Set Of Candidate Solutions Research Articles

Constructive Solving of Raven's IQ Tests with Analogical Proportions

The paper shows that a Boolean logic modeling of analogical proportions can serve as a basis for solving quizzes as well as a common and popular type of IQ tests, namely Raven's progressive matrices. They are nonverbal tests supposedly measuring general intelligence. 3 × 3 Raven matrix exhibits eight geometric pictures displayed as its eight first cells: the remaining ninth cell is empty. In these tests, a set of candidate pictures is also given among which the subject is asked to identify the solution. In this paper, we investigate a general approach allowing to automatically solve Raven's progressive matrices tests. The approach is based on a logical view of analogical proportions, i.e., statements of the form A is to B as C is to D. We assume that analogical proportions hold between the rows and between the columns of the Raven's matrix. This view can be applied to a feature-based description of the pictures but also, in a number of cases, to a very low level representation, i.e., the pixel level. It appears that the analogical proportion reading just amounts here to a recopy of patterns of feature values that already appear in the data, after checking that there is no conflicting patterns. Implementing this principle, our algorithm builds up the ninth picture, without the help of any set of candidate solutions, and only on the basis of the eight known cells of the Raven matrices. comparison with other approaches is provided. The ability to construct the missing picture without relying on candidate solutions is a distinctive feature of our work. Moreover, we emphasize the general principle underlying the approach that offers a simple and uniform mechanism applicable to the tests. At this step, the paper makes no claim about the cognitive validity of the approach with respect to the way humans solve such tests.

Particle swarm optimization (PSO). A tutorial

Swarm-based algorithms emerged as a powerful family of optimization techniques, inspired by the collective behavior of social animals. In particle swarm optimization (PSO) the set of candidate solutions to the optimization problem is defined as a swarm of particles which may flow through the parameter space defining trajectories which are driven by their own and neighbors' best performances. In the present paper, the potential of particle swarm optimization for solving various kinds of optimization problems in chemometrics is shown through an extensive description of the algorithm (highlighting the importance of the proper choice of its metaparameters) and by means of selected worked examples in the fields of signal warping, estimation robust PCA solutions and variable selection.

Emergency scheduling of engineering rescue tasks in disaster relief operations and its application in China

AbstractDisaster relief operations typically involve a large number of engineering rescue tasks, the efficient completion of which is vital to the success of the operations. The paper establishes a model of emergency scheduling of engineering rescue tasks in disaster relief operations, which involves multiple rescue teams and tasks, different and, perhaps fuzzy, processing times, as well as different importance weights of the tasks. We then propose a method based on biogeography‐based optimization, develop effective migration and mutation operators, and employ a multiobjective optimization approach for providing a set of candidate solutions for decision support. Computational experiments demonstrate that our approach exhibits competitive performance on a set of test problems. The proposed model and method have been successfully applied to the recent 2013 Dingxi earthquake that occurred in China.

Preference-based anonymization of numerical datasets by multi-objective microaggregation

Microaggregation is a statistical disclosure control mechanism to realize k-anonymity as a basic privacy model. The method first partitions the dataset into groups of at least k records and then aggregates the group members. Generally, larger values of k provide lower Disclosure Risk (DR) at the expense of increasing Information Loss (IL). Therefore, the data publisher has to set appropriate microaggregation parameters to produce a protected and useful anonymized data. Unfortunately, in the most of the conventional microaggregation methods, the only available parameter of the algorithm, i.e., k does not enable the data publisher to effectively control the trade-off problem between DR and IL. This paper proposes a novel microaggregation method to optimize information loss and disclosure risk, simultaneously. The trade-off problem is expressed and solved within a multi-objective optimization framework. The data publisher can choose a more preferred protected dataset from a set of non-dominated candidate solutions, or even direct the method toward a desired point. Experimental results show that for a fixed value of k, the proposed method can usually produce more protected and useful datasets in comparison with the conventional methods.

A Multilevel Thresholding algorithm using electromagnetism optimization

Segmentation is one of the most important tasks in image processing. It consists of classifying the pixels into two or more groups depending on their intensity levels and a threshold value. The quality of the segmentation depends on the method applied to select the threshold. The use of the classical implementations for Multilevel Thresholding is computationally expensive since they exhaustively search the best values to optimize the objective function. Under such conditions, the use of optimization evolutionary approaches has been extended. The Electromagnetism-like Algorithm (EMO) is an evolutionary method which mimics the attraction–repulsion mechanism among charges to evolve the members of a population. Different to other algorithms, EMO exhibits interesting search capabilities whereas maintains a low computational overhead. In this paper, a Multilevel Thresholding (MT) algorithm based on the EMO is introduced. The approach combines the good search capabilities of EMO algorithm with objective functions proposed by the popular MT methods of Otsu and Kapur. The algorithm takes random samples from a feasible search space inside the image histogram. Such samples build each particle in the EMO context whereas its quality is evaluated considering the objective that is function employed by the Otsu׳s or Kapur׳s method. Guided by these objective values the set of candidate solutions are evolved through the EMO operators until an optimal solution is found. The approach generates a multilevel segmentation algorithm which can effectively identify the threshold values of a digital image in a reduced number of iterations. Experimental results show performance evidence of the implementation of EMO for digital image segmentation.

Maximizing Future Flexibility in Electric Generation Portfolios

This paper presents a methodology to obtain flexible future capacity expansion plans under diverse types and sources of uncertainty classified as global and local. Planning flexibility is defined as the capability of a long-term planning solution to adapt cost-effectively to any of the conditions of characterizing the identified scenarios. Global (or high-impact) uncertainties allow us to create scenarios that guide the flexibility-based planning model; whereas local uncertainties allow us to create uncertainty sets that model the imperfect knowledge of each global uncertainty (GU). Our methodology, rather than choosing the most flexible plan among a set of candidate solutions, designs a flexible system that is less sensitive to the choice of scenarios. In addition to minimizing the investment and operational cost, the model minimizes its future adaptation cost to the conditions of other identified scenarios via adjustable robust optimization. Results obtained with our methodology in a 5-region US system under a 40-year planning horizon show how a flexible system adapts to future high-impact uncertainties at reasonably low costs with a low number of adaptation actions. A folding horizon process where GUs are guided by Markov chains was performed to assess the degree of flexibility of the system and its cost under multiple operation conditions.

A polynomial-delay algorithm for enumerating approximate solutions to the interval constrained coloring problem

We study the interval constrained coloring problem, a combinatorial problem arising in the interpretation of data on protein structure emanating from experiments based on hydrogen/deuterium exchange and mass spectrometry. The problem captures the challenging task of increasing the spatial resolution of experimental data in order to get a better picture of the protein structure. Since solutions proposed by any algorithmic framework have to ultimately be verified by biochemists, it is important to provide not just a single solution, but a valuable set of candidate solutions. Our contribution is a polynomial-delay, polynomial-space algorithm for enumerating all exact solutions plus further approximate solutions, which are guaranteed to be within an absolute error of two of the optimum within fragments of the protein, that is, within sets of consecutive residues. Our experiments indicate that the quality of the approximate solutions is comparable to the optimal ones in terms of deviation from the underlying true solution. In addition, the experiments also confirm the effectiveness of the method in reducing the delay between two consecutive solutions considerably, compared to what it takes an integer programming solver to produce the next exact solution.

An Efficient Risk Analysis based Risk Priority in Requirement Engineering using Modified Goal Risk Model

Risk analysis is traditionally considered a critical activity for the whole software system‟s lifecycle. Risks are identified by considering technical aspects (e.g., failures of the system, unavailability of services, etc.) and handled by suitable countermeasures through a refined design. A modified Tropos goal model was developed in which the evidence of satisfaction and denial of the goal is calculated from the likelihood of the events corresponding to the goals. Relations are defined between multiple goals and events, which define the importance of a particular goal. The event may be considered as a risk according to their likelihood value. So the inter relation values of the goals and events gives the impact of that event on the particular goal. In order to analyze the risk in achieving some particular goals, a set of candidate solutions are generated. The candidate solutions are evaluated on the basis of a risk affinitive value of the goals according to their events. The risk affinitive value is calculated from the different set of risk parameters, which is set like high, medium and low. The risk parameters clearly evaluate the affinity of that event to a particular set of goals. A priority based parameter is added to the proposed approach to sort out the risk values. According to the proposed approach distinct cost to risk values are achieved. Finally, the candidate solutions with low cost are selected.

Meander Line Antenna Design Using an Adaptive Genetic Algorithm

This paper presents the optimization of a meander line antenna finite-element model by means of an adaptive genetic algorithm (GA). To search for optimal antenna configurations, the present method employs a GA with an adaptive method that adjusts the characteristics of its selection, crossover, and mutation operators in order to maintain a diverse set of high-quality candidate solutions during its execution. It is shown that the present method can find an optimal solution faster than the conventional GA. Moreover, the fitness values of the optimal solutions obtained by the present method are better than those obtained by conventional GA.

A non dominated ranking Multi Objective Genetic Algorithm and electre method for unequal area facility layout problems

The unequal area facility layout problem (UA-FLP) comprises a class of extremely difficult and widely applicable optimization problems arising in diverse areas and meeting the requirements for real-world applications. Genetic Algorithms (GAs) have recently proven their effectiveness in finding (sub) optimal solutions to many NP-hard problems such as UA-FLP. A main issue in such approach is related to the genetic encoding and to the evolutionary mechanism implemented, which must allow the efficient exploration of a wide solution space, preserving the feasibility of the solutions and ensuring the convergence towards the optimum. In addition, in realistic situations where several design issues must be taken into account, the layout problem falls in the broader framework of multi-objective optimization problems. To date, there are only a few multi-objective FLP approaches, and most of them employ over-simplified optimization techniques which eventually influence the quality of the solutions obtained and the performance of the optimization procedure. In this paper, this difficulty is overcome by approaching the problem in two subsequent steps: in the first step, the Pareto-optimal solutions are determined by employing Multi Objective Genetic Algorithm (MOGA) implementing four separate fitness functions within a Pareto evolutionary procedure, following the general structure of Non-dominated Ranking Genetic Algorithm (NRGA) and the subsequent selection of the optimal solution is carried out by means of the multi-criteria decision-making procedure Electre. This procedure allows the decision maker to express his preferences on the basis of the knowledge of candidate solution set. Quantitative and qualitative objectives are considered referring to the slicing-tree layout representation scheme. The numerical results obtained outperform previous referenced approaches, thus confirming the effectiveness of the procedure proposed.

Multilevel Thresholding Segmentation Based on Harmony Search Optimization

In this paper, a multilevel thresholding (MT) algorithm based on the harmony search algorithm (HSA) is introduced. HSA is an evolutionary method which is inspired in musicians improvising new harmonies while playing. Different to other evolutionary algorithms, HSA exhibits interesting search capabilities still keeping a low computational overhead. The proposed algorithm encodes random samples from a feasible search space inside the image histogram as candidate solutions, whereas their quality is evaluated considering the objective functions that are employed by the Otsu’s or Kapur’s methods. Guided by these objective values, the set of candidate solutions are evolved through the HSA operators until an optimal solution is found. Experimental results demonstrate the high performance of the proposed method for the segmentation of digital images.

Ranking and compacting binding segments of protein families using aligned pattern clusters

BackgroundDiscovering sequence patterns with variation can unveil functions of a protein family that are important for drug discovery. Exploring protein families using existing methods such as multiple sequence alignment is computationally expensive, thus pattern search, called motif finding in Bioinformatics, is used. However, at present, combinatorial algorithms result in large sets of solutions, and probabilistic models require a richer representation of the amino acid associations. To overcome these shortcomings, we present a method for ranking and compacting these solutions in a new representation referred to as Aligned Pattern Clusters (APCs). To tackle the problem of a large solution set, our method reveals a reduced set of candidate solutions without losing any information. To address the problem of representation, our method captures the amino acid associations and conservations of the aligned patterns. Our algorithm renders a set of APCs in which a set of patterns is discovered, pruned, aligned, and synthesized from the input sequences of a protein family.ResultsOur algorithm identifies the binding or other functional segments and their embedded residues which are important drug targets from the cytochrome c and the ubiquitin protein families taken from Unitprot. The results are independently confirmed by pFam's multiple sequence alignment. For cytochrome c protein the number of resulting patterns with variations are reduced by 76.62% from the number of original patterns without variations. Furthermore, all of the top four candidate APCs correspond to the binding segments with one of each of their conserved amino acid as the binding residue. The discovered proximal APCs agree with pFam and PROSITE results. Surprisingly, the distal binding site discovered by our algorithm is not discovered by pFam nor PROSITE, but confirmed by the three-dimensional cytochrome c structure. When applied to the ubiquitin protein family, our results agree with pFam and reveals six of the seven Lysine binding residues as conserved aligned columns with entropy redundancy measure of 1.0.ConclusionThe discovery, ranking, reduction, and representation of a set of patterns is important to avert time-consuming and expensive simulations and experimentations during proteomic study and drug discovery.

Improving the Efficiency of Natural Computing Algorithms in DOA Estimation Using a Noise Filtering Approach

We propose a novel strategy to generate initial candidate solutions for bio-inspired algorithms applied to the direction of arrival estimation problem. The idea, which aims to improve the efficiency of the estimator, consists in using the frequency response of a well-known optimum noise reduction filter as the probability density function of the set of candidate solutions. In accordance to this approach, we also employ a modified likelihood function to reduce the estimation error. Simulation results considering an immune-inspired algorithm confirm a significant improvement of its performance and efficiency, and the new estimator reaches the conditional Cramer–Rao lower bound.

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,(5,12,20)) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization. Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods(3,4,9,10,13-15,17-19,22,23,25). In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model(7) with a multiobjective evolutionary algorithm SPEA2(26), and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

Estimation of operating blast furnace reactor invisible interior surface using Differential Evolution

A blast furnace reactor that transforms processed minerals into liquid iron is the most important link in a steel making process chain. Estimating the condition of refractory shield in a running furnace is important for production campaign planning and hazardous leakage prevention. A method coupling Differential Evolution with heat transfer simulations is developed for estimating refractory inner surface within process real times. The fitness value that drives this evolutionary algorithm is calculated from the differences between temperatures measured from thermocouples embedded in the refractory, and those obtained from numerical solution of the heat transfer equations. A population of different refractory inner surfaces constitutes the candidate solution set. These evolving surfaces tend to become craggy which impact variably on accuracy of numerical solution with significant consequence on the final synthesized solution. These inter-candidate accuracy variations are neutralized by transforming each physical refractory grid configuration onto identical rectangular computational domains using a set of continuity-ensuring elliptic partial differential equations. The heat transfer equation is correspondingly transformed into the computational space and solved within a finite difference framework. A set of practical constraints are imposed on the Differential Evolution algorithm to guide evolution toward the optimal surface that best represents the measured temperatures. Rigorous verification of the various stages of development facilitating industrial acceptance of the method are presented. This simulation-embedded evolutionary inverse-design approach can be used generically for online estimation of unobservable critical parameters of infrastructure, equipment and processes in diverse domains.

다목표 의사결정 방법론 기반의 수상함 획득대안 분석

획득대안 분석 프로세스는 비용, 일정, 성능 및 위험이라는 제약사항 내에서 다양한 후보 대안들 가운데에서 최적의 대안을 선택하는 것이다. 신규 무기체계 획득을 위해 사용하고 있는 기존의 대안 분석 방법은 일반적으로 요구사항 분석, 설계 조합, 그리고 비용 추정을 통해 수행되고 있다. 본 논문은 함정 설계개념 정제 및 물자적 대안분석 단계에서 다목표 의사결정 방법을 기반으로 개선된 획득대안 분석 방법을 제시한 것이다. 이번 연구에서는 시스템공학 원리를 기반으로 효과도 분석, 사업 비용 추정, 그리고 위험도 평가 기법을 활용하여 차세대 다목적 훈련지원함에 대한 실질적인 응용 및 적용 연구를 수행하였다. The process of an analysis of alternatives(AoA) attempts to select the best and balanced solution among a set of multiple candidate solutions under the constraints of cost, schedule, performance and risk(CSPR). The traditional AoA for the acquisition of a new weapon system has usually centered on the sequence of the requirement analysis, design synthesis, and cost estimation. An improved process for AoA is developed in this paper based on a multi-objective decision-making method, which is intended to be applied in the design concept refinement and material solution analysis stage for the acquisition of naval surface ships. The presentation of the proposed AoA approach is then followed by a case study for the next generation multi-purpose training ship based on the principles of systems engineering and also using the models of the effectiveness measure, cost estimation, and risk assessments.

Particle swarm optimization approach for protein structure prediction in the 3D HP model

The primary structure of proteins consists of a linear chain of amino acids that can vary in length. Proteins fold, under the influence of several chemical and physical factors, into their 3D structures, which determine their biological functions and properties. Misfolding occurs when the protein folds into a 3D structure that does not represent its native structure, which can lead to diseases. Due to the importance of this problem and since laboratory techniques are not always feasible, computational methods for characterizing protein structures have been proposed. In this paper, we present a particle swarm optimization (PSO) based algorithm for predicting protein structures in the 3D hydrophobic polar model. Starting from a small set of candidate solutions, our algorithm efficiently explores the search space and returns 3D protein structures with minimal energy. To test our algorithm, we used two sets of benchmark sequences of different lengths and compared our results to published results. Our algorithm performs better than previous algorithms by finding lower energy structures or by performing fewer numbers of energy evaluations.

Iterative simulation optimization approach for optimal volt-ampere reactive sources planning

This work proposes a two-phase iterative simulation optimization approach to solve the optimal volt-ampere reactive (VAR) sources planning problem. In the upper phase, an Ordinal Optimization (OO)-based method is employed as the optimization methodology, which performs ranking and selection to choose better solutions from a candidate solution set based on the structural information of system extracted from the simulation methodology performed in the lower phase. The simulation methodology, which is based on the Dual Projected Pseudo Quasi-Newton (DPPQN) method, is employed in the lower phase. The proposed OO-based approach consists of four levels. The size and solution quality of the resulting candidate solution set is reduced and improved level by level, and the best solution obtained in the final level is the good enough capacitor placement pattern that we seek. Via numerous tests, this work demonstrates the efficiency of the proposed approach with respect to the genetic algorithm (GA) and tabu search (TS) on the IEEE 118-bus system. Test results show that the proposed approach is an excellent alternative for the optimal VAR sources planning problems.

Multi-candidate missing data imputation for robust speech recognition

The application of Missing Data Techniques (MDT) to increase the noise robustness of HMM/GMM-based large vocabulary speech recognizers is hampered by a large computational burden. The likelihood evaluations imply solving many constrained least squares (CLSQ) optimization problems. As an alternative, researchers have proposed frontend MDT or have made oversimplifying independence assumptions for the backend acoustic model. In this article, we propose a fast Multi-Candidate (MC) approach that solves the per-Gaussian CLSQ problems approximately by selecting the best from a small set of candidate solutions, which are generated as the MDT solutions on a reduced set of cluster Gaussians. Experiments show that the MC MDT runs equally fast as the uncompensated recognizer while achieving the accuracy of the full backend optimization approach. The experiments also show that exploiting the more accurate acoustic model of the backend does pay off in terms of accuracy when compared to frontend MDT.