5,837 research outputs found
Multi-objective Anti-swing Trajectory Planning of Double-pendulum Tower Crane Operations using Opposition-based Evolutionary Algorithm
Underactuated tower crane lifting requires time-energy optimal trajectories
for the trolley/slew operations and reduction of the unactuated swings
resulting from the trolley/jib motion. In scenarios involving non-negligible
hook mass or long rig-cable, the hook-payload unit exhibits double-pendulum
behaviour, making the problem highly challenging. This article introduces an
offline multi-objective anti-swing trajectory planning module for a
Computer-Aided Lift Planning (CALP) system of autonomous double-pendulum tower
cranes, addressing all the transient state constraints. A set of auxiliary
outputs are selected by methodically analyzing the payload swing dynamics and
are used to prove the differential flatness property of the crane operations.
The flat outputs are parameterized via suitable B\'{e}zier curves to formulate
the multi-objective trajectory optimization problems in the flat output space.
A novel multi-objective evolutionary algorithm called Collective Oppositional
Generalized Differential Evolution 3 (CO-GDE3) is employed as the optimizer. To
obtain faster convergence and better consistency in getting a wide range of
good solutions, a new population initialization strategy is integrated into the
conventional GDE3. The computationally efficient initialization method
incorporates various concepts of computational opposition. Statistical
comparisons based on trolley and slew operations verify the superiority of
convergence and reliability of CO-GDE3 over the standard GDE3. Trolley and slew
operations of a collision-free lifting path computed via the path planner of
the CALP system are selected for a simulation study. The simulated trajectories
demonstrate that the proposed planner can produce time-energy optimal
solutions, keeping all the state variables within their respective limits and
restricting the hook and payload swings.Comment: 14 pages, 14 figures, 6 table
Optimal Microgrid Topology Design and Siting of Distributed Generation Sources Using a Multi-Objective Substrate Layer Coral Reefs Optimization Algorithm
n this work, a problem of optimal placement of renewable generation and topology design for a Microgrid (MG) is tackled. The problem consists of determining the MG nodes where renewable energy generators must be optimally located and also the optimization of the MG topology design, i.e., deciding which nodes should be connected and deciding the lines’ optimal cross-sectional areas (CSA). For this purpose, a multi-objective optimization with two conflicting objectives has been used, utilizing the cost of the lines, C, higher as the lines’ CSA increases, and the MG energy losses, E, lower as the lines’ CSA increases. To characterize generators and loads connected to the nodes, on-site monitored annual energy generation and consumption profiles have been considered. Optimization has been carried out by using a novel multi-objective algorithm, the Multi-objective Substrate Layers Coral Reefs Optimization algorithm (Mo-SL-CRO). The performance of the proposed approach has been tested in a realistic simulation of a MG with 12 nodes, considering photovoltaic generators and micro-wind turbines as renewable energy generators, as well as the consumption loads from different commercial and industrial sites. We show that the proposed Mo-SL-CRO is able to solve the problem providing good solutions, better than other well-known multi-objective optimization techniques, such as NSGA-II or multi-objective Harmony Search algorithm.This research was partially funded by Ministerio de Economía, Industria y Competitividad, project
number TIN2017-85887-C2-1-P and TIN2017-85887-C2-2-P, and by the Comunidad Autónoma de Madrid, project
number S2013ICE-2933_02
Learning Opposites Using Neural Networks
Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input and its opposite . We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
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
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