Applying River Formation Dynamics to Solve NP-Complete Problems

Rabanal, Pablo; Rodrı́guez, Ismael; Rubio, Fernando

doi:10.1007/978-3-642-00267-0_12

Cited by 22 publications

(20 citation statements)

References 15 publications

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“…The different suitability of each solution representation makes each metaheuristic be easily or hardly adaptable to the problem under consideration. For instance, Ant Colony Optimization [8,7] and River Formation Dynamics [16,17,18] naturally fit into problems where problem instances are graphs and solutions are paths in these graphs, whereas managing other kinds of problem representations might be artificial to these methods. On the other hand, in problems where we must find paths in graphs, a GA [10] must impose unnatural restrictions in order to guarantee that solutions are paths indeed (in particular, there must exist an edge between every two consecutive nodes included in a solution).…”

Section: Notions and Methodologymentioning

confidence: 99%

Using polynomial reductions to test the suitability of metaheuristics for solving NP-complete problems

Rabanal

Rodrı́guez

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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We present a method to compare the suitability of evolutionary computation metaheuristics for solving different NPcomplete problems. Instead of checking the performance of each metaheuristic for each problem by using a specific benchmark of that problem, polynomial reductions are used to transform instances of a problem into the other. In this way, we avoid assessing each metaheuristic in terms of incomparable benchmarks. Several pairs of NP-complete problems are compared. For each pair, the impact of the difficulty of the polynomial reduction on the capability of an evolutionary method (in particular, genetic algorithms) to achieve a similar performance for both problems is studied.

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Section: Notions and Methodologymentioning

confidence: 99%

Using polynomial reductions to test the suitability of metaheuristics for solving NP-complete problems

Rabanal

Rodrı́guez

2013

Proceedings of the 28th Annual ACM Symposium on Applied Computing

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“…With the analysis of natural systems several important tools and techniques have been invented by the researchers during last two decades such as genetic algorithms (Holland 1975), particle swarm optimization (Kennedy and Eberhart 1995), ant colony optimization (Dorigo and Gambardella 1997), and river formation dynamics (Rabanal et al 2009). Among them, GA and PSO have emerged as promising algorithms for handling the optimization problems because of their versatility and ability to optimize in complex multimodal search spaces applied to non differentiable cost functions.…”

Section: Nature Inspired Optimization Techniquesmentioning

confidence: 99%

Filterbank optimization for robust ASR using GA and PSO

Aggarwal

Dave

2012

Int J Speech Technol

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Automatic speech recognition (ASR) systems follow a well established approach of pattern recognition, that is signal processing based feature extraction at front-end and likelihood evaluation of feature vectors at back-end. Melfrequency cepstral coefficients (MFCCs) are the features widely used in state-of-the-art ASR systems, which are derived by logarithmic spectral energies of the speech signal using Mel-scale filterbank. In filterbank analysis of MFCC there is no consensus for the spacing and number of filters used in various noise conditions and applications. In this paper, we propose a novel approach to use particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the parameters of MFCC filterbank such as the central and side frequencies. The experimental results show that the new front-end outperforms the conventional MFCC technique. All the investigations are conducted using two separate classifiers, HMM and MLP, for Hindi vowels recognition in typical field condition as well as in noisy environment.

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“…Mathematical model for the RFD has been elaborated in [31], [32], [81]. This algorithm begins by depositing all drops in the initial node.…”

Section: Initialize Drops 2 Initialize Nodes 3 While (Termination Cmentioning

confidence: 99%

Investigating Performance of Various Natural Computing Algorithms

Chawda¹,

Patel²

2017

IJISA

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Abstract-Nature is there since millenniums. Natural elements have withstood harsh complexities since years and have proved their efficiency in tackling them. This aspect has inspired many researchers to design algorithms based on phenomena in the natural world since the last couple of decades. Such algorithms are known as natural computing algorithms or nature inspired algorithms. These algorithms have established their ability to solve a large number of real-world complex problems by providing optimal solutions within the reasonable time duration. This paper presents an investigation by assessing the performance of some of the well-known natural computing algorithms with their variations. These algorithms include Genetic Algorithms, Ant Colony Optimization, River Formation Dynamics, Firefly Algorithm and Cuckoo Search. The Traveling Salesman Problem is used here as a test bed problem for performance evaluation of these algorithms. It is a kind of combinatorial optimization problem and known as one the most famous NP-Hard problems. It is simple and easy to understand, but at the same time, very difficult to find the optimal solution in a reasonable time -particularly with the increase in a number of cities. The source code for the above natural computing algorithms is developed in MATLAB R2015b and applied on several TSP instances given in TSPLIB library. Results obtained are analyzed based on various criteria such as tour length, required iterations, convergence time and quality of solutions. Conclusions derived from this analysis help to establish the superiority of Firefly Algorithms over the other algorithms in comparative terms.

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Applying River Formation Dynamics to Solve NP-Complete Problems

Cited by 22 publications

References 15 publications

Using polynomial reductions to test the suitability of metaheuristics for solving NP-complete problems

Using polynomial reductions to test the suitability of metaheuristics for solving NP-complete problems

Filterbank optimization for robust ASR using GA and PSO

Investigating Performance of Various Natural Computing Algorithms

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