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Leveraging enhanced egret swarm optimization algorithm and artificial intelligence-driven prompt strategies for portfolio selection

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SCIENTIFIC REPORTS 2024年第1期14卷 1-20页

作者： Huang, Zhendai Zhang, Zhen Hua, Cheng Liao, Bolin Li, Shuai Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Changsha Univ Sch Elect Informat & Elect Engn Changsha 410003 Peoples R China Univ Oulu Fac Informat Technol & Elect Engn Oulu 90570 Finland VTT Tech Res Ctr Finland Kaitovayla 1 Oulu 90570 Finland

In the financial field, constructing efficient investment portfolios is a focal point of research, encompassing asset selection and optimization of asset allocation. With the advancements in Large Language Models (LLMs), generative Artificial Intelligence (AI) tools have showcased capabilities never seen before. However, the black-box nature of these tools renders their outputs difficult to interpret directly, often necessitating iterative fine-tuning to align with users' expected outcomes. This study presents a structured prompt framework specifically designed for stock selection, aiming to provide direct and interpretable stock-selecting tools for investors of various levels. By creating representative scenarios and combining them into different cases for experimentation, we can explore how the construction of prompts influences the responses generated by generative AI tools. Additionally, this paper proposes a novel algorithm that combines the Nonlinear-Activated Beetle Antennae Search strategy with the Egret Swarm Optimization algorithm (NBESOA) to address the Mean-Variance Portfolio Selection problem with Transaction Costs and Cardinality Constraints (MVPS-TCCC), utilizing real stock market data to construct portfolios based on generative AI tools recommendations. Simulation results indicate that, compared to other algorithms, NBESOA prefers optimizing portfolio configurations to achieve the highest Sharpe Ratio with the strictest constraints, bringing the outcomes closer to the portfolio's efficient frontier.

关键词： Large language models Prompt engineering Egret swarm optimal algorithm Portfolio selection bio-inspired algorithm

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Modified flower pollination algorithm for optimizing FOPID controller and its application with the programmablen-level inverter using fuzzy logic

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SOFT COMPUTING 2021年第4期25卷 2615-2633页

作者： Rajeswari, Chidambaram Santhi, M. PTR Coll Engn & Technol Dept Elect & Elect Engn Madurai Tamil Nadu India Solamalai Coll Engn Dept Elect & Elect Engn Madurai Tamil Nadu India

Ann-level multilevel inverter for PV connected systems is proposed in this paper. The proposed inverter is the combination of a buck-boost converter and an H-bridge inverter ((BH)-H-2 multilevel inverter). This system produces a constant AC output voltage with multilevels for a PV connected system with few number of switches. With the proposed inverter, the constant output voltage level is obtained from the PV system irrespective of the variable irradiance. Any number of levels is achieved by varying the duty cycle of the buck-boost converter over a wide range using an n-level generation algorithm. Thus, many numbers of levels can be attained in the output voltage which reduces the THD significantly. The voltage regulation process is performed by designing an optimized fractional-order proportional integral and derivative (FOPID) controller. The FOPID controller provides improved voltage regulation characteristics through the optimization of parameters of controller with the use of modified flower pollination algorithm. The proposed configuration is designed and implemented in MATLAB Simulink. The quality of output, their harmonics and the output resolution time are evaluated for various input voltages and for various numbers of output levels.

关键词： Multilevel inverter Converter Buck-boost converter Optimization problem bio-inspired algorithm Harmonics

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A Survey on Deep Learning Models Embed bio-inspired algorithms in Cardiac Disease Classification

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Open biomedical Engineering Journal 2023年 17卷

作者： Pandiyan, Nandakumar Narayan, Subhashini School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

Deep learning is a sub-field of machine learning that emerged as a noticeable model in the world, specifically for the disease classification field. This work aims to review the state-of-the-art deep learning models in Cardiac Disease prediction by examining several research papers. In this study, popular datasets listed and analyzed in the prediction process of cardiac disease with their performance using various deep learning techniques are presented. This review emphasizes the latest advancement in the six deep learning models, namely, deep neural networks, convolutional neural networks, recurrent neural networks, extreme learning machines, deep belief networks, and transfer learning with its applications. The important features of cardiac disease used by five different countries have been listed that guide researchers to analyze it for future purposes. Freshly, deep learning models have yielded an extended performance in cardiac disease detection that shows its rapid growth. Specifically, deep learning effectiveness concerted with the bio-inspired algorithms is reviewed. This paper also presents what major applications of deep learning techniques have been grasped in the past decade. © 2023 Pandiyan and Narayan.

关键词： algorithms bio-inspired algorithm Cardiac arrests Cardiac disease Deep learning Optimization

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Binary Bat algorithm for Graph Coloring Problem 2

Binary Bat Algorithm for Graph Coloring Problem

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2014 Second World Conference on Complex Systems (WCCS)

作者： Djelloul, Halima Sabba, Sara Chikhi, Salim Constantine 2 Univ MISC Lab Dept Comp Sci La Nouvelle Ville Ali Me Algeria

ISBN: (纸本)9781479946471

Bat algorithm (BA) is one of the most recent bioinspired algorithm. It is based on the echolocation behavior of microbats. The standard BA is proposed only for continuous optimization problems. In this paper, we try to solve the graph coloring problem using a binary bat algorithm. To show the feasibility and the effectiveness of the algorithm, we have used the DIMACS benchmark, and the obtained results are very encouraging.

关键词： bat algorithm bio-inspired algorithm discrete bat algorithm graph coloring problem heuristic discrete optimization problem

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Comparison Between Single and Multi-objective Clustering algorithms: MathE Case Study 1

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4th International Conference on Optimization Learning algorithms and Applications

作者： Azevedo, Beatriz Flamia Rocha, Ana Maria A. C. Fernandes, Florbela P. Pacheco, Maria F. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Aveiro Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M P-5300253 Braganca Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

This paper compares the results obtained for four single clustering algorithms with a multi-objective clustering approach. For this, a dataset describing the student's behavior within the Linear Algebra topic on the MathE e-learning platform is used. This dataset aids in understanding student performance and engagement in MathE to support the development of an intelligent system to tailor the platform's resources to users's needs. The four algorithms suggested two clusters as the optimal solution for the dataset. However, this binary categorization did not provide meaningful insights into the proposal of the MathE platform;that is, it did not provide a customized system according to individual needs. Thus, this study uses the multi-objective clustering algorithm, which results in a set of non-dominated solutions, providing decision-makers with a broader range of options to choose the solution that best meets their needs. The results demonstrate the main benefits of the proposed human-in-the-loop multi-objective approach since it provides several optimal solutions and allows the decision-maker to apply fundamental knowledge to define the most appropriate solution to the problem based on previous knowledge.

关键词： multi-objective clustering automatic clustering optimization bio-inspired algorithm

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A Bat algorithm for Job Scheduling in Ceramics Production Lines

A Bat Algorithm for Job Scheduling in Ceramics Production Li...

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International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS)

作者： Tupia, Manuel Cueva, Rony Guanira, Miguel Pontificia Univ Catolica Peru Dept Engn Ave Univ 1801 San Miguel Lima Peru

ISBN: (纸本)9781538605141

The wide array of products manufactured in ceramics production lines and their manufacturing limitations fall into the job scheduling environment. Task scheduling involved in this problem 's variant has already been solved by several authors approaches [1]. That said, this paper intends to propose the use of a relatively new metaheuristics called bat algorithm [3] ( BA) to solve such scenarios. Our study includes a numerical experimentation process which confronts the proposed BA algorithm against a Genetic algorithm [2] and a GRASP algorithm [1].

关键词： Bat algorithm Job scheduling Production Scheduling Meta heuristics bio-inspired algorithm

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bio-inspired Scheme of Killer Whale Hunting-Based Behaviour for Enhancing Performance of Wireless Sensor Network 3rd

Bio-Inspired Scheme of Killer Whale Hunting-Based Behaviour ...

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3rd Conference on Data Engineering and Communication Technology (ICDECT)

作者： Parvathi, C. Talanki, Suresha Sri Venkateshwara Coll Engn Dept Comp Sci & Engn Bengaluru India Sri Venkateshwara Coll Engn Bengaluru India

ISBN: (纸本)9789811510977;9789811510960

Optimization is one of the best alternative solutions for addressing majority of the computational challenges associated with the wireless sensor network (WSN). At present, the successful implications of bio-inspired approaches are significantly proven to solve diversified complex problems in wireless network. However, after reviewing the existing literatures, it was found that yet there is bigger scope to enhance the capability of bio-inspired approach in the area of WSN. Therefore, this paper introduces a novel bio-inspired algorithm called as killer whale hunting (KWH) targeted for optimizing the data aggregation process with highly improved network sustenance capability. The formulation of the algorithm is designed on the basis of social and cognitive behaviour of killer whale which has a distinct style of hunting its prey. Using analytical modelling, the proposed approach was found to offer better energy efficiency and sufficient data forwarding capability in contrast to existing energy-efficient bio-inspired techniques.

关键词： Energy bio-inspired algorithm Optimization Data aggregation Wireless sensor network

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Multi-objective chemical reaction optimization based decomposition for multi-objective traveling salesman problem 3

Multi-objective chemical reaction optimization based decompo...

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3rd IEEE World Conference on Complex Systems (WCCS)

作者： Bouzoubia, Samira Layeb, Abdesslem Chikhi, Salim Constantine 2 Univ Dept Fundamental Comp Sci & Its Applicat MISC Lab Constantine 25017 Algeria

ISBN: (纸本)9781467396691

The multi-objective traveling salesman problem (MOTSP) is a well-known hard discrete optimization problem. Indeed, finding efficient MOTSP solutions is still a challenging problem. In this paper, we propose an approach called, a multi-objective chemical reaction optimization based decomposition (MOCRO/D), which is based on both Tchebycheff approach and chemical reactions concepts. In MOCRO/D, an MOTSP is decomposed into a set of scalar objective sub-problems. The chemical reactions are used to guide the search for each sub-problem. Thanks to the decomposition approach, MOCRO/D optimizes all the subproblems simultaneously. Finally, the experimental results have shown the superior performance of MOCRO/D compared to NSGA2 and MOEA/D on most of the given test instances.

关键词： component Chemical Reaction Optimization Multi-objective optimization Decomposition bio-inspired algorithm multi-objective traveling salesman problem

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Selective Harmonics Elimination in Multilevel Inverter Using bio-inspired Intelligent algorithms 31

Selective Harmonics Elimination in Multilevel Inverter Using...

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31st Australasian Universities Power Engineering Conference (AUPEC)

作者： Shahbaz, Rawal Ahmed, Toqeer Elavarasan, Rajvikram Madurai Raju, Kannadasan Waqas, Muhammad Subramaniam, Umashankar Bahria Univ Dept Elect Engn Islamabad Pakistan Int Islamic Univ Ctr Adv Elect & Photovolta Engn CAEPE Islamabad Pakistan Texas A&M Univ Clean & Resilient Energy Syst Galveston TX USA Sri Venkateswara Coll Eng Dept Elect Engn Chennai Tamil Nadu India James Cook Univ Coll Sci & Engn Townsville Qld Australia Coll Eng Prince Sultan Uni Renewable Energy Lab Riyadh Saudi Arabia

ISBN: (纸本)9781665434515

Multilevel inverters are powerful electronic devices that are used for the conversion of DC input voltage into AC output voltage and mostly used in medium and high voltage operations. In these operations, pulse width modulation (PWM) frequency is distorted because of electromagnetic interference (EMI) and switching losses which are caused by dv/dt stress. To achieve a pure sinusoidal waveform at output of multilevel inverter is a primary purpose so that a smaller number of harmonic contents are produced. Selective harmonic elimination PWM technique is used in cascaded multilevel inverter for the mitigation of lower harmonics by solving non-linear transcendental equations and maintains the required fundamental voltage. An objective function is derived from SHE problem to calculate switching angles. For the solution of objective function, optimization approach such as bio-inspired intelligent algorithms are used. In this paper, Genetic algorithm (GA), Particle Swarm Optimization (PSO) and Bee algorithm (BA) are used to determine the optimum switching angles for cascaded multilevel inverters to get low total harmonic distortion (THD) in output voltage. These computed angles are analyzed in MATLAB simulation model to authenticate the results. And there will be direct comparison among these algorithms.

关键词： Multilevel inverter total harmonic distortion bio-inspired algorithm Pulse width modulation

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Optimal path selection in Graded network using Artificial Bee Colony algorithm with Agent enabled Information

Optimal path selection in Graded network using Artificial Be...

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12th International Conference on Hybrid Intelligent Systems (HIS)

作者： Sooda, Kavitha Nair, T. R. Gopalakrishnan DSI RIIC Adv Networking Res Grp Bangalore 560064 Karnataka India Dayananda Sagar Inst Res & Ind Incubat Ctr KSA Adv Networking Res Grp VP ARAMCO ENDOWED Chair TechnolPMUKSA Bangalore 560078 Karnataka India

ISBN: (纸本)9781467351157;9781467351164

In this paper we propose a network aware approach for routing in graded network using Artificial Bee Colony (ABC) algorithm. ABC has been used as a good search process for optimality exploitation and exploration. The paper shows how ABC approach has been utilized for determining the optimal path based on bandwidth availability of the link and how it outperformed non graded network while deriving the optimal path. The selection of the nodes is based on the direction of the destination node also. This would help in narrowing down the number of nodes participating in routing. Here an agent system governs the collection of QoS parameters of the nodes. Also a quadrant is synthesized with centre as the source node. Based on the information of which quadrant the destination belongs, a search is performed. Among the many searches observed by the onlooker bees the best path is selected based on which onlooker bee comes back to source with information of the optimal path. The simulation result shows that the path convergence in graded network with ABC was 30% faster than non-graded ABC.

关键词： Keywords-Network Awareness Graded Network bio-inspired algorithm ABC Optimalpath

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