Data-Driven Predictive Modeling to Enhance Search Efficiency of Glowworm-Inspired Robotic Swarms in Multiple Emission Source Localization Tasks

In time-sensitive search and rescue applications, a team of multiple mobile robots broadens the scope of operational capabilities. Scaling multi-robot systems (\u3c 10 agents) to larger robot teams (10 – 100 agents) using centralized coordination schemes becomes computationally intractable during runtime. One solution to this problem is inspired by swarm intelligence principles found in nature, offering the benefits of decentralized control, fault tolerance to individual failures, and self-organizing adaptability. Glowworm swarm optimization (GSO) is unique among swarm-based algorithms as it simultaneously focuses on searching for multiple targets. This thesis presents GPR-GSO—a modification to the GSO algorithm that incorporates Gaussian Process Regression (GPR) based data-driven predictive modeling—to improve the search efficiency of robotic swarms in multiple emission source localization tasks. The problem formulation and methods are presented, followed by numerical simulations to illustrate the working of the algorithm. Results from a comparative analysis show that the GPR-GSO algorithm exceeds the performance of the benchmark GSO algorithm on evaluation metrics of swarm size, search completion time, and travel distance

Retina Based Glowworm Swarm Optimization for Random Cryptographic Key Generation

ان توليد المفاتيح المستندة إلى المقاييس الحيوية يمثل استخدام الميزات المستخرجة من السمات التشريحية (الفسيولوجية) البشرية مثل بصمات الأصابع أو شبكية العين أو السمات السلوكية مثل التوقيع. تتميز القياسات الحيوية لشبكية العين بمتانة متأصلة، وبالتالي، فهي قادرة على توليد مفاتيح عشوائية بمستوى أمان أعلى مقارنة مع السمات الحيوية الأخرى. في السنوات الأخيرة ، اكتسبت خوارزميات التحسين المستوحاة من الطبيعة شعبية كبيرة في معالجة المشكلات الواقعية الصعبة وحل وظائف التحسين المعقدة التي لا تتوفر فيها الحلول الفعلية. في هذه الورقة ، تم اقتراح نظام فعال لتوليد مفاتيح عشوائية آمنة وقوية وفريدة من نوعها تستند إلى ميزات شبكية العين لتطبيقات التشفير. يتم استخراج ميزات شبكية العين باستخدام خوارزمية تحسين سرب الدودة المتوهجة (GSO)  والتي توفر نتائج واعدة من خلال التجارب باستخدام قواعد بيانات شبكية العين القياسية. بالإضافة إلى ذلك، من أجل توفير مفاتيح عشوائية عالية الجودة وغير متوقعة وغير مجددة، تم استخدام الخريطة الفوضوية في النظام المقترح. حيث يتضمن النظام المقترح أربع مراحل رئيسية: التقاط صورة شبكية العين باستخدام أي كاميرا شبكية موجودة في الأسواق, أو باستخدام قاعدة البيانات المتاحة والتي تسمى DRIONS-DB,  ثم معالجتها معالجة اولية، ثم فصل صورة شبكية العين المعالجة مسبقًا إلى أربعة أجزاء باستخدام تحويل مويجات الهار المنفصلة ذات المستوى الواحد (DWHT), بعد ذلك ، يتم استخدام النطاق الفرعي ذو التردد المنخفض (LL) للمرحلة التالية حيث يمثل النطاق الفرعي التشغيلي, بعد ذلك ، يتم استخراج الميزات المثلى باستخدام خوارزمية تحسين سرب الدودة المتوهجة (GSO)، وأخيرًا يتم دمج الميزات المثلى مع الخريطة الفوضوية لإنشاء مفتاح التشفير العشوائي. في النتائج التجريبية، تم استخدام التحليل الإحصائي NIST الذي يتضمن عشرة اختبارات إحصائية للتحقق من عشوائية مفتاح البت الثنائي المولد. مفاتيح التشفير العشوائية التي تم الحصول عليها كانت ناجحة في اختبارات التحليل الإحصائي NIST ، بالإضافة إلى درجة كبيرة من اللامركزية.The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order to provide high-quality random, unpredictable, and non-regenerated keys, the chaotic map has been used in the proposed system. In the experiments, the NIST statistical analysis which includes ten statistical tests has been employed to check the randomness of the generated binary bits key. The obtained random cryptographic keys are successful in the tests of NIST, in addition to a considerable degree of aperiodicity

Review of Metaheuristics and Generalized Evolutionary Walk Algorithm

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief history to their applications. We try to analyze the main components of these algorithms and how and why they works. Then, we intend to provide a unified view of metaheuristics by proposing a generalized evolutionary walk algorithm (GEWA). Finally, we discuss some of the important open questions.Comment: 14 page

Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model,  a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach

Robot navigation and target capturing using nature-inspired approaches in a dynamic environment

Path Planning and target searching in a three-dimensional environment is a challenging task in the field of robotics. It is an optimization problem as the path from source to destination has to be optimal. This paper aims to generate a collision-free trajectory in a dynamic environment. The path planning problem has sought to be of extreme importance in the military, search and rescue missions and in life-saving tasks. During its operation, the unmanned air vehicle operates in a hostile environment, and faster replanning is needed to reach the target as optimally as possible. This paper presents a novel approach of hierarchical planning using multiresolution abstract levels for faster replanning. Economic constraints like path length, total path planning time and the number of turns are taken into consideration that mandate the use of cost functions. Experimental results show that the hierarchical version of GSO gives better performance compared to the BBO, IWO and their hierarchical versions.Comment: 8 pages, 8 figure

Swarm-based Descriptor Combination and its Application for Image Classification

In this paper, we deal with the descriptor combination problem in image classification tasks. This problem refers to the definition of an appropriate combination of image content descriptors that characterize different visual properties, such as color, shape and texture. In this paper, we propose to model the descriptor combination as a swarm-based optimization problem, which finds out the set of parameters that maximizes the classification accuracy of the Optimum-Path Forest (OPF) classifier. In our model, a descriptor is seen as a pair composed of a feature extraction algorithm and a suitable distance function. Our strategy here is to combine distance scores defined by different descriptors, as well as to employ them to weight OPF edges, which connect samples in the feature space. An extensive evaluation of several swarm-based optimization techniques was performed. Experimental results have demonstrated the robustness of the proposed combination approach

Automated Lung Disease Detection and Classification Using Quantum Glowworm Swarm Optimizer with Quasi Recurrent Neural Network on Chest X-Ray Images

Lung diseases or otherwise called respiratory diseases are airborne diseases that affect the lungs and the other tissues of the lungs. Tuberculosis, Coronavirus Disease 2019 (COVID-19), and Pneumonia are a few instances of lung diseases. If the lung disease is diagnosed and treated in the initial stage, the chances of recovery rate and long-term survival rates can be increased. Usually, lung disease is identified by Chest X-Ray (CXR) image examination, skin test, sputum sample test, Computed Tomography (CT) scan examination, and blood test. Because of its non-invasive and convenient evaluation for overall outcomes of the chest situation, Lung disease can be detected by specialized radiologists on CXR images. In recent times, Deep Learning (DL) applies to medical images for disease detection and has proved an effective technique for detecting disease. The recent advancement of DL supports the detection and classification of lung diseases in medicinal imaging. This article presents an Automated Lung Disease Detection Using Quantum Glowworm Swarm Optimization with Quasi Recurrent Neural Network (QGSO-QRNN) model on CXR imaging. The presented QGSO-QRNN technique focuses on the identification of lung diseases using DL concepts. To accomplish this, the presented QGSO-QRNN technique initially performs image pre-processing by the use of the Gaussian Filtering (GF) technique. Besides, the Faster SqueezeNet approach is exploited for feature vector generation. Finally, the QRNN model is applied for precise classification of lung diseases with the QGSO technique as a hyperparameter optimizer. The investigational assessment of the QGSO-QRNN technique is examined by employing standard medical datasets and the outputs display the promising performance of the QGSO-QRNN technique over other existing techniques by means of diverse measures

Hybridizing Invasive Weed Optimization with Firefly Algorithm for Unconstrained and Constrained Optimization Problems

This study presents a hybrid invasive weed firefly optimization (HIWFO) algorithm for global optimization problems. Unconstrained and constrained optimization problems with continuous design variables are used to illustrate the effectiveness and robustness of the proposed algorithm. The firefly algorithm (FA is effective in local search, but can easily get trapped in local optima. The invasive weed optimization (IWO) algorithm, on the other hand, is effective in accurate global search, but not in local search. Therefore, the idea of hybridization between IWO and FA is to achieve a more robust optimization technique, especially to compensate for the deficiencies of the individual algorithms. In the proposed algorithm, the firefly method is embedded into IWO to enhance the local search capability of IWO algorithm that already has very good exploration capability. The performance of the proposed method is assessed with four well-known unconstrained problems and four practical constrained problems. Comparative assessments of performance of the proposed algorithm with the original FA and IWO are carried out on the unconstrained problems and with several other hybrid methods reported in the literature on the practical constrained problems, to illustrate its effectiveness. Simulation results show that the proposed HIWFO algorithm h as superior searching quality and robustness than the approaches considered