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Adequacy Evaluation of Composite Power Systems Using an evolutionary Swarm Algorithm

IEEE ACCESS 2022年 10卷 19732-19741页

作者： Amarasinghe, P. A. Gihan M. Abeygunawardane, Saranga K. Singh, Chanan Univ Moratuwa Dept Elect Engn Moratuwa 10400 Sri Lanka Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

The generation and transmission capacities of many power systems in the world are significantly increasing due to the escalating global electricity demand. Therefore, the adequacy evaluation of power systems has become a computationally challenging and time-consuming task. Recently, population-based intelligent search methods such as Genetic algorithms (GAs) and Binary Particle Swarm Optimization (BPSO) have been successfully employed for evaluating the adequacy of power generation systems. In this work, the authors propose a novel evolutionary Swarm Algorithm (ESA) for the adequacy evaluation of composite generation and transmission systems. The random search guiding mechanism of the ESA is based on the underlying philosophies of GAs and BPSO. The main objective of the ESA is to find out the most probable system failure states that significantly affect the adequacy of composite systems. The identified system failure states can be directly used to estimate the system adequacy indices. The proposed ESA-based framework is used to evaluate the adequacy of the IEEE Reliability Test System (RTS). The estimated annualized and annual adequacy indices such as Probability of Load Curtailments (PLC), Expected Duration of Load Curtailments (EDLC), Expected Energy Not Supplied (EENS) and Expected Frequency of Load Curtailments (EFLC) are compared with those obtained using Sequential Monte Carlo Simulation (SMCS), GA and BPSO. The results show that the accuracy, computational efficiency, convergence characteristics, and precision of the ESA outperform those of GA and BPSO. Moreover, compared to SMCS, the ESA can estimate the adequacy indices in a more time-efficient manner.

关键词： Genetic algorithms Power systems Statistics Sociology Reliability Power system reliability Interconnected systems Composite system adequacy evolutionary algorithms genetic algorithms particle swarm optimization population-based methods reliability assessment

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An effective and efficient evolutionary algorithm for many-objective optimization

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INFORMATION SCIENCES 2022年 617卷 211-233页

作者： Xue, Yani Li, Miqing Liu, Xiaohui Brunel Univ London Dept Comp Sci Uxbridge UB8 3PH Middlesex England Univ Birmingham Sch Comp Sci CERCIA Birmingham B15 2TT England

In evolutionary multiobjective optimization, effectiveness refers to how an evolutionary algorithm performs in terms of converging its solutions into the Pareto front and also diversifying them over the front. This is not an easy job, particularly for optimization prob-lems with more than three objectives, dubbed many-objective optimization problems. In such problems, classic Pareto-based algorithms fail to provide sufficient selection pressure towards the Pareto front, whilst recently developed algorithms, such as decomposition -based ones, may struggle to maintain a set of well-distributed solutions on certain prob-lems (e.g., those with irregular Pareto fronts). Another issue in some many-objective opti-mizers is rapidly increasing computational requirement with the number of objectives, such as hypervolume-based algorithms and shift-based density estimation (SDE) methods. In this paper, we aim to address this problem and develop an effective and efficient evolu-tionary algorithm (E3A) that can handle various many-objective problems. In E3A, inspired by SDE, a novel population maintenance method is proposed to select high-quality solu-tions in the environmental selection procedure. We conduct extensive experiments and show that E3A performs better than 11 state-of-the-art many-objective evolutionary algo-rithms in quickly finding a set of well-converged and well-diversified solutions.(c) 2022 Elsevier Inc. All rights reserved.

关键词： evolutionary algorithms Many-objective optimization Effectiveness Efficiency

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A dual-population constrained multi-objective evolutionary algorithm with a feasible archive set

A dual-population constrained multi-objective evolutionary a...

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2023 International Conference on Algorithm, Imaging Processing, and Machine Vision, AIPMV 2023

作者： Yu, Xinchang Wang, Yumeng Zhang, Tong Xu, Huaqing School of Physics and Electronics Shandong Normal University Jinan China

ISBN: (纸本)9781510672444

Continuous updating and maintenance of feasible solutions is crucial when solving constrained multi-objective optimization problems (CMOPs). However, most existing constrained multi-objective evolutionary algorithms (CMOEAs) are not efficient enough in updating and preserving competitive feasible solutions, thus reducing population diversity. To address this issue, this paper proposes a dual-population (i.e., mainPop and auxPop) constrained multi-objective evolutionary algorithm with a feasible archive set for CMOPs, named DPFAS. The two populations have different functions in the algorithm. Specifically, the mainPop considers both objectives and constraints for solving the original CMOPs, while the auxPop is used only for the optimization of objectives without considering constraints. In addition, a feasible archive set is used to store feasible solutions that are competitive in the auxPop and provide useful information for the mainPop . Moreover, a fitness assignment strategy is designed to speed up the algorithm’s convergence. Particularly, the population converges faster by selecting better-nondominated solutions into the matching pool. Finally, experimental studies on 23 benchmark functions show that the proposed algorithm was more competitive compared with five state-of-the-art CMOEAs. © 2024 SPIE.

关键词： evolutionary algorithms

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Multimodal Lotus Effect Algorithm for Engineering Optimization Problems

ENGINEERING REPORTS

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ENGINEERING REPORTS 2025年第4期7卷

作者： Dalirinia, Elham Yaghoobi, Mahdi Tabatabaee, Hamid Chandna, Swati Jalali, Mehrdad Islamic Azad Univ Dept Comp Engn Ma C Mashhad Iran Islamic Azad Univ Intelligent Financial Innovat Res Ctr Dept Artificial Intelligent & Data Sci Ma C Mashhad Iran Islamic Azad Univ Dept Elect Engn Ma C Mashhad Iran SRH Univ Appl Sci Heidelberg Dept Informat Heidelberg Germany Karlsruhe Inst Technol KIT Inst Funct Interfaces Eggenstein Leopoldshafen Germany

Multimodal optimization problems (MMOPs) are critical in fields like game theory and robotics, where identifying multiple optimal solutions simultaneously is essential, yet challenging due to the need for effective global exploration and precise localization of optima. This study introduces the multimodal lotus effect algorithm (M-LEA), a novel extension of our previously published lotus effect optimization algorithm (LEA), which was designed for single-modal optimization and thus struggled to maintain multiple optima in complex multimodal spaces. M-LEA addresses this limitation by incorporating a roaming technique with independently evolving subpopulations, enabling it to navigate multimodal spaces without requiring parameters such as radius or prior information about the number or distribution of optima. Its robustness is demonstrated through comparisons with five algorithms on the IEEE CEC2013-2015 challenge, where M-LEA consistently outperformed competitors. The algorithm's practical utility is further validated in two applications: identifying Nash equilibrium points in game theory and localizing resources via robotic systems. Results show that M-LEA achieves superior performance and stability, making it well-suited for scenarios demanding high efficiency and precision. These findings highlight M-LEA's potential for diverse domains, paving the way for its application in game theory, robotics, and other fields requiring advanced multimodal optimization techniques.

关键词： evolutionary algorithms glowworm swarm algorithm lotus effect algorithm M-LEA multimodal optimization Nash equilibrium resource identification

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Evolving Multimodal Robot Behavior via Many Stepping Stones with the Combinatorial Multiobjective evolutionary Algorithm

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evolutionary COMPUTATION 2022年第2期30卷 131-164页

作者： Huizinga, Joost Clune, Jeff OpenAI San Francisco CA 94110 USA Univ British Columbia Vancouver BC V6T 1Z4 Canada

An important challenge in reinforcement learning is to solve multimodal problems, where agents have to act in qualitatively different ways depending on the circumstances. Because multimodal problems are often too difficult to solve directly, it is often helpful to define a curriculum, which is an ordered set of subtasks that can serve as the stepping stones for solving the overall problem. Unfortunately, choosing an effective ordering for these subtasks is difficult, and a poor ordering can reduce the performance of the learning process. Here, we provide a thorough introduction and investigation of the Combinatorial Multiobjective evolutionary Algorithm (CMOEA), which allows all combinations of subtasks to be explored simultaneously. We compare CMOEA against three algorithms that can similarly optimize on multiple subtasks simultaneously: NSGA-II, NSGA-III, and epsilon -Lexicase Selection. The algorithms are tested on a function-optimization problem with two subtasks, a simulated multimodal robot locomotion problem with six subtasks, and a simulated robot maze-navigation problem where a hundred random mazes are treated as subtasks. On these problems, CMOEA either outperforms or is competitive with the controls. As a separate contribution, we show that adding a linear combination over all objectives can improve the ability of the control algorithms to solve these multimodal problems. Lastly, we show that CMOEA can leverage auxiliary objectives more effectively than the controls on the multimodal locomotion task. In general, our experiments suggest that CMOEA is a promising algorithm for solving multimodal problems.

关键词： Many-objective optimization evolutionary multiobjective optimization multimodal problems evolutionary algorithms robotics stepping stones

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A Modified evolutionary Algorithm for Generating the Cycling Training Routes

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IEEE ACCESS 2022年 10卷 109743-109759页

作者： Rajsp, Alen Fister, Iztok, Jr. Univ Maribor Fac Elect Engn & Comp Sci Maribor 2000 Slovenia

We present a novel method for generating cycling training routes from geographical property graphs based on an evolutionary Algorithm. The algorithm operators of crossover and mutation are adjusted for use in the Property Graph domain. Data fusion of geographical data from the OpenStreetMap, EU-DEM digital surface model, and existing training records is performed as a basis of the intersections-paths property graph. The proposed approach allows route generation based on their starting and ending points in the property graph and their distance and ascent. A property graph of all intersections and cycling roads is shown and generated for the regions of Podravje and Pomurje. The property graph used in the proposed algorithm's feasibility demonstration is shown. This is done by presenting four different cases of routes generated with our algorithm. The algorithm allows for generating classic cycling routes of A to B nature, routes with more than two fixed points, and cyclical training routes. The research is concluded by offering further directions on route generation research.

关键词： Training Sports Heart rate Prediction algorithms Predictive models Data mining Physiology evolutionary algorithms Data mining sports training automatic generation of sports training evolutionary algorithms

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ABC-DE hybrid evolutionary algorithm based on individual preference population selection 5

ABC-DE hybrid evolutionary algorithm based on individual pre...

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5th International Conference on Computer Communication and Network Security, CCNS 2024

作者： Song, XiaoYu Li, Wen Zhao, Ming Shenyang China Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang China Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (纸本)9781510681842

To fully leverage the complementary advantages of the Artificial Bee Colony (ABC) and Differential Evolution (DE) algorithms for various optimization problems, this paper introduces an ABC-DE hybrid evolutionary algorithm based on individual preference for population selection. This method divides the population into subpopulations using a preference-based selection mechanism and employs ABC and DE for diverse subpopulation search strategies. Additionally, it implements a new resource allocation mechanism that increases computational efforts for promising solutions. Comparative analysis on the CEC2014 benchmark suite with five other current ABC and DE improved algorithms confirms that the proposed hybrid evolutionary algorithm effectively enhances convergence speed, accuracy, and robustness. © 2024 SPIE.

关键词： evolutionary algorithms Chromium Analytical research Mathematical optimization evolutionary optimization Statistical analysis Algorithm development Chemical elements Lithium Reflection

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An enhanced slime mould algorithm with triple strategy for engineering design optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第6期11卷 36-74页

作者： Wang, Shuai Zhang, Junxing Li, Shaobo Wu, Fengbin Li, Shaoyang Guizhou Univ State Key Lab Publ Big Data Guiyang 550025 Peoples R China Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Peoples R China Guizhou Inst Technol Guiyang 550025 Peoples R China Guizhou Univ Sch Mech Engn Guiyang 550025 Peoples R China

This paper introduces an enhanced slime mould algorithm (EESMA) designed to address critical challenges in engineering design optimization. The EESMA integrates three novel strategies: the Laplace logistic sine map technique, the adaptive t-distribution elite mutation mechanism, and the ranking-based dynamic learning strategy. These enhancements collectively improve the algorithm's search efficiency, mitigate convergence to local optima, and bolster robustness in complex optimization tasks. The proposed EESMA demonstrates significant advantages over many conventional optimization algorithms and performs on par with, or even surpasses, several advanced algorithms in benchmark tests. Its superior performance is validated through extensive evaluations on diverse test sets, including IEEE CEC2014, IEEE CEC2020, and IEEE CEC2022, and its successful application in six distinct engineering problems. Notably, EESMA excels in solving economic load dispatch problems, highlighting its capability to tackle challenging optimization scenarios. The results affirm that EESMA is a competitive and effective tool for addressing complex optimization issues, showcasing its potential for widespread application in engineering and beyond.

关键词： slime mould algorithm adaptive t-distribution elite mutation strategy ranking-based dynamic learning strategy mechanical engineering design optimization evolutionary algorithms

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A Novel Ensemble Machine Learning and an evolutionary Algorithm in Modeling the COVID-19 Epidemic and Optimizing Government Policies

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第10期52卷 6362-6372页

作者： Tayarani-Najaran, Mohammad-Hassan Univ Hertfordshire Sch Comp Sci Hatfield AL10 9AB Herts England

The spread of the COVID-19 disease has prompted a need for immediate reaction by governments to curb the pandemic. Many countries have adopted different policies and studies are performed to understand the effect of each of the policies on the growth rate of the infected cases. In this article, the data about the policies taken by all countries at each date, and the effect of the policies on the growth rate of the pandemic are used to build a model of the pandemic's behavior. The model takes as input a set of policies and predicts the growth rate of the pandemic. Then, a population-based multiobjective optimization algorithm is developed, which uses the model to search through the policy space and finds a set of policies that minimize the cost induced to the society due to the policies and the growth rate of the pandemic. Because of the complexity of the modeling problem and the uncertainty in measuring the growth rate of the pandemic via the models, an ensemble learning algorithm is proposed in this article to improve the performance of individual learning algorithms. The ensemble consists of ten learning algorithms and a metamodel algorithm that is built to predict the accuracy of each learning algorithm for a given data record. The metamodel is a set of support vector machine (SVM) algorithms that is used in the aggregation phase of the ensemble algorithm. Because there is uncertainty in measuring the growth rate via the models, a landscape smoothing operator is proposed in the optimization process, which aims at reducing uncertainty. The algorithm is tested on open access data online and experiments on the ensemble learning and the policy optimization algorithms are performed.

关键词： Prediction algorithms Pandemics Uncertainty Optimization Predictive models COVID-19 Neural networks Ensemble learning epidemiology COVID-19 evolutionary algorithms optimization policy making

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Integrated Whale Swarm and Neuro-evolutionary Computing for Large-Scale Sparse Optimization Problems 2nd

Integrated Whale Swarm and Neuro-Evolutionary Computing for ...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Balakrishnan, Biju Shanthini, B. Amudha, R. St. Peter’s Institute of Higher Education and Research Avadi Tamilnadu Chennai India Hindusthan College of Engineering and Technology Tamilnadu Coimbatore India

ISBN: (纸本)9789819995233

Sparse optimization problems at a large scale present considerable difficulties in diverse fields, such as machine learning, data mining, and signal processing. The aim is to identify the most efficient solutions within expansive search spaces, while constraining the number of non-zero variables. The present study aims to tackle this obstacle by introducing an innovative methodology that integrates whale swarm optimization (WSO) with neuro-evolutionary computing (NEC). The WSO algorithm draws inspiration from the social behavior of humpback whales and presents a proficient approach for investigating and utilizing the search space. The integration of evolutionary algorithms with neural networks by NEC has demonstrated potential in effectively addressing intricate problems. Through the simultaneous evolution of neural networks and optimization processes, the algorithm is capable of adapting and enhancing its search strategy progressively. The study aims to improve the efficiency of WSO in addressing optimization problems characterized by sparsity at a significant scale by incorporating techniques from neuro-evolutionary computing. The method under consideration entails the integration of neural networks into the WSO algorithm with the aim of directing and augmenting the exploration procedure. The study evaluates the efficacy of the hybrid algorithm in relation to established methodologies based on criteria such as the quality of solutions, rate of convergence, and computational efficiency. The results obtained from the experiment provide evidence to support the efficacy of the proposed approach. The hybrid algorithm effectively utilizes the sparsity structure inherent in the problems, resulting in expedited convergence and enhanced precision of the solutions. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： evolutionary algorithms

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