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A Hybrid Retinex-Based Algorithm for UAV-Taken Image Enhancement

IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第11期E104D卷 2024-2027页

作者： Liu, Xinran Wang, Zhongju Wang, Long Huang, Chao Luo, Xiong Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528300 Peoples R China

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

关键词： image enhancement multiscale Retinex Rao algorithm evolutionary computing

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Zombie politics: evolutionary algorithms to counteract the spread of negative opinions

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SOFT computing 2020年第1期24卷 591-601页

作者： Hochreiter, Ronald Waldhauser, Christoph Webster Vienna Private Univ Dept Business & Management Praterstr 23 A-1020 Vienna Austria Raiffeisen Bank Int AG Stadtpk 9 A-1030 Vienna Austria

This paper is about simulating the spread of opinions in a society and about finding ways to counteract that spread. To abstract away from potentially emotionally laden opinions, we instead simulate the spread of a zombie outbreak in a society. The virus causing this outbreak is different from traditional approaches: It not only causes a binary outcome (healthy vs. infected) but rather a continuous outcome. To counteract the outbreak, a discrete number of infection-level-specific treatments are available. This corresponds to acts of mild persuasion or the threats of legal action in the opinion spreading use case. This paper offers a genetic and a cultural algorithm that find the optimal mixture of treatments during the run of the simulation. They are assessed in a number of different scenarios. It is shown that albeit far from being perfect, the cultural algorithm delivers superior performance at lower computational expense.

关键词： Dynamic optimization Opinion propagation Epidemiology evolutionary computing

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Pareto-Optimal Gamma Spectroscopic Radionuclide Identification Using evolutionary computing

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2013年第3期60卷 2222-2231页

作者： Alamaniotis, Miltiadis Mattingly, John Tsoukalas, Lefteri H. Purdue Univ Sch Nucl Engn Appl Intelligent Syst Lab W Lafayette IN 47907 USA N Carolina State Univ Dept Nucl Engn Raleigh NC 27695 USA

Detection and identification of special nuclear materials (SNM) in measured gamma spectra is a challenge for nuclear security applications of spectroscopy. There are ongoing international efforts in industry, academia, and government to develop accurate, robust automated methods of gamma spectroscopic analysis to identify signature patterns characteristic of SNM. In this paper, we introduce a new approach for processing gamma-ray spectra measured by an NaI detector. The approach searches for radionuclide signature patterns in the measured spectrum by formulating a multiobjective optimization problem. Search in the multiobjective space is performed by an evolutionary algorithm, which identifies a solution in the context of Pareto Optimality Theory. The proposed approach is composed of two main steps: 1) signature selection, and 2) Pareto-optimal fitting. The output of the algorithm includes a list of all the identified radionuclides and their relative contributions to the gamma spectrum. The methodology is applied to a set of gamma spectra and compared to multiple linear regression fitting. Results demonstrate the superiority of the Pareto-optimal approach over multiple regression in the majority of the tested cases.

关键词： evolutionary computing gamma spectroscopy multiobjective optimization Pareto-optimal spectrum fitting

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EvoCluster: An Open-Source Nature-Inspired Optimization Clustering Framework

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SN Computer Science 2021年第3期2卷 185页

作者： Qaddoura, Raneem Faris, Hossam Aljarah, Ibrahim Castillo, Pedro A. Information Technology Philadelphia University Amman Jordan King Abdullah II School for Information Technology The University of Jordan Amman Jordan ETSIIT-CITIC University of Granada Granada Spain

EvoCluster is an open source and cross-platform framework implemented in Python language, which includes the most well-known and recent nature-inspired metaheuristic optimizers that are customized to perform partitional clustering tasks. This paper is an extension to the existing EvoCluster framework in which it includes different distance measures for the objective function, different techniques of detecting the k value, and a user option to consider either supervised or unsupervised datasets. The current implementation of the framework includes ten metaheuristic optimizers, thirty datasets, five objective functions, twelve evaluation measures, more than twenty distance measures, and ten different ways for detecting the k value. The source code of EvoCluster is publicly available at http://***/evocluster/. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. part of Springer Nature.

关键词： Cluster analysis Clustering evolutionary computing Framework Python

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Reconfigurable Scheduling as a Discrete-Event Process: Monte Carlo Tree Search in Industrial Manufacturing 2

Reconfigurable Scheduling as a Discrete-Event Process: Monte...

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2nd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL)

作者： Helliwell, T. J. Morgan, B. Vincent, A. Forgeoux, G. Mahfouf, M. Univ Sheffield Automat Control & Syst Engn Dept Sheffield S Yorkshire England Univ Sheffield Adv Mfg Res Ctr AMRC Sheffield S Yorkshire England Safran Landing Syst Gloucester England

ISBN: (纸本)9789897585357

In this paper we introduce a theoretical basis for reconfigurable makespan scheduling that is computationally-efficient and general purpose in manufacturing. A full-scale scale case study for batch production in the aerospace industry is shown. A knowledge-based Discrete-Event System, based on a Timed Petri Net, is injected with the initial - current - state and simulated to generate trajectories that represent valid possible schedules or policies analogous to the Monte-Carlo Tree Search (MCTS) planning algorithm. A new, concise, evolutionary metaheuristic is proposed called Elitist Trajectory Mutation (ETM) in order to exploit high performing schedules in localising search and optimisation. The advantage of this approach is reconfigurability, extensibility and ability to be parallelised to enable satisficing performance for real-time applications such as intelligent industrial cyber-physical systems scheduling, autonomous control of distributed systems and active industrial informatics.

关键词： Reconfigurable Scheduling Autonomous Planning Discrete-Event Systems evolutionary computing Generative Models Manufacturing Systems

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3D-2D Registration Using X-Ray Simulation and CMA-ES 24th

3D-2D Registration Using X-Ray Simulation and CMA-ES

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24th International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Wen, Tianci Mihail, Radu P. Vidal, Franck P. Bangor Univ Sch Comp Sci & Elect Engn Bangor Gwynedd Wales Valdosta State Univ Dept Comp Sci Valdosta GA USA

ISBN: (纸本)9783030726980;9783030726997

Radiographs of the hand are useful in diagnosing and staging diseases such as rheumatoid arthritis (RA) and other musculoskeletal diseases. Radiographs are projections of the 3D anatomy, with the useful information such as pose and pathology becoming lost in the process. We propose a 3D hand pose recovery method for radiographs of hands using a novel hybrid image registration method. Our pose recovery pipeline consists of aligning a simulated X-ray (digitally reconstructed radiograph) of an articulated phantom mesh model to a real hand radiograph using Covariance Matrix Adaptation Evolution Strategy. Early results demonstrate that our approach works well. Further inquiry is required to evaluate the applicability of our registration approach to other articulated musculoskeletal anatomy.

关键词： 2D-3D registration X-ray simulation Artificial evolution evolutionary computing CMA-ES DRRs

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Intelligent optimization for multiprocessor systems: hybrid algorithmic strategies for scheduling and load balancing

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COGENT ENGINEERING 2024年第1期11卷

作者： Reddy, Gundreddi Deepika Medikondu, Nageswara Rao Kumar, T. Vijaya Koneru, Sireesha Bobba, Phaneendra Babu Singla, Atul Pandey, Alok Kumar Al-Jawahry, Hassan M. Koneru Lakshmaiah Educ Fdn Dept Mech Engn Vaddeswaram Andhra Pradesh India GRIET Dept Elect & Elect Engn Hyderabad India Lovely Profess Univ Sch Architecture Div Res & Dev Phagwara Punjab India Uttaranchal Univ Dept Engn Dehra Dun India Islamic Univ Coll Tech Engn Dept Comp Tech Engn Najaf Iraq

Efficient scheduling and load balancing are essential for optimizing performance in multiprocessor systems. This study proposes a novel hybrid algorithm that integrates beam search and differential evaluation techniques within the domain of artificial intelligence (AI) to address these challenges. Our objective is to minimize the operational completion time (OCT), a critical metric for evaluating system performance. Beam search is utilized to explore the solution space effectively, enabling the algorithm to identify promising solutions. Moreover, we employ a differential evaluation approach to assess the quality of candidate solutions and guide the search toward optimal or near-optimal scheduling and load-balancing configurations. By combining these techniques, our hybrid algorithm aims to minimize OCT, thereby enhancing system throughput and resource utilization. Experimental evaluations demonstrate the effectiveness of our approach in achieving improved performance compared to traditional methods. This research contributes to advancing the field of AI in multiprocessor systems optimization, providing practical solutions for real-world deployment in high-performance computing environments.

关键词： Multiprocessor systems hybrid algorithm beam search differential evaluation operational completion time Professor Swadesh Kumar Singh, Gokaraju Rangaraju Institute of Engineering and Technology, India evolutionary computing Artificial Intelligence Automation

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Exploiting variability in the design of genetic algorithms to generate telerehabilitation activities

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APPLIED SOFT computing 2022年 117卷 108441-108441页

作者： Moya, Alejandro Navarro, Elena Jaen, Javier Lopez-Jaquero, Victor Capilla, Rafael Univ Castilla La Mancha LoUISE Res Grp Albacete 02071 Spain Univ Politecn Valencia Inst Univ Mixto Tecnol Informat Valencia Spain Univ Rey Juan Carlos SAIT Res Grp Madrid Spain

The increasing number of people with impairments and the lack of specialists has led to a loss of efficiency to deliver proper treatments from National healthcare systems. In this light, telerehabilitation can play an important role as patients can perform certain therapies at home. Consequently, telerehabilitation systems must support delivering bespoke therapies to patients tailored to their deficits and preferences. However, creating bespoke telerehabilitation activities is a complex and time-consuming task because of the great assortment of deficits. To address this problem, we propose in this research work an automatic generation of such telerehabilitation activities aiming to both assist the specialist in designing and creating telerehabilitation activities that best fit each patient's needs. Therefore, the main contributions of this paper are: (1) the exploitation of Feature Models (FM) to describe the variability in the telerehabilitation domain and to facilitate the communication among the stakeholders to accurately specify the patients' deficits and the features of an association telerehabilitation activity. (2) The design and development of a genetic algorithm (GA) relying on the specified FM able to generate customized association telerehabilitation activities. The FM specified describes precisely the search problem so that the GA chromosomes can be easily identified. It also facilitates the discussion with the stakeholders during the design of the algorithm since its specification can be understood by non-experts in Computer Science. (3) The evaluation of the effectiveness and efficiency of the GA developed by using two sets of experiments: one for tuning the parameters of the GA and another to assess the effectiveness and efficiency of the algorithm while stressed under constraining conditions. (4) The integration of the proposal with a tool for telerehabilitation of people with Acquired Brain Injury (ABI). The proposal targets people with ABI because

关键词： Genetic algorithm evolutionary computing Experimentation Feature Model Adaptation Telerehabilitation User model Acquired Brain Injury

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A Novel Bio-Inspired Path Planning for Autonomous Underwater Vehicle for Search and Tracing of Underwater Target 4

A Novel Bio-Inspired Path Planning for Autonomous Underwater...

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4th International Conference on Innovative computing (ICIC)

作者： Khalil, Adnan Elahi Khan Anwar, Shahzad Husnain, Ghassan Elahi, Atif Dong, Zhang Univ Engn & Technol Dept Mechatron Engn Peshawar Pakistan Iqra Natl Univ Dept Comp Sci Peshawar Pakistan Int Islamic Univ Fac Engn & Technol Dept Elect Engn H-10 Islamabad Pakistan Shandong Acad Sci Inst Automat Jinan Peoples R China

ISBN: (纸本)9781665400916

Underwater search and tracing is a complex engineering problem, due to various factors, such as unpredicted oceanic environment, poor communications, underwater navigations. Therefore, to accomplish a challenging mission an autonomous underwater vehicle (AUV) cooperation is needed. This study focuses on the search and tracing of an underwater target, based on an improved cooperative path planning model. To achieve the possible results, the mission is divided into search and tracing phases aiming to maximize the search space thus reducing terminal error respectively. The developed method is inspired by bio-inspired Improved Whale Optimization Algorithm (IWOA) based method, hence modeling improved path for autonomous underwater vehicle followed by simulations of various parameters for experimentation, such as maneuverability and communication ranges between autonomous vehicles, in a distributed or centralized scenarios. For the above process, the IWOA is compared with the improved particle swarm optimization (IPSO) algorithm for different parameters for the search and tracing of the underwater target. The simulated results exhibit that the proposed method presents superior results for the search and tracing phase, which also increases the computational performance together with well-achieved optimization on large-scale complex problems for cooperative path planning.

关键词： evolutionary computing Meta-Heuristic Algorithms Improved path planning Acoustic communication Autonomous vehicles Microwave Communication

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Genetic Crossover in the Evolution of Time-dependent Neural Networks

Genetic Crossover in the Evolution of Time-dependent Neural ...

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Orlosky, Jason Grabowski, Tim Osaka Univ Osaka Japan

ISBN: (纸本)9781450383509

Neural networks with temporal characteristics such as asynchronous spiking have made much progress towards biologically plausible artificial intelligence. However, genetic approaches for evolving network structures in this area are still relatively unexplored. In this paper, we examine a specific variant of time-dependent spiking neural networks (NN) in which the spatial and temporal relationships between neurons affect output. First, we built and customized a standard NN implementation to more closely model the time-delay characteristics of biological neurons. Next, we tested this with simulated tasks such as food foraging and image recognition, demonstrating success in multiple domains. We then developed a genetic representation for the network that allows for both scalable network size and compatibility with genetic crossover operations. Finally, we analyzed the effects of genetic crossover algorithms compared to random mutations on the food foraging task. Results showed that crossover operations based on node usage converge on a local maximum more quickly than random mutations, but suffer from genetic defects that reduce overall population performance.

关键词： Genetic Crossover evolutionary computing Spiking Neural Network Simulation

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