This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution ...
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ISBN:
(纸本)9781538621349
This paper presents a new Modified Fruit Fly Optimization Algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution law in MFOA for some of the fruit flies improves searching diversity in earlier iterations and increases solution precession in last iterations. In order to apply the PID controllers to the robot manipulator, a nonlinear feedback linearization control technique is employed which can fully linearize and decouple nonlinear robot's dynamics. Simulation results confirm that the MFOA-PID controller can achieve better closed-loop system responses with respect to the original FOA-PID controller.
We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defense strategies through modeling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer ne...
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ISBN:
(纸本)9781450349390
We introduce a new cybersecurity project named RIVALS. RIVALS will assist in developing network defense strategies through modeling adversarial network attack and defense dynamics. RIVALS will focus on peer-to-peer networks and use coevolutionary algorithms. In this contribution, we describe RIVALS' current suite of coevolutionary algorithms that use archiving to maintain progressive exploration and that support different solution concepts as fitness metrics. We compare and contrast their effectiveness by executing a standard coevolutionary benchmark (Compare-on-one) and RIVALS simulations on 3 different network topologies. Currently, we model denial of service (DOS) attack strategies by the attacker selecting one or more network servers to disable for some duration. Defenders can choose one of three different network routing protocols: shortest path, flooding and a peer-to-peer ring overlay to try to maintain their performance. Attack completion and resource cost minimization serve as attacker objectives. Mission completion and resource cost minimization are the reciprocal defender objectives. Our experiments show that existing algorithms either sacrifice execution speed or forgo the assurance of consistent results. rIPCA, our adaptation of a known coevolutionary algorithm named IPCA, is able to more consistently produce high quality results, albeit without IPCA's guarantees for results with monotonically increasing performance, without sacrificing speed.
We present an optimization platform for turbomachineries with complex mesh configuration in a parallel computation environment. A continuous adjoint solver for 3-D viscous internal flow is coded under the same paralle...
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ISBN:
(纸本)9780791850794
We present an optimization platform for turbomachineries with complex mesh configuration in a parallel computation environment. A continuous adjoint solver for 3-D viscous internal flow is coded under the same parallel framework as the flow solver. To meet the various permitted extents of reshaping on blade surface and cut down the computation cost in grid perturbation, a localized two-level mesh deformation method is developed based on Gaussian radial basis function (RBF). This method works efficiently for both the 0 mesh surrounding the blade and the O-H mesh blocks inside tip gap. In optimization of the transonic NASA Rotor 67 for higher adiabatic efficiency with a mass flow rate constraint, an adjoint sensitivity analysis is conducted. The relations between the design sensitive regions and physical phenomena in internal flow are discussed. Flow fields before and after adjoint optimization are investigated, including shock system, tip leakage flow and flow separation.
The basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discuss...
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ISBN:
(纸本)9781538621653
The basic information required to utilize one of possible computation tools/algorithms (mainly the evolution strategy) to solve a wide class of real practical engineering optimization problems is presented and discussed in the present paper. The effectiveness of the considered method is demonstrated by the possibility of the use of different form of objective functions, various and numerous nonlinear constraints and different types of design variables (continuous, discrete, real, integer). The sensitivity of the algorithm to the choice of the evolution strategy parameters is also discussed herein. The generality of the evolution strategy is illustrated by the analysis of three examples dealing with: the design of helical springs, the buckling of cylindrical composite panels and the buckling of pressure vessels with domed heads.
In this paper, we study the effect of Meta-Lamarckian learning on the performance of a generic hybrid Multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) to solve a well-known combinatorial Multi-Ob...
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ISBN:
(纸本)9781538637319
In this paper, we study the effect of Meta-Lamarckian learning on the performance of a generic hybrid Multi-objective evolutionary Algorithm based on Decomposition (MOEA/D) to solve a well-known combinatorial Multi-Objective Optimization (MOO) problem. We study the hybridization of MOEA/D with a pool of six general-purpose heuristics so as to locally optimize the solutions during the evolution. We initially consider the six individualistic hybrid MOEA/D's, in which at every step of the evolution the same local search heuristic from the generic pool is applied. MOEA/D is then enriched with a learning strategy that, based on the problem's properties and objective functions, adaptively selects at each step of the evolution and for each problem neighbourhood the best performing local search heuristic from the generic pool of heuristics. The proposed method is evaluated on various test instances of a multi-objective Permutation Flow Shop Scheduling Problem (MO-PFFSP): given a set of jobs and a series of machines, the corresponding processing time of each job on every machine and the due dates of each job, determine a processing order of the jobs on each machine, so as to simultaneously minimize the makespan (total completion time), and the maximum job tardiness. The results of our experimental studies suggest that the proposed method successfully learns the behaviour of individual local search heuristics during the evolution outperforming in terms of both convergence and diversity the conventional MOEA/D and the individualistic hybrid MOEA/D's. The proposed method does not utilize any problem-specific heuristics, and as a result, is readily applicable to other combinatorial MOO problems.
In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the...
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In order to effectively design nearly Zero Energy Buildings, the assessment of energy performance in the early design stages through simulation is an important, although very demanding and complex, procedure. Over the last decades, various tools and methods have been developed to address performance-related design questions, mostly using Multi-Objective Optimization algorithms. Technological advances have revolutionized the way Architects design and think, automating complex tasks and allowing the assessment of multiple variants at the same time. In this paper, a new nZEB design workflow methodology is proposed, integrating evolutionary algorithms and energy simulation, and its capabilities and current limitations are explored. (C) 2017 The Authors. Published by Elsevier Ltd.
Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time;evolutionary algorithms are perhaps the most well-known examples of meta-h...
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ISBN:
(纸本)9781510855076
Meta-heuristics are algorithms which are applied to solve problems when conventional algorithms can not find good solutions in reasonable time;evolutionary algorithms are perhaps the most well-known examples of meta-heuristics. As there are many possible meta-heuristics, finding the most suitable meta-heuristic for a given problem is not a trivial task. In order to make this choice, one can design hyperheuristics. In the literature, one can find some agent-based research whose focus is to propose a framework where metaheuristics are considered as agents, that solve a given problem in a collaborative or competitive way. Most of these works focus on mono-objective meta-heuristics. Other works focus on how to select multi-objective meta-heuristics, but not using an agent-based approach. We present in this work an agent-based hyper-heuristic for choosing the most suitable evolutionary meta-heuristic for a given problem. Our approach performs a cooperative Copeland voting procedure, considering five different metrics, to de fine which one of three competitive evolutionary meta-heuristics should execute during a certain processing time. We use the Walking Fish Problem (WFG) suite with two and three objectives to analyse the proposed approach performance. The obtained results showed that in all cases our strategy found the most indicated evolutionary algorithm and gets competitive results against the state of art.
Introduction: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the d...
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Introduction: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. Objectives: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. Methods: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery' as well as other detection methods in the previous studies. Results: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute app
When geo-locating ground objects from a UAV, multiple views of the same object can lead to improved geolocation accuracy. Of equal importance to the location estimate, however, is the uncertainty estimate associated w...
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ISBN:
(数字)9781510609006
ISBN:
(纸本)9781510608993;9781510609006
When geo-locating ground objects from a UAV, multiple views of the same object can lead to improved geolocation accuracy. Of equal importance to the location estimate, however, is the uncertainty estimate associated with that location. Standard methods for estimating uncertainty from multiple views generally assume that each view represents an independent measurement of the geo-location. Unfortunately, this assumption is often violated due to correlation between the location estimates. This correlation may occur due to the measurements coming from the same platform, meaning that the error in attitude or location may be correlated across time;or it may be due to external sources (such as GPS) having the same error in multiple aircraft. In either case, the geo-location estimates are not truly independent, leading to optimistic estimates of the geo-location uncertainty. For distributed data fusion applications, correlation-agnostic fusion methods have been developed that can fuse data together regardless of how much correlation may be present between the two estimates. While the results are generally not as impressive as when correlation is perfectly known and taken into account, the fused uncertainty results are guaranteed to be conservative and an improvement on operating without fusion. In this paper, we apply a selection of these correlation-agnostic fusion techniques to the multi-view geo-location problem and analyze their effects on geo-location and predicted uncertainty accuracy. We find that significant benefits can be found from applying these correlation agnostic fusion effects, but that they vary greatly in how well they estimate their own uncertainty.
Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goa...
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ISBN:
(纸本)9781538623879
Swarm of drones are increasingly deployed to perform a variety of critical missions such as surveillance, rescue in disaster areas etc. To guarantee success of a mission, the controlling software should pursue two goals. Firstly, it should ensure safety, i.e., guarantee collision avoidance. Secondly, it should prevent a premature depletion of the batteries of the drones by minimizing their travel paths. In this paper, we propose an approach that combines run-time safety monitoring and high performance evolutionary algorithm to predict dynamically emerging hazards. High performance of the route calculation algorithm allows us to ensure that the routes of drones are dynamically adjusted to avoid collisions while maintaining efficiency. The benchmarking of the proposed approach validates its efficiency and safety.
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