Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the param...
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ISBN:
(纸本)9789898111302
Expeditious modelling of virtual urban environments consists of generating realistic 3d models from limited information. It has several practical applications but typically suffers from a lack of accuracy in the parameter values that feed the modeller. By gathering small amounts of information about certain key urban areas, it becomes possible to feed a system that automatically compares and adjusts the input parameter values to find optimal solutions of parameter combinations that resemble the real life model. These correctly parameterized rules can then be reapplied to generate virtual models of real areas with similar characteristics to the referenced area. Based on several nature inspired metaheuristic algorithms such as genetic algorithms, simulated annealing and harmony search, this paper presents a new hybrid metaheuristic algorithm capable of optimizing functions with both discrete and continuous parameters and offer competitive results in a highly neglected field of application.
Urban road traffic is the foundation of the existence and development of urban society, therefore the calculation of the capacity of road network is an urgent problem. Based on the bi-level programming model of the ro...
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ISBN:
(纸本)9780769533575
Urban road traffic is the foundation of the existence and development of urban society, therefore the calculation of the capacity of road network is an urgent problem. Based on the bi-level programming model of the road network capacity, the concept of the level of network service is introduced. Considering the impact of network service level on the whole network, this paper has constructed a network capacity calculation model based on level of service of network, and solved it using a kind of hybrid optimization algorithm combined genetic algorithm and simulate annealing (GASA). Ultimately, a simple network has been provided to prove the model as well as the algorithm.
Micro-electronics component and circuit design requires long computation time;to reduce this time, the use of simplification techniques has been introduced. In order to obtain a first validation of the method, a first...
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ISBN:
(纸本)9781424416653
Micro-electronics component and circuit design requires long computation time;to reduce this time, the use of simplification techniques has been introduced. In order to obtain a first validation of the method, a first test case is presented;the simplification techniques have been applied to the analytical expression of Y parameters of an inductor equivalent circuit. The resulting expressions have been used in the fitting process in order to reproduce the behaviour of a simulated inductor. Five different optimization algorithms, both deterministic (POWELL and DIRECT) and stochastic (CRS, CRS ENHANCED and OPTIA) have been tested for the fitting. The result of the introduction of the simplification techniques has been the reduction of the running time during the fitting. From an optimization point of view, the best results have been obtained by the stochastic algorithms CRS, and OPTIA.
Maritime terminals of pure transhipment are emerging logistic realities in long-distance containerized trade. Here, complex activities of resource allocation and scheduling should be optimized in a dynamic, non determ...
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ISBN:
(纸本)9781424427086
Maritime terminals of pure transhipment are emerging logistic realities in long-distance containerized trade. Here, complex activities of resource allocation and scheduling should be optimized in a dynamic, non deterministic environment. The assignment of expensive quay cranes to multiple vessel-holds for container discharging and loading operations is a major problem, whose solution affects the operational performance of the whole terminal container. In OR literature, this problem is known as the quay crane scheduling problem. With the objective of minimizing the vessel's overall completion time, we first give our IP formulation and then, under the more realistic assumption that discharge-loading times are non deterministic, we focus on a simulation-based optimization approach which embodies the IP formulation. Two different simulation optimization algorithms are tailored to the problem: simulated annealing and adaptive balanced explorative and exploitative search. Preliminary numerical results are presented on real vessel data.
This paper aims to solve multi-objective problems by adaptive random search with intensification and diversification combined with genetic algorithm (RasID-GA). Problems with multi-objectives are common in engineering...
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ISBN:
(纸本)9784907764302
This paper aims to solve multi-objective problems by adaptive random search with intensification and diversification combined with genetic algorithm (RasID-GA). Problems with multi-objectives are common in engineering, economics, computer science, and many others field of studies. It has been a challenge for the researchers to develop algorithms able to solve this kind of problem. RasID is an optimization algorithm, which is good at finding local optima, but its diversified search isnpsilat so efficient, for this reason, we combined RasID with genetic algorithms (GA), which is superior at finding global optima. In this paper, RasID-GA is used to find the Pareto- optimal solutions. RasID-GA is compared with the algorithm of NSGA-II using well known benchmarks.
Prematurity is a troublesome problem that has to be faced and got rid of by many optimization algorithms, especially the Particle Swarm optimization (PSO). To combat with prematurity, this paper proposes a selfadaptiv...
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ISBN:
(纸本)9781424432806;9780769534497
Prematurity is a troublesome problem that has to be faced and got rid of by many optimization algorithms, especially the Particle Swarm optimization (PSO). To combat with prematurity, this paper proposes a selfadaptive casting net mechanism that is able to search global fitness efficiently. To keep diversity of particles, the self-adaptive casting net mechanism tunes parameters dynamically according to the number of iteration. Based on the proposed casting net mechanism, a novel Self-adaptive Casting Net-based Particle Swarm optimization (SCNPSO) is presented. Experiments were carried out to compare the standard PSO with SCNPSO with various parameters for selfadaptive and different strategies for moving based on benchmark functions of optimization. Experimental results show that SCNPSO outperforms PSO due to adjusting parameters self-adaptively and strategies for moving.
This paper formulates optimal control problems for rigid bodies in a geometric manner and it presents computational procedures based on this geometric formulation for numerically solving these optimal control problems...
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This paper formulates optimal control problems for rigid bodies in a geometric manner and it presents computational procedures based on this geometric formulation for numerically solving these optimal control problems. The dynamics of each rigid body is viewed as evolving on a configuration manifold that is a Lie group. Discrete-time dynamics of each rigid body are developed that evolve on the configuration manifold according to a discrete version of Hamilton's principle so that the computations preserve geometric features of the dynamics and guarantee evolution on the configuration manifold;these discrete-time dynamics are referred to as Lie group variational integrators. Rigid body optimal control problems are formulated as discrete-time optimization problems for discrete Lagrangian/Hamiltonian dynamics, to which standard numerical optimization algorithms can be applied. This general approach is illustrated by presenting results for several different optimal control problems for a single rigid body and for multiple interacting rigid bodies. The computational advantages of the approach, that arise from correctly modeling the geometry, are discussed.
Ant Colony Algorithm and Genetic Algorithm (GA), two bionic-inspired optimization algorithms, have great potentials to solve the combination optimization problems, respectively used in solving traveling salesman probl...
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ISBN:
(纸本)9780769533162
Ant Colony Algorithm and Genetic Algorithm (GA), two bionic-inspired optimization algorithms, have great potentials to solve the combination optimization problems, respectively used in solving traveling salesman problem, but there are some shortcomings if only one of them is used to solve TSP. Performance comparative analysis have been done by using ACA and GA respectively in solving TSP in this paper. The experiments show the advantages and disadvantages used only ACA or GA, we can overcome the shortcomings if GA and ACA are combined to solve TSP and get faster convergent speed and more accurate results compared with only using ACA or GA.
This paper presents a simple mathematical analysis of some features of the Harmony Search algorithm (HS). HS is a recently developed derivative-free optimization algorithm, which draws inspiration from the musical pro...
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ISBN:
(纸本)9781424429165
This paper presents a simple mathematical analysis of some features of the Harmony Search algorithm (HS). HS is a recently developed derivative-free optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work analyses the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. Experimental results have been provided to validate the theoretical treatment. A simple modification of the classical HS has been proposed in the light of the mathematical analysis undertaken here.
Aggregate measures summarizing subsets of data are valuable in exploratory analysis and decision support, especially when dependent aggregations can be easily specified and computed. A novel class of queries, called c...
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ISBN:
(纸本)9781424418367;1424418364
Aggregate measures summarizing subsets of data are valuable in exploratory analysis and decision support, especially when dependent aggregations can be easily specified and computed. A novel class of queries, called composite subset measures, was previously introduced to allow correlated aggregate queries to be easily expressed. This paper considers how to evaluate composite subset measure queries using a large distributed system. We describe a cross-node data redistribution strategy that takes into account the nested structure of a given query. The main idea is to group data into blocks in "cube space", such that aggregations can be generated locally within each block, leveraging previously proposed optimizations per-block. The partitioning scheme allows overlap among blocks so that sliding window aggregation can be handled. Furthermore, it also guarantees that the final answer is the union of local results with no duplication and there is no need for the expensive data combination step. We identify the most important partitioning parameters and propose an optimization algorithm. We also demonstrate effectiveness of the optimizer to minimize the query response time.
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