Multiple Sequence Alignment Menggunakan Nature-Inspired Metaheuristic Algorithms

Multiple sequence alignment adalah proses dasar yang sering dibutuhkan dalam mengolah beberapa sequence yang berhubungan dengan bioinformatika. Apabila multiple sequence alignment telah selesai dikerjakan, maka dapat dilakukan analisis-analisis lain yang lebih jauh, seperti analisis filogenetik atau prediksi struktur protein. Banyaknya kegunaan dari multiple sequence alignment mengakibatkannya menjadi salah satu permasalahan yang banyak diteliti. Banyak algoritma-algoritma metaheuristic yang berdasar pada kejadian-kejadian alami, yang biasa disebut dengan nature-inspired metaheuristic algorithms. Beberapa algoritma baru dalam nature-inspired metaheuristic algorithms yang dianggap cukup efisien antara lain adalah firefly algorithm, cuckoo search, dan flower pollination algorithm. Dalam penelitian ini dipaparkan modified Needleman-Wunsch alignment. Didapatkan hasil bahwa modified Needleman-Wunsch alignment adalah metode yang cukup bagus. Modified Needleman-Wunsch alignment tersebut digunakan untuk membentuk solusi awal dari firefly algorithm, cuckoo search, dan flower pollination algorithm. Didapatkan hasil bahwa firefly algorithm, cuckoo search, dan flower pollination algorithm dapat menghasilkan solusi-solusi baru yang lebih baik. Secara keseluruhan, firefly algorithm adalah algoritma yang terbaik dari tiga algoritma tersebut dalam segi skor alignment, namun membutuhkan waktu komputasi yang lebih besar. ======================================================================================== Multiple sequence alignment is a fundamental tool that often needed to process bioinformatic sequences. If multiple sequence alignment is completed, we can process other further analysis, such as phylogenetic analysis or protein structure prediction. The versatility of multiple sequence alignment led it to be the one of the problems that studied continously. Many metaheuristic algorithms are based on natural events, with the so called nature-inspired metaheuristic algorithms. Algorithms in nature-inspired metaheuristic algorithms that considered to be good are firefly algorithm, cuckoo search, and flower pollination algorithm. In this research, we propose modified Needleman-Wunsch alignment. The results show that modified Needleman-Wunsch alignment is a good method. Modified Needleman-Wunsch alignment is used to create initial solution of firefly algorithm, cuckoo search, and flower pollination algorithm. The results show that firefly algorithm, cuckoo search, and flower pollination algorithm can produce new better solution. Overall, firefly algorithm is the best algorithm among the others in alignment score, but need large computation time

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

Construction of Prioritized T-Way Test Suite Using Bi-Objective Dragonfly Algorithm

Software testing is important for ensuring the reliability of software systems. In software testing, effective test case generation is essential as an alternative to exhaustive testing. For improving the software testing technology, the t-way testing technique combined with metaheuristic algorithm has been great to analyze a large number of combinations for getting optimal solutions. However, most of the existing t-way strategies consider test case weights while generating test suites. Priority of test cases hasn’t been fully considered in previous works, but in practice, it’s frequently necessary to distinguish between high-priority and low-priority test cases. Therefore, the significance of test case prioritization is quite high. For this reason, this paper has proposed a t-way strategy that implements an adaptive Dragonfly Algorithm (DA) to construct prioritized t-way test suites. Both test case weight and test case priority have equal significance during test suite generation in this strategy. We have designed and implemented a Bi-objective Dragonfly Algorithm (BDA) for prioritized t-way test suite generation, and the two objectives are test case weight and test case priority. The test results demonstrate that BDA performs competitively against existing t-way strategies in terms of test suite size, and in addition, BDA generates prioritized test suites.©2022 Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed

Quantum Genetic Algorithms for Computer Scientists

Genetic algorithms (GAs) are a class of evolutionary algorithms inspired by Darwinian natural selection. They are popular heuristic optimisation methods based on simulated genetic mechanisms, i.e., mutation, crossover, etc. and population dynamical processes such as reproduction, selection, etc. Over the last decade, the possibility to emulate a quantum computer (a computer using quantum-mechanical phenomena to perform operations on data) has led to a new class of GAs known as “Quantum Genetic Algorithms” (QGAs). In this review, we present a discussion, future potential, pros and cons of this new class of GAs. The review will be oriented towards computer scientists interested in QGAs “avoiding” the possible difficulties of quantum-mechanical phenomena

Monte Carlo Method with Heuristic Adjustment for Irregularly Shaped Food Product Volume Measurement

Volume measurement plays an important role in the production and processing of food products. Various methods have been proposed to measure the volume of food products with irregular shapes based on 3D reconstruction. However, 3D reconstruction comes with a high-priced computational cost. Furthermore, some of the volume measurement methods based on 3D reconstruction have a low accuracy. Another method for measuring volume of objects uses Monte Carlo method. Monte Carlo method performs volume measurements using random points. Monte Carlo method only requires information regarding whether random points fall inside or outside an object and does not require a 3D reconstruction. This paper proposes volume measurement using a computer vision system for irregularly shaped food products without 3D reconstruction based on Monte Carlo method with heuristic adjustment. Five images of food product were captured using five cameras and processed to produce binary images. Monte Carlo integration with heuristic adjustment was performed to measure the volume based on the information extracted from binary images. The experimental results show that the proposed method provided high accuracy and precision compared to the water displacement method. In addition, the proposed method is more accurate and faster than the space carving method

Comparing AI Algorithms for Optimizing Elliptic Curve Cryptography Parameters in Third-Party E-Commerce Integrations: A Pre-Quantum Era Analysis

This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters. These encompass the elliptic curve coefficients, prime number, generator point, group order, and cofactor. The study provides insights into which of the bio-inspired algorithms yields better optimization results for ECC configurations, examining performances under the same fitness function. This function incorporates methods to ensure robust ECC parameters, including assessing for singular or anomalous curves and applying Pollard's rho attack and Hasse's theorem for optimization precision. The optimized parameters generated by GA and PSO are tested in a simulated e-commerce environment, contrasting with well-known curves like secp256k1 during the transmission of order messages using Elliptic Curve-Diffie Hellman (ECDH) and Hash-based Message Authentication Code (HMAC). Focusing on traditional computing in the pre-quantum era, this research highlights the efficacy of GA and PSO in ECC optimization, with implications for enhancing cybersecurity in third-party e-commerce integrations. We recommend the immediate consideration of these findings before quantum computing's widespread adoption.Comment: 14 page