Automatic surrogate model type selection during the optimization of expensive black-box problems

The use of Surrogate Based Optimization (SBO) has become commonplace for optimizing expensive black-box simulation codes. A popular SBO method is the Efficient Global Optimization (EGO) approach. However, the performance of SBO methods critically depends on the quality of the guiding surrogate. In EGO the surrogate type is usually fixed to Kriging even though this may not be optimal for all problems. In this paper the authors propose to extend the well-known EGO method with an automatic surrogate model type selection framework that is able to dynamically select the best model type (including hybrid ensembles) depending on the data available so far. Hence, the expected improvement criterion will always be based on the best approximation available at each step of the optimization process. The approach is demonstrated on a structural optimization problem, i.e., reducing the stress on a truss-like structure. Results show that the proposed algorithm consequently finds better optimums than traditional kriging-based infill optimization

A Planning and Optimization Framework for Hybrid Ultra-Dense Network Topologies

The deployment of small cells has been a critical upgrade in Fourth Generation (4G) mobile networks as they provide macrocell traffic offloading gains, improved spectrum reuse and reduce coverage holes. The need for small cells will be even more critical in Fifth Generation (5G) networks due to the introduction of higher spectrum bands, which necessitate denser network deployments to support larger traffic volumes per unit area. A network densification scenario envisioned for evolved fourth and fifth generation networks is the deployment of Ultra-Dense Networks (UDNs) with small cell site densities exceeding 90 sites/km2 (or inter-site distances of less than 112 m). The careful planning and optimization of ultra-dense networks topologies have been known to significantly improve the achievable performance compared to completely random (unplanned) ultra-dense network deployments by various third-part stakeholders (e.g. home owners). However, these well-planned and optimized ultra-dense network deployments are difficult to realize in practice due to various constraints, such as limited or no access to preferred optimum small cell site locations in a given service area. The hybrid ultra-dense network topologies provide an interesting trade-off, whereby, an ultra-dense network may constitute a combination of operator optimized small cell deployments that are complemented by random small cell deployments by third-parties. In this study, an ultra-dense network multiobjective optimization framework and post-deployment power optimization approach are developed for realization and performance comparison of random, optimized and hybrid ultra-dense network topologies in a realistic urban case study area. The results of the case study demonstrate how simple transmit power optimization enable hybrid ultra-dense network topologies to achieve performance almost comparable to optimized topologies whilst also providing the convenience benefits of random small cell deployments

Hosting Capacity Distribution System Yogyakarta with Ant Lion Optimization: A Case Multiobjective

Penetration of hosting capacity in radial type power distribution systems aims to increase the voltage profile on the end customer side. The more electricity customers increase, the more electricity user load growth will increase. This results in a voltage drop on the end customer side in the radial distribution system, characterized by a voltage exceeding the minimum standard operating acceptable voltage. This paper aims to provide solutions to cases in radial grid type power systems in solve voltage drops. The addition of hosting capacity for distributed renewable energy generators is one of the goals to increase the capacity of the electricity system. This research uses the Ant Lion Optimization algorithm method to try to find the optimal location and capacity of Distributed Generation in the electric power distribution system. Penetration hosting capacity injects renewable energy generation in the form of solar cells. The simulation results show that the increased voltage profile in the electric power distribution system exceeds the minimum voltage drop standard. So that in reducing power losses in the radial type network

An Approach to the Optimization of Convergent Networks on IP/MPLS with an Optical GMPLS Backbone in Multicast

This paper shows the solution of a multiobjective scheme for multicast transmissions in MPLS networks with a GMLS optical backbone using evolutive algorithms. It has not been showed models that optimize one or more parameters integrating these two types of networks. Because the proposed scheme is a NP-Hard problem, an algorithm has been developed to solve the problem on polynomial time. The main contributions of this paper are the proposed mathematical model and the algorithm to solve it

Bi-criteria network optimization: problems and algorithms

Several approaches, exact and heuristics, have been designed in order to generate the Pareto frontier for multi-objective combinatorial optimization problems. Although several classes of standard optimization models have been studied in their multi- objective version, there still exists a big gap between the solution techniques and the complexity of the mathematical models that derive from the most recent real world applications. In this thesis such aspect is highlighted with reference to a specific application field, the telecommunication sector, where several emerging optimization problems are characterized by a multi-objective nature. The study of some of these problems, analyzed and solved in the thesis, has been the starting point for an assessment of the state of the art in multicriteria optimization with particular focus on multi-objective integer linear programming. A general two-phase approach for bi-criteria integer network flow problems has been proposed and applied to the bi-objective integer minimum cost flow and the bi-objective minimum spanning tree problem. For both of them the two-phase approach has been designed and tested to generate a complete set of efficient solutions. This procedure, with appropriate changes according to the specific problem, could be applied on other bi-objective integer network flow problems. In this perspective, this work can be seen as a first attempt in the direction of closing the gap between the complex models associated with the most recent real world applications and the methodologies to deal with multi-objective programming. The thesis is structured in the following way: Chapter 1 reports some preliminary concepts on graph and networks and a short overview of the main network flow problems; in Chapter 2 some emerging optimization problems are described, mathematically formalized and solved, underling their multi-objective nature. Chapter 3 presents the state of the art on multicriteria optimization. Chapter 4 describes the general idea of the solution algorithm proposed in this work for bi-objective integer network flow problems. Chapter 5 is focused on the bi-objective integer minimum cost flow problem and on the adaptation of the procedure proposed in Chapter 4 on such a problem. Analogously, Chapter 6 describes the application of the same approach on the bi-objective minimum spanning tree problem. Summing up, the general scheme appears to adapt very well to both problems and can be easily implemented. For the bi-objective integer minimum cost flow problem, the numerical tests performed on a selection of test instances, taken from the literature, permit to verify that the algorithm finds a complete set of efficient solutions. For the bi-objective minimum spanning tree problem, we solved a numerical example using two alternative methods for the first phase, confirming the practicability of the approach

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM