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DUALIPA: a new immune plasma algorithm for path planning of unmanned aerial vehicles

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第4期28卷 1-26页

作者： Aslan, Selcuk Erkin, Tevfik Erciyes Univ Dept Aeronaut Engn Kayseri Turkiye

Unpredictable capabilities and versatile usage purposes of the unmanned aerial vehicles (UAVs) changed the future projections of the developed countries and researchers focused on solving challenging problems about these vehicles. Path planning is among the challenging problems about the UAV systems and requires a solution routine that optimizes some objectives defined for the enemy threats, fuel consumption and turning maneuvers. In this study, a greedy heuristic approach utilizing from the description of the geometric properties of the path planning problem and generating two eligible path candidates was integrated into the immune plasma algorithm (IP algorithm or IPA) and then a new variant called the DUALIPA exploiting the paths of the greedy approach was introduced. As its name implies, DUALIPA has two populations being executed simultaneously and communicated with each other. While the first population of the DUALIPA is initialized with the help of the first path found by the greedy method, the second population is initialized with the help of the second path of the same greedy method. Moreover, for exploiting the qualified solutions of the populations, a deterministic plasma collection procedure and specialized treatment schema that removes the necessity of using the control parameters belonging to the standard IPA were designed and combined with the DUALIPA. The path planning capabilities of the DUALIPA were investigated by using twelve test cases of three different battlefield scenarios in detail. The results found by the DUALIPA were compared to the results of other fourteen path planners based on meta-heuristics. Comparative studies showed that the DUALIPA performs better than all fourteen techniques for eleven of twelve test cases by completing a run of it nearly two times faster.

关键词： Greedy approach Geometric path planning Unmanned aerial vehicles immune plasma algorithm

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Enhancing immune plasma algorithm for Coverage Optimization Problem in Wireless Sensor Networks

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-22页

作者： Tasdemir, Adnan Demirci, Sercan Aslan, Selcuk Ondokuz Mayis Univ Dept Comp Engn TR-55139 Samsun Turkiye Erciyes Univ Dept Aeronaut Engn TR-38280 Kayseri Turkiye

Wireless sensor networks (WSNs) have gained paramount importance in diverse applications, resulting in extensive research efforts. Among the pivotal challenges facing WSNs is the strategic deployment of nodes, which are critical for efficient data processing and dissemination. Maximizing the coverage area of the sensor nodes emerges as a key determinant of optimal performance in various application domains. Leveraging advanced node deployment algorithms holds the promise of significantly enhancing sensor node coverage within monitoring regions, thereby yielding benefits such as reduced energy consumption, prolonged network lifespan, and streamlined sensor operations. This study endeavors to address the coverage area challenge by employing two variants of the immune plasma algorithm (IP), augmented by sophisticated modeling techniques and tools. Inspired by the biological transfer of plasma or antibodies between patients, the IP algorithm offers a robust framework to optimize WSN deployment. Rigorous experimentation showcases the efficacy of the proposed algorithm in effectively addressing the multifaceted challenges inherent in WSN deployment, thereby presenting compelling avenues for future research and implementation.

关键词： Wireless sensor networks Node deployment Coverage optimization immune plasma algorithm Meta-heuristic algorithms Sensor coverage models Binary detection model Probabilistic detection model Optimization techniques

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immune plasma algorithm: A Novel Meta-Heuristic for Optimization Problems

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IEEE ACCESS 2020年 8卷 220227-220245页

作者： Aslan, Selcuk Demirci, Sercan Nevsehir Ilaci Bektas Veli Univ Dept Comp Engn TR-50300 Nevsehir Turkey Ondokuz Mayis Univ Dept Comp Engn TR-55200 Samsun Turkey

The recent global health crisis also known as the COVID-19 or coronavirus pandemic has attracted the researchers' attentions to a treatment approach called immune plasma or convalescent plasma once more again. The main idea lying behind the immune plasma treatment is transferring the antibody rich part of the blood taken from the patients who are recovered previously to the critical individuals and its efficiency has been proven by successfully using against great influenza of 1918, H1N1 flu, MERS, SARS and Ebola. In this study, we modeled the mentioned treatment approach and introduced a new meta-heuristic called immune plasma (IP) algorithm. The performance of the IP algorithm was investigated in detail and then compared with some of the classical and state-of-art meta-heuristics by solving a set of numerical benchmark problems. Moreover, the capabilities of the IP algorithm were also analyzed over complex engineering optimization problems related with the noise minimization of the electro-encephalography signal measurements. The results of the experimental studies showed that the IP algorithm is capable of obtaining better solutions for the vast majority of the test problems compared to other commonly used meta-heuristic algorithms.

关键词： plasmas immune system Optimization Cells (biology) IP networks Heuristic algorithms Viruses (medical) Meta-heuristics immune plasma algorithm plasma treatment

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Best Individual Guided immune plasma algorithm on Solving Path Planning Problem of Unmanned Combat Aerial Vehicles

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UNMANNED SYSTEMS 2024年第0期

作者： Aslan, Selcuk Ozkok, Fatma Ozge Erciyes Univ Dept Aeronaut Engn TR-38039 Kayseri Turkiye Erciyes Univ Dept Comp Engn TR-38039 Kayseri Turkiye

Unmanned aerial vehicles (UAVs) and their variants equipped with sophisticated weapon systems called unmanned combat aerial vehicles (UCAVs) have completely changed the classical war strategies and concept of military operations. For guaranteeing the autonomous flight safety and success of the task being performed by these modern aerial vehicles, a path must be determined optimally after considering some kinematic constrains, existence of enemy threats, fuel or battery limitations. immune plasma algorithm (IP algorithm or IPA) inspired by the implementation steps of a medical method gained popularity with the COVID-19 and known as convalescent or plasma treatment is one of the most recent intelligent optimization or meta-heuristic techniques. In this study, plasma treatment procedure of the IPA was changed with a newly introduced approach called the best individual guidance for short BIG that is based on using the most qualified solution found by the algorithm and three different donors when collecting plasma and BIGIPA was developed as a novel UCAV path planner. For investigating the path planning capabilities of the BIGIPA, a set of detailed experiments was carried out by using different battlefield configurations and assigning various constants to the control parameters such as population size and number of receivers and then obtained results were compared with the results of other path planners based on well-known meta-heuristic algorithms. Experimental studies showed that introduced treatment procedure gives a significant contribution to the convergence performance and qualities of the final solutions especially for the test cases with relatively high dimensionalities and BIGIPA calculates more promising, flight efficient, and safe UCAV paths compared to the tested algorithms.

关键词： Best individual guidance immune plasma algorithm path planning unmanned combat aerial vehicle

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A New immune plasma algorithm Based on Levy Flight 30

A New Immune Plasma Algorithm Based on Levy Flight

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30th IEEE Signal Processing and Communications Applications Conference (SIU)

作者： Erkin, Tevfik Aslan, Selcuk Erciyes Univ Ucak Muhendisligi Bolumu Kayseri Turkey

ISBN: (纸本)9781665450928

immune plasma algorithm (IP algorithm or IPA) is one of the most recently introduced intelligent optimization techniques. IP algorithm modeled by guiding the fundamental properties of the immune or convalescent plasma treatment that gains the researchers' attentions with the ongoing COVID-19 pandemic has been used for solving different types of problems. In this study, the plasma extraction mechanism of the IP algorithm was updated with the Levy flight and a new IPA variant also called Levy-IPA was introduced. The solving capabilities of the newly introduced IPA variant were tested over numerical optimization problems and then obtained results were compared with the results of standard IPA and other intelligent optimization techniques. Comparative studies showed that Levy flight significantly contributes to the performance of the IP algorithm.

关键词： intelligent optimization immune plasma algorithm levy flight

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Decision Probability based immune plasma algorithm for Path Planning Problem of Unmanned Aerial Vehicles 10

Decision Probability based Immune Plasma Algorithm for Path ...

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10th International Conference on Recent Advances in Air and Space Technologies (RAST)

作者： Erkin, Tevfik Aslan, Selcuk Erciyes Univ Dept Aeronaut Engn Kayseri Turkiye

ISBN: (纸本)9798350323023

The increasing operational capabilities of modern unmanned aerial vehicles and their military counterparts focused researchers' attentions for solving complex problems to do with them optimally. In this study, immune plasma algorithm (IP algorithm or IPA) that is a recent intelligent optimization technique was modified by integrating newly designed plasma transfer schema and treatment continuation procedure and then decision probability based IPA (decIPA) was introduced for solving path planning problem of the mentioned aerial vehicles. In order to investigate how the improvements made on the fundamental operations of IPA change the qualities of the discovered paths, a set of experiments and comparative studies were carried out. The comparative studies between decIPA and other path planners allowed to state that decIPA is capable of planning more robust and safe paths especially for the test cases with high number of segmentation points.

关键词： immune plasma algorithm unmanned aerial vehicle path planning

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immune plasma Programming: A new evolutionary computation-based automatic programming method

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Arslan, Sibel Sivas Cumhuriyet Univ Dept Software Engn TR-58140 Sivas Turkiye

immune plasma therapy, one of the treatment modalities, has proven effective in combating the now rapidly spreading COVID-19 and many other pandemics. The immune plasma algorithm (IPA), inspired by the application phases of this treatment modality, is a recently proposed metaheuristic algorithm. Since its introduction, it has achieved promising results in engineering applications. In this paper, we propose for the first time immune plasma programming (IPP) based on the structure of IPA as a new evolutionary computation -based automatic programming (AP) method. It is compared with well-known AP methods such as artificial bee colony programming, genetic programming, and cartesian ant programming using symbolic regression test problems. It is also compared with baseline methods, many of which are based on recurrent neural networks and a real-word problem is solved. The control parameters of IPP are also tuned separately. The results of the experiments and statistical tests have shown that the prediction accuracy and convergence speed of the models produced by IPP are high. Therefore, IPP has been proposed as a method that can be used to solve various problems.

关键词： Automatic programming immune plasma programming immune plasma algorithm Genetic programming Symbolic regression

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