Biologically Inspired Techniques for Data Mining

Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques.

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From Optimization to Clustering

Handbook of Research on Artificial Intelligence Techniques and Algorithms - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-4666-7258-1.ch019 ◽

2015 ◽

pp. 594-619 ◽

Cited By ~ 5

Author(s):

Megha Vora ◽

T. T. Mirnalinee

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Optimization Algorithm ◽

Efficient Solution ◽

Optimization Problems ◽

Particle Swarm ◽

Optimization Techniques ◽

Ant Colony Optimization Algorithm ◽

Swarm Optimization ◽

Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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From Optimization to Clustering

Nature-Inspired Computing ◽

10.4018/978-1-5225-0788-8.ch058 ◽

2016 ◽

pp. 1519-1544 ◽

Cited By ~ 1

Author(s):

Megha Vora ◽

T. T. Mirnalinee

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Optimization Algorithm ◽

Efficient Solution ◽

Optimization Problems ◽

Particle Swarm ◽

Optimization Techniques ◽

Ant Colony Optimization Algorithm ◽

Swarm Optimization ◽

Insight Into

In the past two decades, Swarm Intelligence (SI)-based optimization techniques have drawn the attention of many researchers for finding an efficient solution to optimization problems. Swarm intelligence techniques are characterized by their decentralized way of working that mimics the behavior of colony of ants, swarm of bees, flock of birds, or school of fishes. Algorithmic simplicity and effectiveness of swarm intelligence techniques have made it a powerful tool for solving global optimization problems. Simulation studies of the graceful, but unpredictable, choreography of bird flocks led to the design of the particle swarm optimization algorithm. Studies of the foraging behavior of ants resulted in the development of ant colony optimization algorithm. This chapter provides insight into swarm intelligence techniques, specifically particle swarm optimization and its variants. The objective of this chapter is twofold: First, it describes how swarm intelligence techniques are employed to solve various optimization problems. Second, it describes how swarm intelligence techniques are efficiently applied for clustering, by imposing clustering as an optimization problem.

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Swarm intelligence versus direct cover algorithms in synthesis of Multi-Valued Logic functions

Applied Computing and Informatics ◽

10.1016/j.aci.2020.03.002 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Mostafa Abd-El-Barr ◽

Kalim Qureshi ◽

Bambang Sarif

Keyword(s):

Particle Swarm Optimization ◽

Information Processing ◽

Ant Colony Optimization ◽

Swarm Intelligence ◽

Optimization Techniques ◽

Ant Colony ◽

Swarm Optimization ◽

Multiple Valued Logic ◽

Simulation Results ◽

Logic Functions

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

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Two Diverse Swarm Intelligence Techniques for Supervised Learning

Psychology and Mental Health ◽

10.4018/978-1-5225-0159-6.ch034 ◽

2016 ◽

pp. 849-861 ◽

Cited By ~ 3

Author(s):

Tad Gonsalves

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Supervised Learning ◽

Prediction Accuracy ◽

Particle Swarm ◽

Optimization Techniques ◽

Neural Net ◽

Swarm Optimization ◽

Fireworks Algorithm ◽

The Neural Networks

Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

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Two Diverse Swarm Intelligence Techniques for Supervised Learning

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2015100103 ◽

2015 ◽

Vol 6 (4) ◽

pp. 55-66

Author(s):

Tad Gonsalves

Keyword(s):

Particle Swarm Optimization ◽

Swarm Intelligence ◽

Supervised Learning ◽

Prediction Accuracy ◽

Particle Swarm ◽

Optimization Techniques ◽

Neural Net ◽

Swarm Optimization ◽

Fireworks Algorithm ◽

The Neural Networks

Particle Swarm Optimization (PSO) and Enhanced Fireworks Algorithm (EFWA) are two diverse optimization techniques of the Swarm Intelligence paradigm. The inspiration of the former comes from animate swarms like those of birds and fish efficiently hunting for prey, while that of the latter comes from inanimate swarms like those of fireworks illuminating the night sky. This novel study, aimed at extending the application of these two Swarm Intelligence techniques to supervised learning, compares and contrasts their performance in training a neural network to perform the task of classification on datasets. Both the techniques are found to be speedy and successful in training the neural networks. Further, their prediction accuracy is also found to be high. Except in the case of two datasets, the training and prediction accuracies of the Enhanced Fireworks Algorithm driven neural net are found to be superior to those of the Particle Swarm Optimization driven neural net.

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Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm

Mathematics ◽

10.3390/math9151795 ◽

2021 ◽

Vol 9 (15) ◽

pp. 1795

Author(s):

Manuel Cedillo-Hernandez ◽

Antonio Cedillo-Hernandez ◽

Francisco J. Garcia-Ugalde

Keyword(s):

Fourier Transform ◽

Particle Swarm Optimization ◽

Discrete Fourier Transform ◽

Image Watermarking ◽

Particle Swarm ◽

Optimization Techniques ◽

Robust Watermarking ◽

Swarm Optimization ◽

General Terms ◽

Operation Parameters

Robust digital image watermarking is an information security technique that has been widely used to solve several issues related mainly with copyright protection as well as ownership authentication. In general terms, robust watermarking conceals a small signal called a “watermark” in a host image in a form imperceptible to human vision. The efficiency of conventional robust watermarking based on frequency domain depend directly on the results of performance in terms of robustness and imperceptibility. According to the application scenario and the image dataset, it is common practice to adjust the key parameters used by robust watermarking methods in an experimental form; however, this manual adjustment may involve exhaustive tasks and at the same time be a drawback in practical scenarios. In recent years, several optimization techniques have been adopted by robust watermarking to allowing adjusting in an automatic form its key operation parameters, improving thus its performance. In this context, this paper proposes an improved robust watermarking algorithm in discrete Fourier transform via spread spectrum, optimizing the key operation parameters, particularly the amounts of bands and coefficients of frequency as well as the watermark strength factor using particle swarm optimization in conjunction with visual information fidelity and bit correct rate criteria. Experimental results obtained in this research show improved robustness against common signal processing and geometric distortions, preserving a high visual quality in color images. Performance comparison with conventional discrete Fourier transform proposal is provided, as well as with the current state-of-the-art of particle swarm optimization applied to image watermarking.

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Asteroid Rendezvous Mission Design Using Multiobjective Particle Swarm Optimization

Mathematical Problems in Engineering ◽

10.1155/2014/823659 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Ya-zhong Luo ◽

Li-ni Zhou

Keyword(s):

Particle Swarm Optimization ◽

Particle Swarm ◽

Optimization Techniques ◽

Mission Design ◽

Trajectory Design ◽

Time Transfer ◽

Pareto Solution ◽

Swarm Optimization ◽

Multiobjective Particle Swarm Optimization ◽

Asteroid Mission

A new preliminary trajectory design method for asteroid rendezvous mission using multiobjective optimization techniques is proposed. This method can overcome the disadvantages of the widely employed Pork-Chop method. The multiobjective integrated launch window and multi-impulse transfer trajectory design model is formulated, which employes minimum-fuel cost and minimum-time transfer as two objective functions. The multiobjective particle swarm optimization (MOPSO) is employed to locate the Pareto solution. The optimization results of two different asteroid mission designs show that the proposed approach can effectively and efficiently demonstrate the relations among the mission characteristic parameters such as launch time, transfer time, propellant cost, and number of maneuvers, which will provide very useful reference for practical asteroid mission design. Compared with the PCP method, the proposed approach is demonstrated to be able to provide much more easily used results, obtain better propellant-optimal solutions, and have much better efficiency. The MOPSO shows a very competitive performance with respect to the NSGA-II and the SPEA-II; besides a proposed boundary constraint optimization strategy is testified to be able to improve its performance.

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