Design optimization of broadband extreme ultraviolet polarizer in high-dimensional objective space

With the purpose of designing the extreme ultraviolet polarizer with many objectives, a combined application of multiobjective genetic algorithms is theoretically proposed. Owing to the multiobjective genetic algorithm, the relationships between different designing objectives of extreme ultraviolet polarizer have been obtained by analyzing the distribution of nondominated solutions in the 4D objective space, and the optimized multilayer design can be obtained by guiding the searching in the desired region based on the multiobjective genetic algorithm with reference direction. Comparing with the conventional method of multilayer design, our method has a higher probability of achieving the optimal multilayer design. Our work should be the first research in optimizing the optical multilayer designs in the high-dimensional objective space, and our results demonstrate a potential application of our method in the designs of optical thin films.

Filsafat Digital Integral: Reformulasi Program Literasi Digital Nasional di Era Pandemi Covid-19 di Indonesia

<p><span lang="EN-US">The Covid-19 pandemic has accelerated the process of human migration from the real world to the virtual world (cyber). One of the impacts is the emergence of the phenomenon of cyber religion. As one of the parties responsible for literacy of its citizens in this regard, the government, through the role of the Ministry of Communication and Information, then initiated the National Digital Literacy Program, which is based on four pillars, namely: digital safety, digital skills, digital ethics, and digital culture. In dealing with the cyber religion phenomenon, the four pillars only reinforce the interobjective space. Therefore, to complete it, this research uses the theoretical framework of Ken Wilber's Universal Integralism or Holonic Integralism, which integrates four dimensions of "space" integrally, namely: intersubjective, interobjective, subjective, and objective space. In his findings, the researcher offers the concept of Integral Digital Philosophy, which integrates the four spaces simultaneously. The implications of this finding are beneficial for the provision of religious preachers about the importance of integrating the awareness of the "four worlds" when preaching in cyberspace.</span></p>

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work, we applied a cluster-based approach for solution selection in a multiobjective evolutionary algorithm based on decomposition with bare bones particle swarm optimization for data clustering and investigated its clustering performance. In our previous work, we found that MOEA/D with BBPSO performed the best on 10 datasets. Here, we extend this work applying a cluster-based approach tested on 13 UCI datasets. We compared with six multiobjective evolutionary clustering algorithms from the existing literature and ten from our previous work. The proposed technique was found to perform well on datasets highly overlapping clusters, such as CMC and Sonar. So far, we found only one work that used cluster-based MOEA for clustering data, the hierarchical topology multiobjective clustering algorithm. All other cluster-based MOEA found were used to solve other problems that are not data clustering problems. By clustering Pareto solutions and evaluating new candidates against the found cluster representatives, local search is introduced in the solution selection process within the objective space, which can be effective on datasets with highly overlapping clusters. This is an added layer of search control in the objective space. The results are found to be promising, prompting different areas of future research which are discussed, including the study of its effects with an increasing number of clusters as well as with other objective functions.

Branch-and-Bound for Biobjective Mixed-Integer Linear Programming

We present a generic branch-and-bound algorithm for finding all the Pareto solutions of a biobjective mixed-integer linear program. The main contributions are new algorithms for obtaining dual bounds at a node, checking node fathoming, presolve, and duality gap measurement. Our branch-and-bound is predominantly a decision space search method because the branching is performed on the decision variables, akin to single objective problems, although we also sometimes split gaps and branch in the objective space. The various algorithms are implemented using a data structure for storing Pareto sets. Computational experiments are carried out on literature instances and on a new set of instances that we generate using a benchmark library (MIPLIB2017) for single objective problems. We also perform comparisons against the triangle splitting method from literature, which is an objective space search algorithm. Summary of Contribution: Biobjective mixed-integer optimization problems have two linear objectives and a mixed-integer feasible region. Such problems have many applications in operations research, because many real-world optimization problems naturally comprise two conflicting objectives to optimize or can be approximated in such a manner and are even harder than single objective mixed-integer programs. Solving them exactly requires the computation of all the nondominated solutions in the objective space, whereas some applications may also require finding at least one solution in the decision space corresponding to each nondominated solution. This paper provides an exact algorithm for solving these problems using the branch-and-bound method, which works predominantly in the decision space. Of the many ingredients of this algorithm, some parts are direct extensions of the single-objective version, but the main parts are newly designed algorithms to handle the distinct challenges of optimizing over two objectives. The goal of this study is to improve solution quality and speed and show that decision-space algorithms perform comparably to, and sometimes better than, algorithms that work mainly in the objective-space.

Concept Exploration of a Surface Effect Patrol Combatant Using a Multiobjective Synthesis Model

This paper demonstrates a concept design methodology for naval SESs that is adapted from modern surface combatant optimization techniques. Similar to current methods, a synthesis model is constructed that uses a variety of discrete and continuous input values to calculate ship characteristics and performance data. The model outputs are generated using a combination of first-principles and exact 3D geometry along with parametrics aggregated from conventional monohulls and SES historical data. A specifically formulated multiobjective genetic algorithm is integrated with the model. The algorithm explores the highly nonlinear and non-convex SES objective space to identify non-dominated design variants. The synthesis model and the associated design space for a patrol boat with a novel SES hullform is detailed. Tradeoffs are evaluated in objective criteria of cost and performance in high-speed littoral operations that include surveillance, reconnaissance, and surface warfare.

An Improved MOEA/D Algorithm for Complex Data Analysis

There are a large number of multiple level datasets in the Industry 4.0 era. Thus, it is necessary to utilize artificial intelligence technology for the complex data analysis. In fact, the technology often suffers from the self-optimization issue of multiple level datasets, which is taken as a kind of multiobjective optimization problem (MOP). Naturally, the MOP can be solved by the multiobjective evolutionary algorithm based on decomposition (MOEA/D). However, most existing MOEA/D algorithms usually fail to adapt neighborhood for the offspring generation, since these algorithms have shortcomings in both global search and adaptive control. To address this issue, we propose a MOEA/D with adaptive exploration and exploitation, termed MOEA/D-AEE, which adopts random numbers with a uniform distribution to explore the objective space and introduces a joint exploitation coefficient between parents to generate better offspring. By dynamic exploration and joint exploitation, MOEA/D-AEE improves both global search ability and diversity of the algorithm. Experimental results on benchmark data sets demonstrate that our proposed approach achieves global search ability and diversity in terms of the population distribution than state-of-the-art MOEA/D algorithms.

The journey of Jephthah’s daughter: On spatial cognition, body and language in Judges 11:37

The traditional literal interpretation of the text in Judges 11:37 shows exceptional variation in topographic depiction. The literal interpretation of Driver, published in Zeitschrift für die Alttestamentliche Wissenschaft, is an example. From a linguistic perspective, no attention was paid whatsoever to the relation of interiority between an objective body and an objective space. This article proposes a cognitive semantic perspective and argues that the motion-path verb ירד (yrd) in Judges 11:37 carries a metaphorical meaning, and the linguistic processing, that is, the metaphorical mapping of the image schematic structure of CHANGE (up-down) as the source domain onto that of BEHAVIOUR as the target domain, involving activation of cultural spatial and bodily systems. With this background in mind, Judges 11:37 represents a new understanding for similar UP-DOWN image schemas applied in the Hebrew Bible.Contribution: This article contributes to the understanding of the apparent ‘inexact’ sense of the use of ירד (yrd) in Judges 11:37.

BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.

BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.