DYNAMIC PROBABILITY SELECTION FOR FLOWER POLLINATION ALGORITHM BASED ON METROPOLISHASTINGS CRITERIA

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Abstract

Flower Pollination Algorithm (FPA) is a relatively new meta-heuristic algorithm that adopts its metaphor from the proliferation role of flowers in plants. Having only one parameter control (i.e. the switch probability, pa) to choose from the global search (i.e. exploration) and local search (i.e. exploitation) is the main strength of FPA as compared to other meta-heuristic algorithms. However, FPA still suffers from variability of its performance as there is no one size that fits all values for pa, depending on the characteristics of the optimisation function. This paper proposed flower pollination algorithm metropolis-hastings (FPA-MH) based on the adoption of Metropolis-Hastings criteria adopted from the Simulated Annealing (SA) algorithm to enable dynamic selection of the pa probability. Adopting the problem of t-way test suite generation as the case study and with the comparative evaluation with the original FPA, FPA-MH gave promising results owing to its dynamic and adaptive selection of search operators based on the need of the current search.

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Is Open Access

GENDER CLASSIFICATION ON SKELETAL REMAINS: EFFICIENCY OF METAHEURISTIC ALGORITHM METHOD AND OPTIMIZED BACK PROPAGATION NEURAL NETWORK

Nurul Liyana Hairuddin, Lizawati Mi Yusuf, Mohd Shahizan Othman (2020)

In forensic anthropology, gender classification is one of the crucial steps involved in developing the biological profiles of skeleton remains. There are several different parts of skeleton remains and every part contains several features. However, not all features can contribute to gender classification in forensic anthropology. Besides that, another limitation that exists in previous researches is the absence of parameter optimization for the classifier. Thus, this paper proposed metaheuristic algorithms such as Particle Swarm Optimization, Ant Colony Algorithm and Harmony Search Algorithm based feature selection to identify the most significant features of skeleton remains. Once the set of significant features was obtained, the learning rate and momentum of Back Propagation Neural Network (BPNN) were optimized. This was to obtain a good combination of parameters in order to produce a better gender classification. This study used 1,538 data samples from Goldman Osteometric Dataset which consisted of femur, humerus and tibia parts. Based on the feature selection results, the Optimized BPNN outperformed other methods for all datasets. The Ant Colony Algorithm-Optimized Back Propagation Neural Network produced the highest accuracy for all parts of the skeleton where for femur was 89.44%, the humerus with 88.97% and tibia with 87.52% accuracy. Hence, it can be concluded that optimized parameter is capable of providing a better gender classification performance with the best set of features. Due to good gender classification techniques, the implication of this study is evident in the area of forensic anthropology where the process of developing a biological profile can be shortened which in turn enhances the productivity of anthropologists.

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Author and article information

Contributors

Kamal Zuhairi Zamli: Malaysia

Fakhrud Din: Pakistan

Abdullah Nasser: Malaysia

Nazirah Ramli: Malaysia

Noraini Mohamed: Malaysia

Journal

Title: Journal of Information and Communication Technology

Publisher: UUM Press

Publication date (Electronic): November 04 2020

Volume: 20

Pages: 41-56

Affiliations

[1 ]Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Malaysia

[2 ]Department of Computer Science & IT, University of Malakand, Pakistan

[3 ]Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Pahang, Malaysia

Article

Internal ID: 10010 Public ID: jict.20.1.2021.11898

DOI: 10.32890/jict.20.1.2021.11898

SO-VID: 6d224271-89a6-451d-8cc6-341c00e69936

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All content is freely available without charge to users or their institutions. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles in this journal without asking prior permission of the publisher or the author. Articles published in the journal are distributed under a http://creativecommons.org/licenses/by/4.0/.

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ScienceOpen disciplines: Communication networks,Applied computer science,Computer science,Information systems & theory,Networking & Internet architecture,Artificial intelligence

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ScienceOpen disciplines: Communication networks, Applied computer science, Computer science, Information systems & theory, Networking & Internet architecture, Artificial intelligence

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DYNAMIC PROBABILITY SELECTION FOR FLOWER POLLINATION ALGORITHM BASED ON METROPOLISHASTINGS CRITERIA

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