Fundamenta Informaticae - Volume 95, issue 4

Article Type: Other

DOI: 10.3233/FI-2009-156

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. i-ii, 2009

Authors: Chakraborty,, Prithwish | Roy, Gourab Ghosh | Das, Swagatam | Jain, Dhaval | Abraham, Ajith

Article Type: Research Article

Abstract: Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This paper presents an attempt to improve the search performance of HS by hybridizing it with Differential Evolution (DE) algorithm. The …performance of the resulting hybrid algorithm has been compared with classical HS, the global best HS, and a very popular variant of DE over a test-suite of six well known benchmark functions and one interesting practical optimization problem. The comparison is based on the following performance indices - (i) accuracy of final result, (ii) computational speed, and (iii) frequency of hitting the optima. Show more

Keywords: Global optimization, Meta-heuristics, Harmony Search, Differential Evolution, Explorative power, Population variance

DOI: 10.3233/FI-2009-157

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 401-426, 2009

Authors: Cui, Zhihua | Cai, Xingjuan

Article Type: Research Article

Abstract: Integral-controlled particle swarm optimization (ICPSO) is an effective variant of particle swarm optimization (PSO) aiming to increase the population diversity. Due to the additional accelerator items, the behavior of ICPSO is more complex, and provides more chances to escaping from a local optimum than the standard version of PSO. However, many experimental results show the performance of ICPSO is not always well because of the particles' un-controlled movements. Therefore, a new variant, integral particle swarm …optimization with dispersed accelerator information (IPSO-DAI) is designed to improve the computational efficiency. In IPSO-DAI, a predefined predicted velocity index is introduced to guide the moving direction. If the average velocity of one particle is superior to the index value, it will choice a convergent manner, otherwise, a divergent manner is employed. Furthermore, the choice of convergent manner or divergent manner for each particle is associated with its performance to fit different living experiences. Simulation results show the proposed variant is more effective than other three variants of particle swarm optimization especially for multi-modal numerical problems. The IPSO-DAI algorithm is also applied to directing the orbits of discrete chaotic dynamical systems by adding small bounded perturbations, and achieves the best performance among four different variants of PSO. Show more

Keywords: Particle swarm optimization, integral controller, social learning factor, cognitive learning factor, dispersed behavior

DOI: 10.3233/FI-2009-158

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 427-447, 2009

Authors: Kwolek, Bogdan

Article Type: Research Article

Abstract: This paper proposes a particle swarm optimization based algorithm for object tracking in image sequences. The parametric models of variability of the object appearance are employed to shift the particle swarm in order to cover the promising object location. Afterwards the particles are drawn from a Gaussian distribution. Then the particle swarm optimization takes place in order to concentrate the particles near the true object state. A grayscale appearance model that is learned online is …utilized in evaluation of the particles score. Experimental results thatwere obtained in a typical office environment show the feasibility of our approach, especially when the object undergoing tracking has a rapid motion or the appearance changes are considerable. The resulting algorithm runs in real-time on a standard computer. Show more

Keywords: Swarm intelligence, particle swarm optimization, visual object tracking

DOI: 10.3233/FI-2009-159

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 449-463, 2009

Authors: Liu, Hongbo | Abraham, Ajith | Wang, Zuwen

Article Type: Research Article

Abstract: Swarm Intelligence (SI) is an innovative distributed intelligent paradigm whereby the collective behaviors of unsophisticated individuals interacting locally with their environment cause coherent functional global patterns to emerge. In this paper, we model the scheduling problem for the multi-objective Flexible Job-shop Scheduling Problems (FJSP) and attempt to formulate and solve the problem using a Multi Particle Swarm Optimization (MPSO) approach. MPSO consists of multi-swarms of particles, which searches for the operation order …update and machine selection. All the swarms search the optima synergistically and maintain the balance between diversity of particles and search space. We theoretically prove that the multi-swarm synergetic optimization algorithm converges with a probability of 1 towards the global optima. The details of the implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP, especially for large scale problems. Show more

Keywords: SwarmIntelligence, Emergence, Multi-objectiveOptimization, Flexible Job-shop Scheduling Problem, Particle Swarm Optimization, Multi-swarm, Probability, Convergence

DOI: 10.3233/FI-2009-160

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 465-489, 2009

Authors: Liu, Ming-Tsung | Yu, Pao-Ta

Article Type: Research Article

Abstract: This paper presents a fast two-stage corner detector with noise tolerance. In the first stage, a novel candidate pruning approach based on PSO-SVM is proposed to select candidatecorner pixels which have great potential to be corners. In the second stage the Harris corner detector is employed to recognize real corners among the candidate-corner pixels. The parameters and feature selection of SVM classifier is optimized by using particle swarm optimization (PSO). The method takes advantage of the …minimum structure risk of SVM and the quickly globally optimizing ability of PSO. Generally speaking, corners are considered as the junction of edges. Thus, edge pixels with a high gradient in more than one direction should be selected as candidate corners. Meanwhile, impulse noise often corrupts digital images while images are transmitted over an unreliable channel or are captured using a camera with faulty sensors. Noise-corrupted pixels usually cause serious false detection problems in most corner detectors. The proposed PSO-SVM candidate pruning approach detects noisy pixels and excludes them from being candidate corners to enhance the noise tolerance of the corner detector. Through the well-selection of candidate corners, the proposed candidate pruning approach can 1) enhance the noise tolerance capability, and 2) reduce the computational effort of the corner detectors. Show more

Keywords: Particle swarm optimization, corner detection, Harris corner detector, support vector machine

DOI: 10.3233/FI-2009-161

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 491-510, 2009

Authors: Pant, Millie | Thangaraj, Radha | Abraha, Ajith

Article Type: Research Article

Abstract: Population based metaheuristics are commonly used for global optimization problems. These techniques depend largely on the generation of initial population. A good initial population may not only result in a better fitness function value but may also help in faster convergence. Although these techniques have been popular since more than three decades very little research has been done on the initialization of the population. In this paper, we propose a modified Particle Swarm Optimization (PSO) called …Improved Constraint Particle Swarm Optimization (ICPSO) algorithm for solving constrained optimization. The proposed ICPSO algorithm is initialized using quasi random Vander Corput sequence and differs from unconstrained PSO algorithm in the phase of updating the position vectors and sorting every generation solutions. The performance of ICPSO algorithm is validated on eighteen constrained benchmark problems. The numerical results show that the proposed algorithm is a quite promising for solving constraint optimization problems. Show more

Keywords: Particle Swarm Optimization, Constrained Optimization Problems, Quasi Random, Vander Corput Sequence

DOI: 10.3233/FI-2009-162

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 511-531, 2009

Authors: Ramanna, Sheela | Meghdadi, Amir H.

Article Type: Research Article

Abstract: The problem considered in this article is how to detect and measure resemblances between swarm behaviours. The solution to this problem stems from an extension of recent work on tolerance near sets and image correspondence. Instead of considering feature extraction from subimages in digital images, we compare swarm behaviours by considering feature extraction from subsets of tuples of feature-values representing the behaviour of observed swarms of organisms. Thanks to recent work on the foundations of near …sets, it is possible to formulate a rigorous approach to measuring the extent that swarm behaviours resemble each other. Fundamental to this approach is what is known as a recent description-based set intersection, a set containing objects with matching or almost the same descriptions extracted from objects contained in pairs of disjoint sets. Implicit in this work is a new approach to comparing information tables representing N. Tinbergen's ethology (study of animal behaviour) and direct result of recent work on what is known as rough ethology. Included in this article is a comparison of recent nearness measures that includes a new form of F. Hausdorff's distance measure. The contribution of this article is a tolerance near set approach to measuring the degree of resemblance between swarm behaviours. Show more

Keywords: Ethology, near sets, nearness measure, rough ethology, swarm behaviour, tolerance space

DOI: 10.3233/FI-2009-163

Citation: Fundamenta Informaticae, vol. 95, no. 4, pp. 533-552, 2009