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Application of Nature-inspired algorithms for Optimising Photovoltaic System Energy Production 33

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Application of Nature-inspired Algorithms for Optimising Pho...

33rd Southern African Universities Power Engineering Conference, SAUPEC 2025

作者： Gwebu, Marcia Olukamni, Peter Mabunda, Nkateko University of Johannesburg Electrical and Electronic Engineeing Technology South Africa

ISBN: (纸本)9798331535162

Researchers have explored methods to maximize energy output from PV systems, with tilt and azimuth optimization being a significant area of focus. While some studies have proposed standard guidelines for tilt and azimuth based on regional differences, this project utilizes intelligent optimization algorithms to achieve optimal setting of these variables based on known mathematical model, to maximize energy production. Specifically, the study explores two nature-inspired algorithms, the Genetic Algorithm (GA) and Simulated Annealing (SA). Real-world data was obtained from South African University Radiometric Network (SAURAN) and residential PV system from Pretoria, Gauteng Province for the four seasons of the years 2023 to 2024. Performance was measured using metrics such as prediction accuracy, Standard Deviation (SD), percentage difference, Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Although both algorithms obtain tilt and azimuth settings that yield better energy production than actual production obtained from the recommended settings used in practice, the optimal settings produced by SA are more realistic (13-40°) compared to GA (30-80°). In terms of standard deviation, both algorithms exhibit high precision (low variability) across all seasons. GA exhibited a lower MAPE, indicating performance that is closer to what is already obtained in the system being studied. MAE values for both algorithms were relatively similar. Finally, sensitivity heat maps demonstrated that irradiance is more stable to variations in tilt and azimuth with SA compared to GA. Overall, this study provides valuable insights for designers and system owners, enabling them to determine the optimal tilt and azimuth angles for effective solar tracking, thereby maximizing energy output © 2025 IEEE.

关键词： optimization algorithms

Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO algorithms 13th

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Visual Explanations of Some Problematic Search Behaviors of ...

13th International Conference on Evolutionary Multi-Criterion optimization, EMO 2025

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： optimization algorithms

A Dynamic Mission Planning Method of Multi-Satellite Cooperative Observation for Highly Time-Sensitive Targets 15

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A Dynamic Mission Planning Method of Multi-Satellite Coopera...

15th Asia Conference on Mechanical and Aerospace Engineering, ACMAE 2024

作者： Jiaxin, Hu Demin, Zhao Yanwei, Zhu College of Aerospace Science National University of Defense Technology Changsha410000 China DFH Satellite Co. Ltd. Beijing10094 China

ISBN: (纸本)9781643685892

To address the challenges posed by highly time-sensitive targets with uncertainty and unpredictability in multi-satellite cooperative observation, conventional intelligent optimization algorithms often suffer from time-consuming and unstable issues. This paper presents a novel dynamic mission planning method that cleverly incorporates waiting time for tracking and tracked time as heuristics to guide task assignment. Furthermore, plannable and robustness indicators are introduced to facilitate global trade-offs in resource conflicts among multiple targets. Initially, the constraints specifically suitable for time-sensitive target observation are analyzed, followed by the modeling of the planning problem. Subsequently, a dynamic mission planning approach is proposed that utilizes dynamic priorities, offering a linear time solution for task assignment. Through simulation verification, the efficacy of the proposed method is demonstrated, showcasing its ability to enhance multi-satellite cooperative observation effectively. Additionally, it maximizes resource utilization in the face of diverse conflicts. © 2025 The Authors.

关键词： optimization algorithms

Distributed Forgetting-factor Regret-based Online optimization over Undirected Connected Networks

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arXiv 2025年

作者： Mo, Lipo Li, Jianjun Zuo, Min Wang, Lei Institute of Systems Science Beijing Wuzi University Beijing101126 China School of Mathematics and Statistics Beijing Technology and Business University Beijing100048 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

The evaluation of final-iteration tracking performance is a formidable obstacle in distributed online optimization algorithms. To address this issue, this paper proposes a novel evaluation metric named distributed forgetting-factor regret (DFFR). It incorporates a weight into the loss function at each iteration, which progressively reduces the weights of historical loss functions while enabling dynamic weights allocation across optimization horizon. Furthermore, we develop two distributed online optimization algorithms based on DFFR over undirected connected networks: the Distributed Online Gradient-free Algorithm for bandit-feedback problems and the Distributed Online Projection-free Algorithm for high-dimensional problems. Through theoretical analysis, we derive the upper bounds of DFFR for both algorithms and further prove that under mild conditions, DFFR either converges to zero or maintains a tight upper bound as iterations approach infinity. Experimental simulation demonstrates the effectiveness of the algorithms and the superior performance of DFFR. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

A TUTORIAL ON KRIGING-BASED STOCHASTIC SIMULATION optimization

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arXiv 2025年

作者： Amini, Sasan Van Nieuwenhuyse, Inneke Hasselt University Agoralaan Diepenbeek3590 Belgium

This tutorial focuses on kriging-based simulation optimization, emphasizing the importance of data efficiency in optimization problems involving expensive simulation models. It discusses how kriging models contribute to developing algorithms that minimize the number of required simulations, particularly in the presence of noisy evaluations. The tutorial compares the performance of kriging-based algorithms against traditional polynomial-based optimization methods using an illustrative example. Additionally, it discusses key extensions of kriging-based algorithms, including multi-objective and constrained optimization, providing insights into their application in complex, real-world settings. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms

An Analysis of Safety Guarantees in Multi-Task Bayesian optimization

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arXiv 2025年

作者： Lübsen, Jannis O. Eichler, Annika Institute of Control Systems Hamburg University of Technology Hamburg Germany Deutsches-Elektronen Synchrotron Hamburg Germany

In many practical scenarios of black box optimization, the objective function is subject to constraints that must be satisfied to avoid undesirable outcomes. Such constraints are typically unknown and must be learned during optimization. Safe Bayesian optimization aims to find the global optimum while ensuring that the constraints are satisfied with high probability. However, it is often sample-inefficient due to the small initial feasible set, which requires expansion by evaluating the objective or constraint functions, limiting its applicability to low-dimensional or inexpensive problems. To enhance sample efficiency, additional information from cheap simulations can be leveraged, albeit at the cost of safeness guarantees. This paper introduces a novel safe multi-task Bayesian optimization algorithm that integrates multiple tasks while maintaining high-probability safety. We derive robust uniform error bounds for the multi-task case and demonstrate the effectiveness of the approach on benchmark functions and a control problem. Our results show a significant improvement in sample efficiency, making the proposed method well-suited for expensive-to-evaluate functions. © 2025, CC BY-NC-ND.

关键词： optimization algorithms

Bidirectional Dijkstra's Algorithm is Instance-Optimal 8

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Bidirectional Dijkstra's Algorithm is Instance-Optimal

8th SIAM Symposium on Simplicity of algorithms, SOSA 2025

作者： Haeupler, Bernhard Hladík, Richard Rozhoň, Václav Tarjan, Robert E. Tětek, Jakub INSAIT Sofia University "St. Kliment Ohridski" & ETH Zurich Princeton University United States

ISBN: (纸本)9781611978315

While Dijkstra's algorithm has near-optimal time complexity for the problem of finding the shortest st-path, in practice, other algorithms are often superior on huge graphs. A prominent such example is the bidirectional search, which executes Dijkstra's algorithm from both endpoints in parallel and stops when these executions meet. In this paper, we give a strong theoretical justification for the use of such bidirectional search algorithms. We prove that for weighted multigraphs, both directed and undirected, a careful implementation of bidirectional search is instance-optimal with respect to the number of edges it explores. That is, we prove that no correct algorithm can outperform our implementation of bidirectional search on any single instance by more than a constant factor. For unweighted graphs, we show that bidirectional search is instace-optimal up to a factor of O(∆) where ∆ is the maximum degree of the graph. We also show that this is the best possible. Copyright © 2025 by SIAM.

关键词： optimization algorithms

Advancements in Bat algorithm 4th

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Advancements in Bat algorithm

4th International Conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Kaur, Arvinder Kumar, Yugal Sidhu, Jagpreet Department of Information Technology Chandigarh Engineering College-CGC Landran Punjab Mohali India Computer Engineering/STME NMIMS Chandigarh Campus India

ISBN: (纸本)9781032911571

The modern times have led to the adoption of distinctive meta-heuristic procedures for solving distinct class of optimization-problems. The meta-heuristics procedures have benefit above conventional algorithms because of strong capabilities of exploration and exploitation. In a similar vein, a Bat algorithm was created based on the echolocation habits of bats to address optimisation difficulties. Several advancements have been made in bat algorithm since then to achieve the state-of art results. The purpose of the study is to investigate bat algorithm for the various advancements done. Henceforth, the study presents the miniature analysis on advancements in bat algorithm. The bat algorithm applications in different areas are also investigated. © 2025 the Author(s).

关键词： optimization algorithms

Projected proximal gradient trust-region algorithm for nonsmooth optimization

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arXiv 2025年

作者： Dao, Minh N. Phan, Hung M. Roberts, Lindon School of Science RMIT University MelbourneVIC3000 Australia Department of Mathematics and Statistics Kennedy College of Sciences University of Massachusetts Lowell LowellMA01854 United States School of Mathematics and Statistics University of Sydney CamperdownNSW2006 Australia

We consider trust-region methods for solving optimization problems where the objective is the sum of a smooth, nonconvex function and a nonsmooth, convex regularizer. We extend the global convergence theory of such methods to include worst-case complexity bounds in the case of unbounded model Hessian growth, and introduce a new, simple nonsmooth trust-region subproblem solver based on combining several iterations of proximal gradient descent with a single projection into the trust region, which meets the sufficient descent requirements for algorithm convergence and has promising numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： optimization algorithms