3,298 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
Robust fuzzy PSS design using ABC
This paper presents an Artificial Bee Colony (ABC) algorithm to tune optimal rule-base of a Fuzzy Power System Stabilizer (FPSS) which leads to damp low frequency oscillation following disturbances in power systems. Thus, extraction of an appropriate set of rules or selection of an optimal set of rules from the set of possible rules is an important and essential step toward the design of any successful fuzzy logic controller. Consequently, in this paper, an ABC based rule generation method is proposed for automated fuzzy PSS design to improve power system stability and reduce the design effort. The effectiveness of the proposed method is demonstrated on a 3-machine 9-bus standard power system in comparison with the Genetic Algorithm based tuned FPSS under different loading condition through ITAE performance indices
Where Should We Place LiDARs on the Autonomous Vehicle? - An Optimal Design Approach
Autonomous vehicle manufacturers recognize that LiDAR provides accurate 3D
views and precise distance measures under highly uncertain driving conditions.
Its practical implementation, however, remains costly. This paper investigates
the optimal LiDAR configuration problem to achieve utility maximization. We use
the perception area and non-detectable subspace to construct the design
procedure as solving a min-max optimization problem and propose a bio-inspired
measure -- volume to surface area ratio (VSR) -- as an easy-to-evaluate cost
function representing the notion of the size of the non-detectable subspaces of
a given configuration. We then adopt a cuboid-based approach to show that the
proposed VSR-based measure is a well-suited proxy for object detection rate. It
is found that the Artificial Bee Colony evolutionary algorithm yields a
tractable cost function computation. Our experiments highlight the
effectiveness of our proposed VSR measure in terms of cost-effectiveness
configuration as well as providing insightful analyses that can improve the
design of AV systems.Comment: 7 pages including the references, accepted by International
Conference on Robotics and Automation (ICRA), 201
A Multi-Service Composition Model for Tasks in Cloud Manufacturing Based on VS-ABC Algorithm
This study analyzes the impact of Industry 4.0 and SARS-CoV-2 on the manufacturing industry, in which manufacturing entities are faced with insufficient resources and uncertain services; however, the current study does not fit this situation well. A multi-service composition for complex manufacturing tasks in a cloud manufacturing environment is proposed to improve the utilization of manufacturing service resources. Combining execution time, cost, energy consumption, service reliability and availability, a quality of service (QoS) model is constructed as the evaluation standard. A hybrid search algorithm (VS–ABC algorithm) based on the vortex search algorithm (VS) and the artificial bee colony algorithm (ABC) is introduced and combines the advantages of the two algorithms in search range and calculation speed. We take the customization production of automobiles as an example, and the case study shows that the VS–ABC algorithm has better applicability compared with traditional vortex search and artificial bee colony algorithms
MITIGATE THE REAL POWER LOSSES IN RADIAL DISTRIBUTED NETWORK USING DG BY ABC ALGORITHM
Recently, integration of Distributed generation (DG) in distribution system has increased to high penetration levels. The impact of DG on various aspects of distribution system operation, such as reliability and energy loss depend highly on DG location in distribution feeder .Optimal DG placement plays an important role . This project presents a new methodology using Artificial Bee Colony algorithm (ABC) to find the optimal size and optimum location for the placement of DG in the radial distribution networks for active power compensation by reduction in real power losses .The proposed technique is tested on standard IEEE-33 bus test system
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