain.

A New Dual-Mode GEP Prediction Algorithm Based on Irregularity and Similar Period

Gene expression programming (GEP) uses simple linear coding to solve complex modeling problems. However, the performance is limited by the effectiveness of the selected method of evaluating population individuals, the breadth and depth of the search domain for the solution, and the ability of accuracy of correcting the solution based on historical data. Therefore, a new dual-mode GEP prediction algorithm based on irregularity and similar period is proposed. It takes measures to specialize origin data to reserve the elite individuals, reevaluate the target individuals, and process data and solutions via the similar period mode, which avoids the tendency to get stuck in local optimum and the complexity of the precisions of correcting complex modeling problems due to insufficiency scope of the search domain, and subsequently, better convergence results are obtained. If we take the leek price and the sunspot observation data as the sample to compare the new algorithm with the GEP simulation test, the results indicate that the new algorithm possesses more powerful exploration ability and higher precision. Under the same accuracy requirements, the new algorithm can find the individual faster. Additionally, the conclusion can be drawn that the performance of new algorithm is better on the condition that we take another set of sunspot observations as samples, combining the ARIMA algorithm and BP neural network prediction algorithm for simulation and comparison with the new algorithm.

Enhanced quadratic approximation integrated with butterfly optimization: a new search algorithm tested on structural and mathematical problems

The Butterfly Optimization Algorithm (BOA) is a swarm based technique, inspired from mating and food searching process of butterflies, developed in last year. Experiments indicate that BOA provides substantial exploration capability on conventional non-constrained benchmark problems, however for the cases with more complex and noisy domains the algorithm can easily be trapped into local minima due to its restricted exploitation behavior. To tackle this issue, current study deals with introducing an alternative search strategy to explore the region of the search domain with high certainty. Such that, firstly a weighted agent is defined and then a quadratic search is performed in the vicinity of this pre-defined agent. This alternative search strategy is named as Enhanced Quadratic Approximation (EQA) and it is combined with BOA method to improve its exploitation behavior and provide an efficient search algorithm. Thus, obtained new method is named as Enhanced Quadratic Approximation Integrated with Butterfly Optimization (EQB) algorithm. Different properties of proposed EQB are tested on mathematical and structural benchmark problems. Acquired results show that the introduced algorithm, in comparison with its parent method and some other well-stablished reported algorithms in the literature, provides a competitive performance in terms of stability, accuracy and convergence rate.

Hybrid inner-outer algorithm for solving real-world mechanical optimization problems

In the real world, the problems mostly are complex; more precisely, the problems generally are nonlinear or large scale other than if it was mandatory to resolve it under certain constraints, and that is common in engineering design problems. Therefore, the complexity of problem plays a critical role in determining the computational time and cost. Accordingly, a novel algorithm called inner-outer array is proposed in this paper. It depends on the design of parameters and then tolerance design as one of design of experiment stages. In this work, the inner-outer algorithm is used to solve real-world optimization problems to choose the preferable feasible regions of the entire search domain. Numerical results are documented and compared based on four well-known constrained mechanical engineering issues. It can be concluded that the performance of inner-outer algorithm is good to optimize constrained engineering problems, but it still needs some enhancements in the future work.

When the search domain is back region in Baltic: The Latvian aiz as compared to the Lithuanian už

The paper sets out to examine prepositional polysemy in the Baltic languages. More precisely, the investigation focuses on the semantic structure of the Latvian preposition aiz + Gen. ‘behind, beyond’ as compared to the Lithuanian už + Gen. / Acc. ‘behind, beyond, for’ discussed in our previous paper (Šeškauskienė & Žilinskaitė-Šinkūnienė 2015). The methodology of research relies on the cognitive linguistic framework, mainly on the principle of motivated polysemy. Its key idea is that in the semantic network of the preposition all senses are seen as directly or indirectly linked to the central sense. In the case of aiz and už, the central sense encodes information about spatial configuration of Figure and Ground with the former located in the back region of the latter. A number of other senses, mostly concrete, derived from the central sense, overlap in Latvian and Lithuanian but demonstrate a differing degree of entrenchment. The most distinct differences are identifiable in the abstract senses.

Adaptive coverage control for multi-USV system in complex environment with unknown obstacles

Aiming at the shortcomings of the existing control law based on the global information, this article studies the coverage problem of a given region in the plane using a team of USVs. The coverage goal, which is to cover a given search domain using multiple mobile sensors so that each point is surveyed until a certain preset level is achieved, is formulated in a mathematically precise problem statement. The adaptive control law is presented which enables multi-USV to navigate in a complex environment in the presence of unknown obstacles and guarantees that a fully connected multi-USV system attains the coverage goal. In particular, the dangerous area was divided into two different parts in order to enhance the searching efficiency. Finally, simulation results are presented to validate the feasibility and efficiency of the proposed approach.

Solution for Optimal Power Flow Problem Using WDO Algorithm

Wind driven optimization (WDO) algorithm is a best optimization method based on atmospherically motion, global optimization nature inspired method. The method is based on population iterative analytical global optimization for multifaceted and multi prototype in the search domain for constraints to implement. In this paper, WDO algorithm is accustomed to find optimal power flow solution. To find the efficacy of the technique, it is applied to IEEE 30 bus systems to find fuel cost for generation of power as a main objective. Obtained results were compared with other techniques shows the better solution for optimal power flow problem.

Asymptotic analysis of extended two-dimensional narrow capture problems

In this paper, we extend our recent work on two-dimensional diffusive search-and-capture processes with multiple small targets (narrow capture problems) by considering an asymptotic expansion of the Laplace transformed probability flux into each target. The latter determines the distribution of arrival or capture times into an individual target, conditioned on the set of events that result in capture by that target. A characteristic feature of strongly localized perturbations in two dimensions is that matched asymptotics generates a series expansion in ν = −1/ln ϵ rather than ϵ , 0 < ϵ ≪ 1, where ϵ specifies the size of each target relative to the size of the search domain. Moreover, it is possible to sum over all logarithmic terms non-perturbatively. We exploit this fact to show how a Taylor expansion in the Laplace variable s for fixed ν provides an efficient method for obtaining corresponding asymptotic expansions of the splitting probabilities and moments of the conditional first-passage-time densities. We then use our asymptotic analysis to derive new results for two major extensions of the classical narrow capture problem: optimal search strategies under stochastic resetting and the accumulation of target resources under multiple rounds of search-and-capture.