This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power gene...
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ISBN:
(纸本)9781467325950;9781467325967
This paper presents an evolutionaryalgorithm for analyzing the best mix of distributed generations (DG) in a distribution network. The multi-objective optimization aims at minimizing the total cost of real power generation, line losses and CO2 emissions, and maximizing the benefits from the DG over a 20 years planning horizon. The method assesses the fault current constraint imposed on the distribution network by the existing and new DG in order not to violate the short circuit capacity of existing switchgear. The analysis utilizes one of the highly regarded evolutionaryalgorithm, the Strength Pareto evolutionaryalgorithm 2 (SPEA2) for multi-objective optimization and MATPOWER for solving the optimal power flow problems.
Several multimedia networking services are based on multicast sessions. Due to high amount of traffic demanded by multicast applications, they need a system or mechanism to protect them from network failures and to ke...
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ISBN:
(纸本)9781614991397;9781614991380
Several multimedia networking services are based on multicast sessions. Due to high amount of traffic demanded by multicast applications, they need a system or mechanism to protect them from network failures and to keep the service when incidents are present. We propose the multicast Session Protection Planner (MSPP) as a tool to protect multicast services which uses an elitist multi-objective technique to determine a min-cost multicast tree. MSPP pursues minimizing the cost associated to establish redundant trees on a network with diverse decision variables, maximizing the network availability to satisfy a demand, and considering multiple failures. MSPP is tested over a generated initial topology which includes population demand over a core network. The simulation results shows that the proposed algorithm satisfies recovery resilience requirements of fault-tolerance varying topology. Results demonstrate that MSPP is an useful tool which finds new low-cost high-availability redundant trees, even if the number of failures increases.
Wheel-legged hybrid robots promise to combine the efficiency of wheeled robots with the versatility of legged robots: they are able to roll on simple terrains, to dynamically adapt their posture and even to walk on un...
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ISBN:
(纸本)9789814374279
Wheel-legged hybrid robots promise to combine the efficiency of wheeled robots with the versatility of legged robots: they are able to roll on simple terrains, to dynamically adapt their posture and even to walk on uneven grounds. Although different locomotion modes of such robots have been studied, a pivotal question remains: how to automatically adapt the locomotion mode when the environment changes? We here propose that the robot autonomously discovers its locomotion mode using optimization-based learning. To that aim, we introduce a new algorithm that relies on a forward model and a stochastic multi-objective optimization. Three objectives are optimized: (1) the average displacement speed, (2) the expended energy and (3) the transferability score, which reflects how well the behavior of the robot is in agreement with the predictions of the forward model. This transferability function is approximated by conducting 20 experiments of one second on the real robot during the optimization. In the three investigated situations (flat ground, grass-like terrain, tunnel-like environment), our method found efficient controllers for forward locomotion in 1 to 2 minutes: the robot used its wheels on the flat ground, it walked on the grass-like terrain and moved with a lowered body in the tunnellike environment.
In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. A Pareto optimal solution set is achieved when ...
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In optimization problems with at least two conflicting objectives, a set of solutions rather than a unique one exists because of the trade-offs between these objectives. A Pareto optimal solution set is achieved when a solution cannot be improved upon without degrading at least one of its objective criteria. This study investigated the application of multi-objective evolutionary algorithm (MOEA) and Pareto ordering optimization in the automatic calibration of the Soil and Water Assessment Tool (SWAT), a process-based, semi-distributed, and continuous hydrologic model. The nondominated sorting genetic algorithm II (NSGA-II), a fast and recent MOEA, and SWAT were called in FORTRAN from a parallel genetic algorithm library (PGAPACK) to determine the Pareto optimal set. A total of 139 parameter values were simultaneously and explicitly optimized in the calibration. The calibrated SWAT model simulated well the daily streamflow of the Calapooia watershed for a 3-year period. The daily Nash-Sutcliffe coefficients were 0.86 at calibration and 0.81 at validation. Automatic multi-objective calibration of a complex watershed model was successfully implemented using Pareto ordering and MOEA. Future studies include simultaneous automatic calibration of water quality and quantity parameters and the application of Pareto optimization in decision and policy-making problems related to conflicting objectives of economics and environmental quality.
Super 14 Rugby is not only a popular game, but also a hugely profitable business. However, determining a schedule for games in the competition is very difficult, as a number of different, often conflicting, factors mu...
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ISBN:
(纸本)9781424407040
Super 14 Rugby is not only a popular game, but also a hugely profitable business. However, determining a schedule for games in the competition is very difficult, as a number of different, often conflicting, factors must be considered. We propose the use of a multi-objective evolutionary algorithm for deciding such a schedule. We detail the technical details needed to apply a multi-objective evolutionary algorithm to this problem and report on experiments that show the effectiveness of this approach. We compare solutions found by our approach with recent fixtures employed by the organising authority;our results showing significant improvements over the existing solutions.
An ultra-wideband (UWB) planar antenna optimized by a newly developed multi-objective evolutionary algorithm- Jumping Genes Genetic algorithm (JGGA) is proposed. The optimization procedure aims at finding an antenna n...
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ISBN:
(纸本)9781424407484
An ultra-wideband (UWB) planar antenna optimized by a newly developed multi-objective evolutionary algorithm- Jumping Genes Genetic algorithm (JGGA) is proposed. The optimization procedure aims at finding an antenna not only with low voltage standing wave ratio (VSWR) and small size, but also with stable radiation pattern over the wide band. There are five objective functions and they are based on VSWR, antenna size, radiation pattern and gain. Both the simulated and measured results are given and they agree well with each other.
This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem...
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ISBN:
(纸本)9781424407040
This paper studies a multi-objective instance of the university exam timetabling problem. On top of satisfying universal hard constraints such as seating capacity and no overlapping exams, the solution to this problem requires the minimization of the timetable length as well as the number of occurrences of students having to take exams in consecutive periods within the same day. While most existing approaches to the problem, as well as the more popular single-objective instance, require prior knowledge of the desired timetable length, the multi-objective evolutionary algorithm proposed in this paper is able to generate feasible solutions even without the information. The effectiveness of the proposed algorithm is benchmarked against a few recent and established optimization techniques and is found to perform well in comparison.
This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives lik...
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This article deals with the comparative performance of some established multi-objective evolutionary algorithms (MOEAs) for structural topology optimization. Four multi-objective problems, having design objectives like structural compliance, natural frequency and mass, and subjected to constraints on stress, etc., are posed for performance testing. The MOEAs include Pareto archive evolution strategy (PAES), population-based incremental learning (PBIL), non-dominated sorting genetic algorithm (NSGA), strength Pareto evolutionaryalgorithm (SPEA), and multi-objective particle swarm optimization (MPSO). The various MOEAs are implemented to solve the problems. The ground element filtering (GEF) technique is used to suppress checkerboard patterns on topologies. The results obtained from the various optimizers are illustrated and compared. It is shown that PBIL is far superior to the others. The optimal topologies from using PBIL can be compared with those obtained by employing the classical gradient-based approach. It can be considered as a powerful tool for structural topological design.
A mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sendi...
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A mobile ad hoc network (MANET) is a collection of mobile nodes communicating through wireless connections without any prior network infrastructure. In such a network the broadcasting methods are widely used for sending safety messages and routing information. To transmit a broadcast message effectively in a wide and high mobility MANET (for instance in vehicular ad hoc network) is a hard task to achieve. An efficient communication algorithm must take into account several aspects like the neighborhood density, the size and shape of the network, the use of the channel. Probabilistic strategies are often used because they do not involve additional latency. Some solutions have been proposed to make their parameters vary dynamically. For instance, the retransmission probability increases when the number of neighbors decreases. But, the authors do not optimize parameters for various environments. This article aims at determining the best communication strategies for each node according to its neighborhood density. It describes a tool combining a network simulator (ns-2) and an evolutionaryalgorithm (EA). Five types of context are considered. For each of them, we tackle the best behavior for each node to determine the right input parameters. The proposed EA is first compared to three EAs found in the literature: two well-known EAs (NSGA-II and SPEA2) and a more recent one (DECMOSA-SQP). Then, it is applied to the MANET broadcasting problem. (C) 2011 Elsevier Ltd. All rights reserved.
In this paper, we propose an integrated model to incorporate inventory control decisions-such as economic order quantity, safety stock and inventory replenishment decisions-into typical facility location models, which...
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In this paper, we propose an integrated model to incorporate inventory control decisions-such as economic order quantity, safety stock and inventory replenishment decisions-into typical facility location models, which are used to solve the distribution network design problem. A simultaneous model is developed considering a stochastic demand, modeling also the risk poling phenomenon. multi-objective decision analysis is adopted to allow use of a performance measurement system that includes cost, customer service levels (fill rates), and flexibility (responsive level). This measurement system provides more comprehensive measurement of supply chain system performance than do traditional, single measure approaches. A multi-objective location-inventory model which permits a comprehensive trade-off evaluation for multi-objective optimization is initially presented. More specifically, a multiobjectiveevolutionaryalgorithm is developed to determine the optimal facility location portfolio and inventory control parameters in order to reach best compromise of these conflicting criteria. An experimental study using practical data was then illustrated for the possibility of the proposed approach. Computational results have presented promising solutions in solving a practical-size problem with 50 buyers and 15 potential DCs and proved to be an innovative and efficient approach for so called difficult-to-solve problems.
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