38,820 research outputs found
Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria using Interactive Genetic Algorithms
This paper emphasizes the necessity of formally bringing qualitative and
quantitative criteria of ergonomic design together, and provides a novel
complementary design framework with this aim. Within this framework, different
design criteria are viewed as optimization objectives; and design solutions are
iteratively improved through the cooperative efforts of computer and user. The
framework is rooted in multi-objective optimization, genetic algorithms and
interactive user evaluation. Three different algorithms based on the framework
are developed, and tested with an ergonomic chair design problem. The parallel
and multi-objective approaches show promising results in fitness convergence,
design diversity and user satisfaction metrics
Improved dynamical particle swarm optimization method for structural dynamics
A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.Peer ReviewedPostprint (published version
A Distance Based Method for Solving Multi-objective Optimization Problems
A new model for the weighted method of goal programming is proposed based on minimizing the distances between ideal objectives to feasible objective space. It provides the best compromised solution for Multi Objective Linear Programming Problems (MOLPP). The proposed model tackles MOLPP by solving a series of single objective sub-problems, where the objectives are transformed into constraints. The compromise solution so obtained may be improved by defining priorities in terms of the weight. A criterion is also proposed for deciding the best compromise solution. Applications of the algorithm are discussed for transportation and assignment problems involving multiple and conflicting objectives. Numerical illustrations are given for the proposed model
Network Flexibility for Recourse Considerations in Bi-Criteria Facility Location
What is the best set of facility location decisions for the establishment of a logistics network when it is uncertain how a companyâs distribution strategy will evolve? What is the best configuration of a distribution network that will most likely have to be altered in the future? Todayâs business environment is turbulent, and operating conditions for firms can take a turn for the worse at any moment. This fact can and often does influence companies to occasionally expand or contract their distribution networks. For most companies operating in this chaotic business environment, there is a continuous struggle between staying cost efficient and supplying adequate service. Establishing a distribution network which is flexible or easily adaptable is the key to survival under these conditions. This research begins to address the problem of locating facilities in a logistics network in the face of an evolving strategic focus through the implicit consideration of the uncertainty of parameters. The trade-off of cost and customer service is thoroughly examined in a series of multi-criteria location problems. Modeling techniques for incorporating service restrictions for facility location in strategic network design are investigated. A flexibility metric is derived for the purposes of quantifying the similarity of a set of non-dominated solutions in strategic network design. Finally, a multi-objective greedy random adaptive search (MOG) metaheuristic is applied to solve a series of bi-criteria, multi-level facility location problems
Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information
Several contributions to the hydrological literature have brought into question the continued usefulness of the classical paradigm for hydrologic model calibration. With the growing popularity of sophisticated 'physically based' watershed models (e.g., landsurface hydrology and hydrochemical models) the complexity of the calibration problem has been multiplied many fold. We disagree with the seemingly widespread conviction that the model calibration problem will simply disappear with the availability of more and better field measurements. This paper suggests that the emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multiobjective nature of the problem and must explicitly recognize the role of model error. The results of our preliminary studies are presented. Through an illustrative case study we show that the multiobjective approach is not only practical and relatively simple to implement but can also provide useful information about the limitations of a model
Identifying Security-Critical Cyber-Physical Components in Industrial Control Systems
In recent years, Industrial Control Systems (ICS) have become an appealing
target for cyber attacks, having massive destructive consequences. Security
metrics are therefore essential to assess their security posture. In this
paper, we present a novel ICS security metric based on AND/OR graphs that
represent cyber-physical dependencies among network components. Our metric is
able to efficiently identify sets of critical cyber-physical components, with
minimal cost for an attacker, such that if compromised, the system would enter
into a non-operational state. We address this problem by efficiently
transforming the input AND/OR graph-based model into a weighted logical formula
that is then used to build and solve a Weighted Partial MAX-SAT problem. Our
tool, META4ICS, leverages state-of-the-art techniques from the field of logical
satisfiability optimisation in order to achieve efficient computation times.
Our experimental results indicate that the proposed security metric can
efficiently scale to networks with thousands of nodes and be computed in
seconds. In addition, we present a case study where we have used our system to
analyse the security posture of a realistic water transport network. We discuss
our findings on the plant as well as further security applications of our
metric.Comment: Keywords: Security metrics, industrial control systems,
cyber-physical systems, AND-OR graphs, MAX-SAT resolutio
A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean
The purpose of this paper is to provide a hierarchical dynamic mission
planning framework for a single autonomous underwater vehicle (AUV) to
accomplish task-assign process in a limited time interval while operating in an
uncertain undersea environment, where spatio-temporal variability of the
operating field is taken into account. To this end, a high level reactive
mission planner and a low level motion planning system are constructed. The
high level system is responsible for task priority assignment and guiding the
vehicle toward a target of interest considering on-time termination of the
mission. The lower layer is in charge of generating optimal trajectories based
on sequence of tasks and dynamicity of operating terrain. The mission planner
is able to reactively re-arrange the tasks based on mission/terrain updates
while the low level planner is capable of coping unexpected changes of the
terrain by correcting the old path and re-generating a new trajectory. As a
result, the vehicle is able to undertake the maximum number of tasks with
certain degree of maneuverability having situational awareness of the operating
field. The computational engine of the mentioned framework is based on the
biogeography based optimization (BBO) algorithm that is capable of providing
efficient solutions. To evaluate the performance of the proposed framework,
firstly, a realistic model of undersea environment is provided based on
realistic map data, and then several scenarios, treated as real experiments,
are designed through the simulation study. Additionally, to show the robustness
and reliability of the framework, Monte-Carlo simulation is carried out and
statistical analysis is performed. The results of simulations indicate the
significant potential of the two-level hierarchical mission planning system in
mission success and its applicability for real-time implementation
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