Clustering with nature-inspired metaheuristics

2020 ◽  
pp. 165-178
Author(s):  
Szymon Łukasik ◽  
Piotr A. Kowalski
Keyword(s):  
Nature Inspired Metaheuristics

Nature Inspired Metaheuristics and Their Applications in Agriculture: A Short Review

2019 ◽  
pp. 167-179 ◽  
Author(s):  
Jorge Miguel Mendes ◽  
Paulo Moura Oliveira ◽  
Filipe Neves dos Santos ◽  
Raul Morais dos Santos
Keyword(s):  
Short Review ◽  
Nature Inspired Metaheuristics

Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding

2014 ◽  
Vol 5 (4) ◽  
pp. 47-69 ◽  
Author(s):  
Salima Ouadfel ◽  
Souham Meshoul
Keyword(s):  
Cuckoo Search ◽  
Exhaustive Search ◽  
The Other ◽  
Multilevel Thresholding ◽  
Bee Colony ◽  
Individual Solution ◽  
Optimal Values ◽  
Comparison Algorithms ◽  
Nature Inspired Metaheuristics ◽  
Better Than

Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding (AMT) problem. The goal is to determine the correct number of thresholds as well as their optimal values. For this purpose, the article adopts—for each metaheuristic—a new hybrid coding scheme such that each individual solution is represented by two parts: a real part which represents the thresholds values and a binary part which indicates if a given threshold will be used or not during the thresholding process. Experiments have been conducted on six real test images and the results have been compared with two automatic multilevel thresholding based PSO methods and the exhaustive search method for fair comparison. Empirical results reveal that AMT-HABC and AMT-HCS algorithms performed equally to the solution provided by the exhaustive search and are better than the other comparison algorithms. In addition, the results indicate that the ATM-HABC algorithm has a higher success rate and a speed convergence than the other metaheuristics.

Multilevel Thresholding for Image Segmentation Based on Cellular Metaheuristics

2018 ◽  
Vol 9 (4) ◽  
pp. 1-32 ◽  
Author(s):  
Mohamed Abdou Bouteldja ◽  
Mohamed Baadeche ◽  
Mohamed Batouche
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Multilevel Thresholding ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Cellular Genetic Algorithm ◽  
Kapur’S Entropy ◽  
Computationally Expensive ◽  
Segmentation Problem ◽  
Nature Inspired Metaheuristics

This article describes how multilevel thresholding image segmentation is a process used to partition an image into well separated regions. It has various applications such as object recognition, edge detection, and particle counting, etc. However, it is computationally expensive and time consuming. To alleviate these limitations, nature inspired metaheuristics are widely used to reduce the computational complexity of such problem. In this article, three cellular metaheuristics namely cellular genetic algorithm (CGA), cellular particle swarm optimization (CPSO) and cellular differential evolution (CDE) are adapted to solve the multilevel thresholding image segmentation problem. Experiments are conducted on different test images to assess the performance of the cellular algorithms in terms of efficiency, quality and stability based on the between-class variance and Kapur's entropy as objective functions. The experimental results have shown that the proposed cellular algorithms compete with and even outperform existing methods for multilevel thresholding image segmentation.

scholarly journals Mitigating Metaphors: A Comprehensible Guide to Recent Nature-Inspired Algorithms

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Michael A. Lones
Keyword(s):  
Evolutionary Algorithms ◽  
Algorithm Design ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Metaheuristic Algorithms ◽  
Future Directions ◽  
Nature Inspired Algorithms ◽  
Different Sources ◽  
Nature Inspired Metaheuristics

AbstractIn recent years, a plethora of new metaheuristic algorithms have explored different sources of inspiration within the biological and natural worlds. This nature-inspired approach to algorithm design has been widely criticised. A notable issue is the tendency for authors to use terminology that is derived from the domain of inspiration, rather than the broader domains of metaheuristics and optimisation. This makes it difficult to both comprehend how these algorithms work and understand their relationships to other metaheuristics. This paper attempts to address this issue, at least to some extent, by providing accessible descriptions of the most cited nature-inspired algorithms published in the last 20 years. It also discusses commonalities between these algorithms and more classical nature-inspired metaheuristics such as evolutionary algorithms and particle swarm optimisation, and finishes with a discussion of future directions for the field.

scholarly journals Efficient Parallel Sorting for Migrating Birds Optimization When Solving Machine-Part Cell Formation Problems

2016 ◽  
Vol 2016 ◽  
pp. 1-39 ◽  
Author(s):  
Ricardo Soto ◽  
Broderick Crawford ◽  
Boris Almonacid ◽  
Fernando Paredes
Keyword(s):  
Machine Part ◽  
Optimization Problem ◽  
Cell Formation ◽  
Global Optimum ◽  
Improve Performance ◽  
Parallel Version ◽  
Migrating Birds Optimization ◽  
Flight Formation ◽  
Migrating Birds ◽  
Nature Inspired Metaheuristics

The Machine-Part Cell Formation Problem (MPCFP) is a NP-Hard optimization problem that consists in grouping machines and parts in a set of cells, so that each cell can operate independently and the intercell movements are minimized. This problem has largely been tackled in the literature by using different techniques ranging from classic methods such as linear programming to more modern nature-inspired metaheuristics. In this paper, we present an efficient parallel version of the Migrating Birds Optimization metaheuristic for solving the MPCFP. Migrating Birds Optimization is a population metaheuristic based on the V-Flight formation of the migrating birds, which is proven to be an effective formation in energy saving. This approach is enhanced by the smart incorporation of parallel procedures that notably improve performance of the several sorting processes performed by the metaheuristic. We perform computational experiments on 1080 benchmarks resulting from the combination of 90 well-known MPCFP instances with 12 sorting configurations with and without threads. We illustrate promising results where the proposal is able to reach the global optimum in all instances, while the solving time with respect to a nonparallel approach is notably reduced.

Swarm-Based Nature-Inspired Metaheuristics for Neural Network Optimization

2018 ◽  
pp. 23-53
Author(s):  
Swathi Jamjala Narayanan ◽  
Boominathan Perumal ◽  
Jayant G. Rohra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Optimization ◽  
Network Architectures ◽  
Neural Network Optimization ◽  
Local Optima ◽  
Gradient Based ◽  
Neural Network Architectures ◽  
Nature Inspired Algorithms ◽  
Nature Inspired Metaheuristics

Nature-inspired algorithms have been productively applied to train neural network architectures. There exist other mechanisms like gradient descent, second order methods, Levenberg-Marquardt methods etc. to optimize the parameters of neural networks. Compared to gradient-based methods, nature-inspired algorithms are found to be less sensitive towards the initial weights set and also it is less likely to become trapped in local optima. Despite these benefits, some nature-inspired algorithms also suffer from stagnation when applied to neural networks. The other challenge when applying nature inspired techniques for neural networks would be in handling large dimensional and correlated weight space. Hence, there arises a need for scalable nature inspired algorithms for high dimensional neural network optimization. In this chapter, the characteristics of nature inspired techniques towards optimizing neural network architectures along with its applicability, advantages and limitations/challenges are studied.

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