In this contribution, an interpolation problem using radial basis functions is considered. A recently proposed approach for the search of the optimal value of the shape parameter is studied. The approach consists of u...
详细信息
ISBN:
(纸本)9783030386030;9783030386023
In this contribution, an interpolation problem using radial basis functions is considered. A recently proposed approach for the search of the optimal value of the shape parameter is studied. The approach consists of using global optimization algorithms to minimize the error function obtained using a leave-one-out cross validation (LOOCV) technique, which is commonly used for solving machine learning problems. In this paper, the proposed approach is studied experimentally on classes of randomly generated test problems using the GKLS-generator, which is widely used for testing global optimization algorithms. The experimental study on classes of randomly generated test problems is very important from the practical point of view, since results show the behavior of the algorithms for solving not a single test problem, but the whole class with controllable difficulty, which is the main property of the GKLS-generator. The obtained results are relevant, since the experiments have been carried out on 200 randomized test problems, and show that the algorithms are efficient for solving difficult real-life problems demonstrating a promising behavior.
The paper addresses the limitations of the Moth-Flame optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation t...
详细信息
The paper addresses the limitations of the Moth-Flame optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at .
A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch...
详细信息
A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from na & iuml;ve space-filling approaches to learning-based black-box optimizationalgorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result.
Many problems in science and technology require finding global minima or maxima of complicated objective functions. The importance of globaloptimization has inspired the development of numerous heuristic algorithms b...
详细信息
Many problems in science and technology require finding global minima or maxima of complicated objective functions. The importance of globaloptimization has inspired the development of numerous heuristic algorithms based on analogies with physical, chemical or biological systems. Here we present a novel algorithm, SmartRunner, which employs a Bayesian probabilistic model informed by the history of accepted and rejected moves to make an informed decision about the next random trial. Thus, SmartRunner intelligently adapts its search strategy to a given objective function and moveset, with the goal of maximizing fitness gain (or energy loss) per function evaluation. Our approach is equivalent to adding a simple adaptive penalty to the original objective function, with SmartRunner performing hill ascent on the modified landscape. The adaptive penalty can be added to many other globaloptimization schemes, enhancing their ability to find high-quality solutions. We have explored SmartRunner's performance on a standard set of test functions, the Sherrington-Kirkpatrick spin glass model, and Kauffman's NK fitness model, finding that it compares favorably with several widely-used alternative approaches to gradient-free optimization.
In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a l...
详细信息
In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a large amount of computations in, is proposed. The proposed approach is based on the reduction of the multicriterial problems to the globaloptimization ones using the minimax convolution of the partial criteria, the dimensionality reduction with the use of the Peano space-filling curves, and the application of the efficient parallel information-statistical globaloptimization methods. The intensive use of the search information obtained in the course of computations is provided when conducting the computations. The results of the computational experiments demonstrated such an approach to allow reducing the computation costs of solving the multicriterial optimization problems essentially tens and hundreds times. (C) 2017 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the International Conference on Computational Science
In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a l...
详细信息
In the present paper, an efficient parallel method for solving complex multicriterial optimization problems, which the optimality criteria can be multiextremal, and the computing of the criteria values can require a large amount of computations in, is proposed. The proposed approach is based on the reduction of the multicriterial problems to the globaloptimization ones using the minimax convolution of the partial criteria, the dimensionality reduction with the use of the Peano space-filling curves, and the application of the efficient parallel information-statistical globaloptimization methods. The intensive use of the search information obtained in the course of computations is provided when conducting the computations. The results of the computational experiments demonstrated such an approach to allow reducing the computation costs of solving the multicriterial optimization problems essentially – tens and hundreds times.
While the deployment of femtocell in residential buildings has firmly positioned it as a major performance leap in wireless communications, its deployment in commercial buildings remains under-explored. In commercial ...
详细信息
While the deployment of femtocell in residential buildings has firmly positioned it as a major performance leap in wireless communications, its deployment in commercial buildings remains under-explored. In commercial building environments, the femtocell base station (FBS) placement planning is particularly challenging due to the impact of the building size, layout, structure, and wall/floor separation. In this paper, we study the problem of jointly optimizing FBS placement and power control in commercial building environments to prolong the battery life for mobile handsets. We first propose a mathematical model that captures the unique building features. Based on this model, we employ a set of novel transformation strategies to formulate the FBS placement problem as a mixed-integer convex program (MICP). Accordingly, we propose an effective globaloptimization algorithm based on using the convex relaxation of the formulated MICP within a branch-and-bound framework. This approach guarantees finding a global optimal solution. To demonstrate the efficacy of our algorithm, we conduct extensive numerical studies. Our proposed model and optimization approach offer useful insights into femtocell deployments in commercial buildings.
Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Plug-in Hybrid Electric Vehicles (PHEV) and Extend Range Electric Vehicles (EREV) draw mechanical power or regenerate electric power using multiple electric moto...
详细信息
ISBN:
(纸本)9780791844489
Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Plug-in Hybrid Electric Vehicles (PHEV) and Extend Range Electric Vehicles (EREV) draw mechanical power or regenerate electric power using multiple electric motors and generators (M/Gs). Conventionally, heuristics and experience based control rules are used to guide the determination of powertrain component operation parameters to obtain good efficiency. To achieve optimal vehicles electrical/mechanical energy conversion efficiency and to prolong the pure electric range of these vehicles, the energy conversion efficiency is to be maximized against powertrain component operation parameters using high fidelity model and simulation. However, the energy conversion efficiency model using vehicle powertrain component model and simulation is complex, multimodal, and computationally intensive. An efficient globaloptimization tool is needed to produce the optimal efficiency look-up surface for real-time control system implementation, or to search for the optimal operation parameters in real time. In this work, the electrical/mechanical energy conversion efficiency of EV and PHEV/EREV in EV mode is modeled using MATLAB Simulink based powertrain component models. In particular, a new 2 mode-plus EREV design is used as a design example. The optimal vehicle electrical/mechanical energy conversion efficiency under various powertrain component operation parameters are obtained using three alternative globaloptimization tools, Genetic algorithm (GA), Particle Swarm optimization (PSO) and Space Exploration and Unimodal Region Elimination (SEUMRE). The conventional GA and PSO tools, with less efficient search efficiency and requiring long search time, are used for benchmark comparisons. The new SEUMRE globaloptimization tool is used obtain equally accurate results much efficiently. A rough look-up surface is created to demonstrate the difference in computational efficiency. Application of the SEUMRE globaloptimization tool a
When optimizing operations of large wireless ad hoc networks, neither global nor local information-based approaches fit well: they require either information about the entire network structure. which is in most cases ...
详细信息
ISBN:
(纸本)0780383443
When optimizing operations of large wireless ad hoc networks, neither global nor local information-based approaches fit well: they require either information about the entire network structure. which is in most cases not possible to get, or are not capable of optimizing beyond a very narrow horizon. We propose a novel optimization scheme based on regional information, to compute network-wide optimizations taking the peculiarities of large ad hoc networks into account, obtaining an "emergent algorithm" out of a globaloptimization algorithm. Our solution uses a clustering algorithm to define regions but needs neither cluster maintenance nor inter-cluster communication protocols, thus is expected to be very robust. The problem of distributed frequency assignment is used as a case study to demonstrate the performance of our method, compared to algorithms based on local- or global information.
暂无评论