Set Of Candidate Solutions Research Articles

Handling changes of performance requirements in multi-objective problems

In this paper, the need for rapid, low-cost changes in a design, in response to changes in performance requirements (PRs), within multi-objective problems, is considered. In the current study, the rapid response is attained through a priori design of a set of satisfying solutions, such that any PR may be satisfied by at least one member of the set. The purpose is to design such a set so that once the PRs change, the changes needed in order to adapt to the existing product (one member of the set) to the new requirements are minimal, while maintaining the aspiration for optimal performances. It is assumed here that minimal changes are related to small changes in the design parameters. In order to find the optimal set, sets of candidate solutions are evolved using an evolutionary multi-objective optimisation algorithm. The algorithm enhances a search pressure towards sets with minimal distances between their members (in design space) and with optimal performances, which are assessed by utilising the hyper-volume measure. An artificial and a real life example are utilised in order to explain the approach and to show its applicability to engineering problems.

Population-Based Metaheuristic Approximation For Dosimetric Optimization in CyberKnife Radiation Therapy Treatment Planning

Population-Based Metaheuristic Approximation For Dosimetric Optimization in CyberKnife Radiation Therapy Treatment Planning

Comparison of computational performance of GA and PSO optimization techniques when designing similar systems – Typical PWR core case

This paper compares the performance of two optimization techniques, particle swarm optimization (PSO) and genetic algorithm (GA) applied to the design a typical reduced scale two loop Pressurized Water Reactor (PWR) core, at full power in single phase forced circulation flow. This comparison aims at analyzing the performance in reaching the global optimum, considering that both heuristics are based on population search methods, that is, methods whose population (candidate solution set) evolve from one generation to the next using a combination of deterministic and probabilistic rules. The simulated PWR, similar to ANGRA 1 power plant, was used as a case example to compare the performance of PSO and GA. Results from simulations indicated that PSO is more adequate to solve this kind of problem.

A Hybrid Fuzzy DEMATEL-VIKOR Method for Product Concept Evaluation

The product concept evaluation plays a very important role in product development. It can be considered as multi-criteria decision making (MCDM) problem. In a MCDM problem, a decision maker has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. This paper introduces a compromise MCDM evaluation model which combines fuzzy DEMATEL and VIKOR methods. The criteria weights are determined based on separate cause-effect assessment of a group of experts using the fuzzy DEMATEL approach. The resulting criteria weights are applied to the decision analysis where the fuzzy VIKOR approach is adopted which determines a compromise solution by using utility weight.

Generating Pareto Surface for Multi Objective Integer Programming Problems with Stochastic Objective Coefficients

Stochastic multi objective programming problems commonly arise in complex systems such as portfolio analysis, medium- to long-term capacity planning and design applications under uncertainty. The identification of the candidate solution set is a main step in many applications which depends on the nature of uncertainty. This study presents a method to generate Pareto surface for multi-objective integer programs with stochastic coefficients in the objective functions based on minimum expectation and variance criteria. The objective function coefficients are represented through random discrete distributions. The methodology follows a two-phase approach where, in the first phase, the stochastic multiple objectives are converted into deterministic equivalents based on the minimum expectation and variance efficiency concepts. The second phase solves the deterministic multi objective problem, using a Pareto generation methodology which aims at generating the whole Pareto surface of multi objective integer programming problems. We present results of experimental study of applying the proposed method to an assignment problem with three objective functions.

Immune system memetic algorithm for power distribution network design with load evolution uncertainty

A recent paper, [1], has proposed a methodology for taking into account uncertainties in the load evolution within the design of electric distribution networks. That paper has presented an immunological algorithm that is used for finding a set of solutions which are sub-optimal under the viewpoint of the “mean scenario” load conditions, and which are submitted to a sensitivity analysis for the load uncertainty. This paper presents a further development of the algorithm presented in [1], employing now a memetic algorithm (an algorithm endowed with local search operators) instead of the original immunological algorithm. The new algorithm is shown to present a better behavior, achieving a better set of candidate solutions, which dominate the solution set of the former algorithm. The solution set of the proposed algorithm is also stable, in the senses that: (i) the same set of solutions is found systematically; and (ii) the merit function values associated to those solutions vary smoothly from one solution to another one. It can be concluded that the design procedure proposed in [1] should be performed preferentially with the algorithm proposed here.

Deriving preference order of open pit mines equipment through MADM methods: Application of modified VIKOR method

In multiple attribute decision making (MADM) problem, a decision maker (DM) has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. It is generally hard to find an alternative that meets all the criteria simultaneously, so a good compromise solution is preferred. The VIKOR method was developed for multi-criteria optimization of complex systems. This method focuses on ranking and selecting from a set of alternatives in the presence of conflicting criteria. It introduces the multi-criteria ranking index based on the particular measure of “closeness” to the “ideal” solution. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in decision process, this paper presents an evaluation model based on deterministic data, fuzzy numbers, interval numbers and linguistic terms. Combination of analytic hierarchy process (AHP) and entropy method was applied for attribute weighting in this proposed MADM method. To demonstrate the potential of the methodology, the proposed method is used for surface mine equipment selection problems.

Minmax-distance approximation and separation problems: geometrical properties

A center hyperplane in the d-dimensional space minimizes the maximum of its distances from a finite set of points A with respect to possibly different gauges. In this note it is shown that a center hyperplane exists which is at (equal) maximum distance from at least d + 1 points of A. Moreover the projections of the points among these which lie above the center hyperplane cannot be separated by another hyperplane from the projections of those that are below it. When all gauges involved are smooth, all center hyperplanes satisfy these properties. This geometric property allows us to improve and generalize previously existing results, which were only known for the case in which all distances are measured using a common norm. The results also extend to the constrained case where for some points it is prespecified on which side of the hyperplane (above, below or on) they must lie. In this case the number of points lying on the hyperplane plus those at maximum distance is at least d + 1. It follows that solving such global optimization problems reduces to inspecting a finite set of candidate solutions. Extensions of these results to a separation problem are outlined.

The Scheduling Algorithm of Grid Task Based on PSO and Cloud Model

Grid task scheduling (GTS) is a NP-hard problem. This paper proposes an optimized GTS algorithm which combines with the advantages of cloud model based on the particle swarm optimization algorithm. This algorithm iterates tasks utilizing the advantages of particle swarm optimization algorithm and then gets a set of candidate solutions quickly. In addition, this algorithm modifies the value of entropy and excess entropy using the characteristics of cloud model and implements the transformation between qualitative variables and quantity of uncertain events. And this algorithm makes particles fly to the global optimal solutions by exact searching in local areas. Theoretical analysis and simulation results show that this algorithm makes load balance of resource efficiently. It also avoids the problems of genetic algorithm and basic particle swarm optimization algorithm, which would easily fall into local optimal solutions and premature convergence caused by too much selected pressure. This algorithm has the advantages of high precision and faster convergence and can be applied in task scheduling on computing grid.

Evolutionary Computation for Remote Sensing Applications

AbstractAs the volume of available remotely sensed imagery increases so does the need to extract more specific information in a timely and cost‐effective fashion to enhance and/or update decision support systems. This manuscript provides an overview of the existing image information extraction techniques using evolutionary computation algorithms. Emphasis is given to remote sensing applications. The literature investigated is further divided into four groups based on the research objectives: image enhancement, image classification, feature extraction, and modeling. Some of the strengths of evolutionary computation are discussed, such as robust classification capabilities and ability to work with linear and non‐linear problems. Conversely, the works reviewed reveal some of the challenges in the integration of evolutionary computation and remote sensing as well as possible solutions. Limited work was found addressing the multi‐class image classification problem and assessment of the randomness involved in the generation of the first set of candidate solutions from which the algorithm begins. Based on our study, the use of evolutionary computation algorithms for information extraction from imagery is likely to grow, specifically in two areas: inverse modeling and object‐based approaches. This technology is still in its early stages of development and the capabilities of evolutionary computation are well suited to the complexities of the image processing and information extraction problems.

Acoustic Sensor Planning for Gunshot Location in National Parks: A Pareto Front Approach

In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabaneros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario.

Solving Complex Puzzles: Automated Protein Complex Assembly From Cryo-Electron Microscopy Data Via Multi-Resolution Modeling

Currently, one of the central problems in structural biology is the better understanding of large protein complexes. Cryo-electron microscopy has made a significant impact in this area due to its ability to image proteins in a near-native environment. Multi-resolution docking approaches then allow atomic models of the whole complex to be built. However, all existing tools only dock one subunit at a time, leaving the user to sift through dozens or hundreds of possible solution candidates to assemble the complete protein complex. Essentially, choosing the correct solutions from the candidate solution list is equivalent to solving the Knapsack problem (KP). The general case of the KP has been extensively studied in the field of computer science and is extremely compute intensive to solve. In the present case, we are interested in a set of candidate solutions without any steric clashes and maximal overall agreement with the experimental volumetric map of the protein complex. We developed a Knapsack solver targeted specifically at the assembly of protein complexes, exploiting knowledge of this problem domain to reduce the overall computational load. The solver first clusters the raw docking data to locate possible subunit neighborhoods, significantly cutting down the overall search space. Sets of candidates, consisting of one possible solution from each region, are then screened for steric clashes using a fast, octree based, clashing detector. Sets with large amounts of steric hindrance between subunits are discarded. The remaining sets are ranked by the sum of all CC scores of the contained candidates. Finally, the best complete docking solution is output. Besides details about our novel assembly algorithm, the present report examines its performance on both simulated and experimental data sets.

On the Floquet problem for second-order Marchaud differential systems

Solutions in a given set of the Floquet boundary value problem are investigated for second-order Marchaud systems. The methods used involve a fixed point index technique developed by ourselves earlier with a bound sets approach. Since the related bounding (Liapunov-like) functions are strictly localized on the boundaries of parameter sets of candidate solutions, some trajectories are allowed to escape from these sets. The main existence and localization theorem is illustrated by two examples for periodic and anti-periodic problems.

Evaluation of software development projects using a fuzzy multi-criteria decision approach

Software development is an inherently uncertain activity. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in decision process, this paper presents an evaluation model based on the fuzzy multi-criteria decision-making (MCDM) method for measuring the performance of software development projects. In an MCDM problem, a decision maker (DM) has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. It is generally hard to find an alternative that meets all the criteria simultaneously, so a good compromise solution is preferred. This problem may become more complex when multiple DMs are involved, each having not a common perception on the alternatives. Recently, a compromise ranking method (known as the VIKOR method) has been proposed to identify such compromise solutions, by providing a maximum group utility for the majority and a minimum of an individual regret for the opponent. In its actual setting, the method treats exact values for the assessment of the alternatives, which can be quite restrictive with unquantifiable criteria. This will be true especially if the evaluation is made by means of linguistic terms. For this reason we extend the VIKOR method so as to process such data and to provide a more comprehensive evaluation in a fuzzy environment. To demonstrate the potential of the methodology, the proposed extension is used for measuring the performance of enterprise resource planning (ERP) software products.

An Implementation of an Initial Scale in Solving Binary Knapsack Problem Using a Genetic Algorithm

In this paper, we used a new operation in a Genetic Algorithm for solving the binary Knapsack problem depending on it 's LP Relaxation solution after eliminating the fractional part of the non-binary values. The benefit is to make a filter to the initial random population from the farness of the optimal solution and unsuitable chromosomes. This good property will be fixed automatically in all generations in the Genetic Algorithm until reaching the optimal binary solution. 1. Introduction to Genetic Algorithm: A Genetic Algorithm (GA) was first introducted by John Holland for the formal investigation of the mechanisims of natural adaptation but the algorithm has been since modified to solve computational search problems, GA has been used to solve the NP-hard combinatorial problems effectively such as Knapsack Problem.(2) GA is a heuristic search algorithm for the solution of optimization problems in which starting from a random initial guess solution, better descendants are tried in an attempt to find one that is the best under some criteria and conditions.(5) GA is a computer algorithm that searches for good solutions to a problem from among a large number of possible solutions. It begins with a set of candidate solutions (chromosomes) called population. A new population is created from solutions of an old population in hope of getting a better population. Solutions which are chosen to the new solutions

Self-Diagnosis and Self-Repair of an Active Tensegrity Structure

This paper focuses on the study of tensegrity active control in situations of partially defined loading and damage events. Self-diagnosis and self-repair methodologies are proposed and validated experimentally on a full-scale active tensegrity structure. Self-diagnosis involves load identification and damage location. The response of the structure to a load and damage is measured and compared with the response of the structure to candidate solutions for load and damage. Self-diagnosis solutions result in sets of candidate solutions for loads and damage. These solutions are employed to compute control commands of shape control and self-repair for the structure. Self-repairing control commands increase stiffness and decrease stresses with respect to the damaged state. Self-repairing control commands are computed using a multiobjective approach. The application of a control command results in self-stress modifications by changing the length of active struts. The proposed methodologies are attractive for tensegrity active control in situations of partially defined loading and damage events.

The Use of Probabilistic Reasoning to Improve Least Squares Based Gas Path Diagnostics

A method is proposed to support least square type of methods for deriving health parameters from a small number of independent gas path measurements. The method derives statistical information using sets of solutions derived from a number of data records, to produce sets of candidate solutions with a lesser number of parameters. These sets can then be processed to derive an accurate component fault diagnosis. It could thus be classified as a new type of "concentrator" approach, which is shown to be more effective than previously existing schemes. The method's effectiveness is demonstrated by application to a number of typical jet engine component faults.

A multi-objective approach to facility layout problem by genetic search algorithm and Electre method

Classical approaches to layout design problem tend to maximise the efficiency of layout, measured by the handling cost related to the interdepartmental flow and to the distance among the departments. However, the actual problem involves several conflicting objectives hence requiring a multi-objective formulation. Multi-objective approaches, recently proposed, in most cases lead to the maximisation of a weighted sum of score functions. The poor practicability of such an approach is due to the difficulty of normalising these functions and of quantifying the weights. 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 a multi-objective constrained genetic algorithm 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 (handling cost) and qualitative (adjacency and distance requests between departments) objectives are considered referring to a bay structure-based layout model, that allows to take into account also practical constraints such as the aspect ratio of departments. Results obtained confirm the effectiveness of the proposed procedure as a practicable support tool for layout designers.

An Ordinal Optimization Theory-Based Algorithm for Solving the Optimal Power Flow Problem With Discrete Control Variables

The optimal power flow (OPF) problem with discrete control variables is an NP-hard problem in its exact formulation. To cope with the immense computational-difficulty of this problem, we propose an ordinal optimization theory-based algorithm to solve for a good enough solution with high probability. Aiming for hard optimization problems, the ordinal optimization theory, in contrast to heuristic methods, guarantee to provide a top n% solution among all with probability more than 0.95. The approach of our ordinal optimization theory-based algorithm consists of three stages. First, select heuristically a large set of candidate solutions. Then, use a simplified model to select a subset of most promising solutions. Finally, evaluate the candidate promising-solutions of the reduced subset using the exact model. We have demonstrated the computational efficiency of our algorithm and the quality of the obtained solution by comparing with the competing methods and the conventional approach through simulations.

Dynamic systems modelling using genetic programming

In this contribution genetic programming (GP) is used to evolve dynamic process models. An innovative feature of the GP algorithm is its ability to automatically discover the appropriate time history of model terms required to build an accurate model. Two case studies are used to compare the performance of the GP algorithm with that of filter-based neural networks (FBNNs). Although the models generated using GP have comparable prediction performance to the FBNN models, a disadvantage is that they required greater computational effort to develop. However, we show that a major benefit of the GP approach is that additional model performance criteria can be included during the model development process. The parallel nature of GP means that it can evolve a set of candidate solutions with varying levels of performance in each objective. Although any combination of model performance criteria could be used as objectives within a multi-objective GP (MOGP) framework, the correlation tests outlined by Billings and Voon (Int. J. Control 44 (1986) 235) were used in this work.