3,804 research outputs found
Embrapa Trigo: carteira de projetos de pesquisa e desenvolvimento em 2010.
bitstream/CNPT-2010/41153/1/p-do120.pd
Entropy diversity in multi-objective particle swarm optimization
Multi-objective particle swarm optimization (MOPSO) is a search algorithm based on social behavior. Most of the existing multi-objective particle swarm optimization schemes are based on Pareto optimality and aim to obtain a representative non-dominated
Pareto front for a given problem. Several approaches have been proposed to study the
convergence and performance of the algorithm, particularly by accessing the final results. In the present paper, a different approach is proposed, by using Shannon entropy to analyzethe MOPSO dynamics along the algorithm execution. The results indicate that Shannon entropy can be used as an indicator of diversity and convergence for MOPSO problems
Dynamical modelling of a genetic algorithm
This work addresses the signal propagation and the fractional-order dynamics during the evolution of a genetic algorithm (GA). In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three distinct fitness functions are used to study their influence in the GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory
Manipulator trajectory planning using a MOEA
Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity
On the width of the last scattering surface
We discuss the physical effects of some accelerated world models on the width
of the last scattering surface (LSS) of the cosmic microwave background
radiation (CMBR). The models considered in our analysis are X-matter (XCDM) and
a Chaplygin type gas. The redshift of the LSS does not depend on the kind of
dark energy (if XCDM of Chaplygin). Further, for a Chaplygin gas, the width of
the LSS is also only weakly dependent on the kind of scenario (if we have dark
energy plus cold dark matter or the unified picture).Comment: 10 pages, 1 figure, 2 tables, accepted to IJMP
Structure and trajectory optimization for redundant manipulators
This paper proposes a genetic algorithm to generate a robot structure and the required manipulating trajectories. The objective is to minimize the space/time ripple in the trajectory without colliding with any obstacles in the workspace, while optimizing the mechanical structure.N/
Multi-Criteria Optimization Manipulator Trajectory Planning
In the last twenty years genetic algorithms (GAs) were applied in a plethora of fields such as: control,
system identification, robotics, planning and scheduling, image processing, and pattern and speech
recognition (Bäck et al., 1997). In robotics the problems of trajectory planning, collision avoidance
and manipulator structure design considering a single criteria has been solved using several techniques
(Alander, 2003).
Most engineering applications require the optimization of several criteria simultaneously. Often the
problems are complex, include discrete and continuous variables and there is no prior knowledge about
the search space. These kind of problems are very more complex, since they consider multiple design
criteria simultaneously within the optimization procedure. This is known as a multi-criteria (or multiobjective)
optimization, that has been addressed successfully through GAs (Deb, 2001). The overall
aim of multi-criteria evolutionary algorithms is to achieve a set of non-dominated optimal solutions
known as Pareto front. At the end of the optimization procedure, instead of a single optimal (or near
optimal) solution, the decision maker can select a solution from the Pareto front. Some of the key issues
in multi-criteria GAs are: i) the number of objectives, ii) to obtain a Pareto front as wide as possible
and iii) to achieve a Pareto front uniformly spread.
Indeed, multi-objective techniques using GAs have been increasing in relevance as a research area.
In 1989, Goldberg suggested the use of a GA to solve multi-objective problems and since then other
researchers have been developing new methods, such as the multi-objective genetic algorithm (MOGA)
(Fonseca & Fleming, 1995), the non-dominated sorted genetic algorithm (NSGA) (Deb, 2001), and
the niched Pareto genetic algorithm (NPGA) (Horn et al., 1994), among several other variants (Coello,
1998).
In this work the trajectory planning problem considers: i) robots with 2 and 3 degrees of freedom (dof ),
ii) the inclusion of obstacles in the workspace and iii) up to five criteria that are used to qualify the
evolving trajectory, namely the: joint traveling distance, joint velocity, end effector / Cartesian distance,
end effector / Cartesian velocity and energy involved. These criteria are used to minimize the joint and end effector traveled distance, trajectory ripple and energy required by the manipulator to reach at
destination point.
Bearing this ideas in mind, the paper addresses the planning of robot trajectories, meaning the development
of an algorithm to find a continuous motion that takes the manipulator from a given starting
configuration up to a desired end position without colliding with any obstacle in the workspace.
The chapter is organized as follows. Section 2 describes the trajectory planning and several approaches
proposed in the literature. Section 3 formulates the problem, namely the representation adopted to
solve the trajectory planning and the objectives considered in the optimization. Section 4 studies the
algorithm convergence. Section 5 studies a 2R manipulator (i.e., a robot with two rotational joints/links)
when the optimization trajectory considers two and five objectives. Sections 6 and 7 show the results for
the 3R redundant manipulator with five goals and for other complementary experiments are described,
respectively. Finally, section 8 draws the main conclusions
Fractional order dynamics in a Genetic Algorithm
This work addresses the fractional-order dynamics during the evolution of a Genetic Algorithm population (GA) for generating a robot manipulator trajectory. The GA objective is to minimize the trajectory space/time ripple without exceeding the torque requirements. In order to investigate the phenomena involved in the GA population evolution, the mutation is exposed to excitation perturbations and the corresponding fitness variations are evaluated. The input/output signals are studied revealing a fractional-order dynamic evolution, characteristic of a long-term system memory.N/
Fractional dynamics in genetic algorithms
This paper investigate the fractional-order dynamics during the evolution of a Genetic Algorithm (GA). In order to study the phenomena involved in the GA population evolution, themutation is exposed to excitation perturbations during some generations and the corresponding fitness variations are evaluated. Three similar functions are tested to measure its influence in GA dynamics. The input and output signals are studied revealing a fractional-order dynamic evolution.N/
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