Search results for "Multi-swarm optimization"
showing 10 items of 14 documents
Applying particle swarm optimization to the motion-cueing-algorithm tuning problem
2017
The MCA tuning problem consists in finding the best values for the parameters/coefficients of Motion Cueing Algorithms (MCA). MCA are used to control the movements of robotic motion platforms employed to generate inertial cues in vehicle simulators. This problem is traditionally approached with a manual pilot-in-the-loop subjective tuning, based on the opinion of several pilots/drivers. Instead, this paper proposes applying Particle Swarm Optimization (PSO) to solve this problem, using simulated motion platforms and objective indicators rather than subjective opinions. Results show that PSO-based tuning can provide a suitable solution for this complex optimization problem.
An Adaptive Metamodel-Based Optimization Approach for Vehicle Suspension System Design
2014
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/965157 The performance index of a suspension system is a function of the maximum and minimum values over the parameter interval. Thus metamodel-based techniques can be used for designing suspension system hardpoints locations. In this study, an adaptive metamodel-based optimization approach is used to find the proper locations of the hardpoints, with the objectives considering the kinematic performance of the suspension. The adaptive optimization method helps to find the optimum locations of the hardpoints efficiently as it may be unachie…
An evolutionary method for complex-process optimization
2010
10 páginas, 7 figuras, 7 tablas
Heuristic-Based Shiftable Loads Optimal Management in Smart Micro-Grids
2015
In this paper, an optimal power dispatch problem on a 24-h basis for distribution systems with distributed energy resources (DER) also including directly controlled shiftable loads is presented. In the literature, the optimal energy management problems in smart grids (SGs) where such types of loads exist are formulated using integer or mixed integer variables. In this paper, a new formulation of shiftable loads is employed. Such formulation allows reduction in the number of optimization variables and the adoption of real valued optimization methods such as the one proposed in this paper. The method applied is a novel nature-inspired multiobjective optimization algorithm based on an original…
Constraint handling in efficient global optimization
2017
Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes.…
Non-linear optimization of track layouts in loop-sorting-systems
2013
Optimization used for enhancing geometric structures iswell known. Applying obstacles to the shape optimization problemis on the other hand not very common. It requires a fast contact search algorithmand an exact continuous formulation to solve the problem robustly. This paper focuses on combining shape optimization problemswith collision avoidance constraints by which a collision detection algorithmis presented. The presentedmethod is tested against the commercial loop-sorting-system used for sorting of medium sized items. The objective is to minimize price and footprint of the system whilemaintaining its functionality. Contact constraints are in this context important to include as variou…
Pareto-optimal Glowworm Swarms Optimization for Smart Grids Management
2013
This paper presents a novel nature-inspired multi-objective optimization algorithm. The method extends the glowworm swarm particles optimization algorithm with algorithmical enhancements which allow to identify optimal pareto front in the objectives space. In addition, the system allows to specify constraining functions which are needed in practical applications. The framework has been applied to the power dispatch problem of distribution systems including Distributed Energy Resources (DER). Results for the test cases are reported and discussed elucidating both numerical and complexity analysis.
Simultaneous and multi-criteria optimization of TS requirements and maintenance at NPPs
2002
Abstract One of the main concerns of the nuclear industry is to improve the availability of safety-related systems at nuclear power plants (NPPs) to achieve high safety levels. The development of efficient testing and maintenance has been traditionally one of the different ways to guarantee high levels of systems availability, which are implemented at NPP through technical specification and maintenance requirements (TS&M). On the other hand, there is a widely recognized interest in using the probabilistic risk analysis (PRA) for risk-informed applications aimed to emphasize both effective risk control and effective resource expenditures at NPPs. TS&M-related parameters in a plant are associ…
A novel abstraction for swarm intelligence: particle field optimization
2016
Particle swarm optimization (PSO) is a popular meta-heuristic for black-box optimization. In essence, within this paradigm, the system is fully defined by a swarm of "particles" each characterized by a set of features such as its position, velocity and acceleration. The consequent optimized global best solution is obtained by comparing the personal best solutions of the entire swarm. Many variations and extensions of PSO have been developed since its creation in 1995, and the algorithm remains a popular topic of research. In this work we submit a new, abstracted perspective of the PSO system, where we attempt to move away from the swarm of individual particles, but rather characterize each …
Wireless sensor network coverage problem using modified fireworks algorithm
2016
Wireless sensor networks are emerging technology with increasing number of applications, and consequently an active research area. One of the problems pertinent to wireless sensor networks is the coverage problem with number of definitions, depending on the assumed conditions. In this paper we consider hard optimization area coverage problem with the goal of finding optimal sensor nodes positions that maximize probabilistic coverage of the area of interest. For such type of optimization problem swarm intelligence stochastic metaheuristics have been successfully used. In this paper we propose a modified enhanced fireworks algorithm for wireless sensor network coverage problem and compare it …