evolution fitness center
Journal of Artificial Evolution and Applications
The latest articles from Hindawi Publishing Corporation
- Multiobjective Optimization Tool for a Free Structure Analog Circuits Design
Using Genetic Algorithms and Incorporating Parasitics
This paper presents a novel approach for a free structure analog circuit design using genetic algorithms (GAs). A major problem in a free structure circuit is its sensitivity calculations as a polynomial approximation for the design is not available. A further problem is the effect of parasitic elements on the resulting circuit's performance. In a single design stage, circuits that are produced satisfy a specific frequency response specifications using circuit structures that are unrestricted and with component values that are chosen from a set of preferred values including their parasitic effects. The sensitivity to component variations for the resulting designs is performed using a novel technique and is incorporated in the fitness evaluation function. The extra degrees of freedom resulting form unbounded circuit structures create a huge search space. The application chosen is an RLC ladder filters circuit design. - Particle Swarms: The Second Decade
- A Hybrid PSO/ACO Algorithm for Discovering Classification Rules in Data Mining
We have previously proposed a hybrid particle swarm optimisation/ant colony optimisation (PSO/ACO) algorithm for the discovery of classification rules. Unlike a conventional PSO algorithm, this hybrid algorithm can directly cope with nominal attributes, without converting nominal values into binary numbers in a preprocessing phase. PSO/ACO2 also directly deals with both continuous and nominal attribute values, a feature that current PSO and ACO rule induction algorithms lack. We evaluate the new version of the PSO/ACO algorithm (PSO/ACO2) in 27 public-domain, real-world data sets often used to benchmark the performance of classification algorithms. We compare the PSO/ACO2 algorithm to an industry standard algorithm PART and compare a reduced version of our PSO/ACO2 algorithm, coping only with continuous data, to our new classification algorithm for continuous data based on differential evolution. The results show that PSO/ACO2 is very competitive in terms of accuracy to PART and that PSO/ACO2 produces significantly simpler (smaller) rule sets, a desirable result in data mining—where the goal is to discover knowledge that is not only accurate but also comprehensible to the user. The results also show that the reduced PSO version for continuous attributes provides a slight increase in accuracy when compared to the differential evolution variant. - The Generalized PSO: A New Door to PSO
Evolution
A generalized form of the particle swarm optimization (PSO) algorithm is presented. Generalized PSO (GPSO) is derived from a continuous version of PSO adopting a time step different than the unit. Generalized continuous particle swarm optimizations are compared in terms of attenuation and oscillation. The deterministic and stochastic stability regions and their respective asymptotic velocities of convergence are analyzed as a function of the time step and the GPSO parameters. The sampling distribution of the GPSO algorithm helps to study the effect of stochasticity on the stability of trajectories. The stability regions for the second-, third-, and fourth-order moments depend on inertia, local, and global accelerations and the time step and are inside of the deterministic stability region for the same time step. We prove that stability regions are the same under stagnation and with a moving center of attraction. Properties of the second-order moments variance and covariance serve to propose some promising parameter sets. High variance and temporal uncorrelation improve the exploration task while solving ill-posed inverse problems. Finally, a comparison is made between PSO and GPSO by means of numerical experiments using well-known benchmark functions with two types of ill-posedness commonly found in inverse problems: the Rosenbrock and the “elongated” DeJong functions (global minimum located in a very flat area), and the Griewank function (global minimum surrounded by multiple minima). Numerical simulations support the results provided by theoretical analysis. Based on these results, two variants of Generalized PSO algorithm are proposed, improving the convergence and the exploration task while solving real applications of inverse problems. - Forma Analysis of Particle Swarm Optimisation for Permutation Problems
Particle swarm optimisation (PSO) is an innovative and competitive optimisation technique for numerical optimisation with real-parameter representation. In this paper, we examine the working mechanism of PSO in a principled manner with forma analysis and investigate the applicability of PSO on the permutation problem domain. Particularly, our derived PSO schemes are empirically studied based on the quadratic assignment problem (QAP) benchmarks to justify its comparable performance, which in turn implies the benefits of our approach in applying PSO to the discrete problem domain. - Inverse Parameter Identification Technique Using PSO Algorithm Applied to Geotechnical
Modeling
This paper presents a concept for the application of particle swarm optimization in geotechnical engineering. For the calculation of deformations in soil or rock, numerical simulations based on continuum methods are widely used. The material behavior is modeled using constitutive relations that require sets of material parameters to be specified. We present an inverse parameter identification technique, based on statistical analyses and a particle swarm optimization algorithm, to be used in the calibration process of geomechanical models. Its application is demonstrated with typical examples from the fields of soil mechanics and engineering geology. The results for two different laboratory tests and a natural slope clearly show that particle swarms are an efficient and fast tool for finding improved parameter sets to represent the measured reference data. - Optimizing the Operation Sequence of
a Multihead Surface Mounting Machine Using
a Discrete Particle Swarm Optimization Algorithm
The optimization of the nozzle selection, for sequencing component pick and place operations, is very important to the efficiency of multihead surface mounting machine (SMM). The nozzle change operation, that is, choosing the best nozzle head relative pair that is most effective for picking and placing components onto the printed circuit board (PCB), significantly adds to the overall assembly time. In this paper, as a practical application, we focus on a discrete particle swarm optimization (DPSO) algorithm for multihead SMM which is used to minimize the number of nozzle change operations and pick and place operations simultaneously. To evaluate the performance of the proposed algorithm, we test it on assembly tasks of PCBs through simulations. The results of computer experiments show that this DPSO algorithm was superior to the standard PSO algorithm. - Particle Swarm Optimization for Multimodal Functions: A Clustering Approach
The particle swarm optimization (PSO) algorithm is designed to find a single optimal solution and needs some modifications to be able to locate multiple optima on a multimodal function. In parallel with evolutionary computation algorithms, these modifications can be grouped in the framework of niching. In this work, we present a new approach to niching in PSO based on clustering particles to identify niches. The neighborhood structure, on which particles rely for communication, is exploited together with the niche information to locate multiple optima in parallel. Our approach was implemented in the k-means-based PSO (kPSO), which employs the standard k-means clustering algorithm, improved with a mechanism to adaptively identify the number of clusters. kPSO proved to be a competitive solution when compared with other existing algorithms, since it showed better performance on a benchmark set of multimodal functions. - Evolving Neural Networks for Static Single-Position Automated Trading
This paper presents an approach to single-position, intraday automated trading based on a neurogenetic algorithm. An artificial neural network is evolved to provide trading signals to a simple automated trading agent. The neural network uses open, high, low, and close quotes of the selected financial instrument from the previous day, as well as a selection of the most popular technical indicators, to decide whether to take a single long or short position at market open. The position is then closed as soon as a given pr