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Adam-assisted quantum particle swarm optimization guided by length of potential well for numerical function optimization

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swarm AND EVOLUTIONARY COMPUTATION 2023年 79卷

作者： Wang, Caicheng Wang, Zili Zhang, Shuyou Tan, Jianrong Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310027 Peoples R China Zhejiang Univ Engn Res Ctr Design Engn & Digital Twin Zhejiang P Hangzhou 310027 Peoples R China

Quantum-inspired particle swarm optimization (QPSO) is a powerful computation technique that introduces quantum mechanics theory into particle swarm optimization (PSO), which has been successfully applied to address complex and challenging functional optimization problems. However, QPSO still suffers from premature convergence and trapping in the local optima. In this paper, we propose a novel QPSO variant by guiding it with the length of the potential well (LPW) and assisting it with Adam (abbreviated as Adam-LGQPSO). To select the appropriate strategy according to the evolutionary states, an LPW-guided particle update strategy is proposed, by introducing a Gaussian random vector for enhancing the information sharing among particles and endowing the swarm with adaptive randomness. Meanwhile, a particle perturbation mechanism is developed to avoid getting into a search dilemma. To balance the exploration and exploitation, a diversity function is introduced and a diversity-based LPW strategy is designed, which helps the algorithm to better adapt to the complex and variable search environment. Finally, the Adam algorithm is integrated into the QPSO to assist the initialization of the population. The effectiveness of the proposed strategies is testified by a set of experiments. With the extensive experiments on the CEC 2017 test suite, we also show that Adam-LGQPSO achieves superior performance compared to several state-of-art metaheuristics.

关键词： Quantum computation Numerical optimization swarm intelligence algorithms Particle swarm Optimization Guiding information

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Optimization of power take-off system settings and regional site selection procedure for a wave energy converter

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ENERGY CONVERSION AND MANAGEMENT-X 2024年 22卷

作者： Mehdipour, Hossein Amini, Erfan Naeeni, Seyed Taghi (Omid) Neshat, Mehdi Gandomi, Amir H. Univ Tehran Coll Engn Sch Civil Engn Tehran 14179 Iran Stevens Inst Technol Dept Civil Environm & Ocean Engn Hoboken NJ 07030 USA Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane QLD 4006 Australia Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

Ocean wave energy stands as a crucial component in the quest for sustainable and renewable energy sources, essential in the global effort to mitigate climate change. However, a significant challenge in this field is optimizing the efficiency of Wave Energy Converters (WECs) on a regional scale, particularly Oscillating Surge Wave Energy Converters (OSWECs). This challenge stems from the complex, nonlinear interactions between ocean waves and these devices, necessitating precise tuning of Power Take-Off (PTO) system settings and optimal placement for the highest possible performance and stability. To address this challenge, our study introduces the Hill Climb - Explorative Grey Wolf Optimizer (HC-EGWO), a novel algorithm combining local search and swarmbased global optimization strategies. This hybrid approach effectively balances exploration and exploitation in the solution space, leading to more optimal PTO settings for OSWECs. Alongside this algorithmic development, we conduct a thorough feasibility analysis based on the constraints of the flap's maximum angular motion. This ensures the optimized OSWEC operates within safe and efficient limits. In a comparative analysis with the Genetic Algorithm (GA), the Particle swarm Optimization (PSO), the artificial Gorilla Troops Optimizer (GTO), and different implementations of the GWO, our results show an improvement in power output, with the HC-EGWO method achieving up to a 3.31% increase over other variations of the GWO and 45% increase compared to all the methods. The findings of this study not only demonstrate the effectiveness of the HC-EGWO method but also provide strategic insights for the deployment of OSWECs in areas like the South Caspian Sea, where unique environmental factors imply careful consideration in the site selection process.

关键词： Ocean renewable energy Oscillating surge wave energy converter Power take-off optimization Site selection Meta-heuristic swarm intelligence algorithms

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A New Improved Multiple Behaviors Whale Optimization Algorithm 23

A New Improved Multiple Behaviors Whale Optimization Algorit...

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Proceedings of the 2023 7th International Conference on Natural Language Processing and Information Retrieval

作者： Yang Zhang Department of Computer Science Kanagawa University Japan

ISBN: (纸本)9798400709227

The Whale Optimization Algorithm (WOA) has demonstrated superior performance compared with other swarm intelligence algorithms, making it widely used and applied to practical problem-solving. In this paper, an enhanced WOA is proposed, referred to as the Improved Whale Optimization Algorithm (IWOA). The convergence factor of the traditional WOA is replaced with a random trigonometric function, which considers both the historical and current best positions of the whale population during the bubble-encircling search phase, thereby accelerating the global search speed of the whale population. In the random-encircling search phase, a combination of equiprobable random selection and roulette wheel selection is used to select random positions. Additionally, a random perturbation of individuals is introduced in the spiral search phase to prevent premature convergence to local optima. The effectiveness and robustness of the IWOA are evaluated using 4 benchmark function problems, and the results are compared with three other algorithms including particle swarm optimization (PSO). The experimental results demonstrate that the proposed IWOA outperforms other algorithms in terms of convergence speed and accuracy.

关键词： Benchmark Function Problems Optimization Problem swarm intelligence algorithms Whale Optimization Algorithm

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A survey on identification of influential users in social media networks using bio inspired algorithms

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Procedia Computer Science 2023年 218卷 2110-2122页

作者： Sebin Jose P. Victer Paul Indian Institute of Information Technology Kottayam 686576 India

There are 4.5 billion active social media users who are continuously generating very large amounts of social big data. This data can be a vital source for prescriptive data analytics as it contains various insights about the day-to-day involvements and activities of the user. Among the large number of user profiles in a social media network, it is critical to recognize the best nodes with maximum influentiality in order to propagate messages through them so as to reach maximum number of nodes. After identifying, this seed set of nodes can be used for effective marketing through the platform. Marketing messages which are propagated via social media networks is evaluated to be the most promising form of advertisement for any product. As in the case of any other advertising strategy, the marketing team will be looking for maximum reachability of the advertisement with minimum cost. They will select the influencers in such a way that the advertisement is propagated to the maximum nodes possible, but with only limited number of initial influencers. In this study we are evaluating the suitability of bio inspired algorithms to the influential node identification problem. We are considering a sub class of bio inspired algorithms called as ”swarm intelligence” algorithms as they exhibits the collective behaviour of an organized group of bio species analogous to a group of profiles in social networks. This study has attempted to compare 4 algorithms which were already used for predicting the seed-set of influential users. The aim is to identify the most suitable one for the target problem and improve the same in future works.

关键词： social media data analytics social big data tweet analysis influential user identification viral marketing bio inspired algorithms swarm intelligence algorithms Literature review Nature-inspired computing soft computing

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Harris Hawks Optimization: A Formal Analysis of Its Variants and Applications 13th

Harris Hawks Optimization: A Formal Analysis of Its Variants...

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13th International Joint Conference on Computational intelligence (IJCCI) / 13th International Conference on Evolutionary Computation Theory and Applications (ECTA)

作者： Abu Khurma, Ruba Aljarah, Ibrahim Castillo, Pedro A. Univ Jordan King Abdullah 2 Sch Informat Technol Amman Jordan Univ Granada Dept Comp Architecture & Comp Technol ETSIIT Granada Spain Univ Granada CITIC Granada Spain

ISBN: (纸本)9789897585340

The Harris Hawks Optimization (HHO) is a recent meta-heuristic algorithm developed by Hideri in 2019. HHO algorithm has been widely utilized to solve many optimization problems in different fields. The primary objective of HHO is to define a fitness function that can successfully optimize a specific problem by finding the minimum or maximum value. This survey presents a thorough study of the algorithm, including its variants such as binary, hybridization, multi-objective and modifications. It highlights the main applications such as medical, engineering, machine learning, and network applications. Finally, the conclusion summarizes the current works on HHO and suggests possible future directions.

关键词： Harris Hawcks Optimization Algorithm HHO Meta-heuristic Optimization swarm intelligence algorithms

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Designing a sustainable supply chain network integrated with vehicle routing: A comparison of hybrid swarm intelligence metaheuristics

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COMPUTERS & OPERATIONS RESEARCH 2019年 110卷 220-235页

作者： Govindan, Kannan Jafarian, Ahmad Nourbakhsh, Vahid Univ Southern Denmark Ctr Sustainable Supply Chain Engn Dept Technol & Innovat Odense Denmark ATU Fac Management & Accounting Tehran Iran Univ Calif Irvine Paul Merage Sch Business Irvine CA 92697 USA

Recently, a growing concern with sustainability has become a consideration in business operations. However, there is a lack of mathematical models that quantify environmental effects and, in particular, social impacts of supply chains because of the inherently subjective nature of these aspects. To fill this gap, this paper models a distribution network in which the triple bottom lines of sustainability are captured. Different impacts of the network on the stakeholders, including company owners, workers, consumers and society, are considered as whole. In the current model, a multi-product vehicle routing problem with time windows (MPVRPTW) as an operational decision is integrated with strategic decisions related to the network design. To solve this model, three hybrid swarm intelligence techniques (particle swarm optimization (PSO), electromagnetism mechanism algorithm (EMA), and artificial bee colony (ABC)) are proposed, and each is hybridized with variable neighborhood search (VNS) are proposed. Because metaheuristic methods are sensitive to input parameters, response surface methodology (RSM) with the multi-objective decision making (MODM) approach is applied for tuning the parameters. The proposed approaches are compared with the hybrid of genetic algorithm (GA) and VNS as the benchmark algorithm. A fair comparison is conducted by employing six metrics to evaluate the quality of the Pareto frontier obtained by the algorithms on the test problems. According to the results, the predominance of EMA is enhanced by VNS local search. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Sustainable supply chain network Environmental management Corporate social responsibility Multiple-objective optimization Vehicle routing problem swarm intelligence algorithms

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Discrete tree seed algorithm for urban land readjustment

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ENGINEERING APPLICATIONS OF ARTIFICIAL intelligence 2022年 112卷 104783-104783页

作者： Koc, Ismail Atay, Yilmaz Babaoglu, Ismail Konya Tech Univ Dept Software Engn TR-42075 Konya Turkey Gazi Univ Dept Comp Engn TR-06570 Ankara Turkey Konya Tech Univ Dept Comp Engn TR-42075 Konya Turkey

Land readjustment and redistribution (LR) is an important approach used to realize development plans by converting rural lands to urban land and also providing urban infrastructure. The LR problem, which is a complex challenging real-world problem, is a discrete optimization problem because its structure is similar to TSP (Traveling Salesman Problem) and scheduling problems which are combinatorial optimization problems. Since classical mathematical methods are insufficient for solving NP (Nondeterministic Polynomial) optimization problems due to time limitations, meta-heuristic optimization algorithms are commonly utilized for solving these kinds of problems. In this paper, meta-heuristic algorithms including genetic, particle swarm, differential evolution, artificial bee, and tree seed algorithms are utilized for solving LR problems. The stated meta-heuristic algorithms are used by applying spatial-based crossover and mutation operators depending upon the LR problem on each algorithm. Moreover, a synthetic dataset is used to ensure that the quality of the solution obtained is acceptable to everyone, to prove an optimal solution easily. By utilizing the suggested spatial-based crossover and mutation operators, finding the ideal solution is aimed using the synthetic dataset. In addition, five different modifications on TSA (Tree-Seed Algorithm) are performed and used to solve LR problems. All the modified versions of TSA are carried out only by changing the mechanism of seed reproduction. The novel TSA approaches are respectively named as tcTSA (tournament current), tbTSA (tournament best), pbTSA (personal-best based), t2TSA (double tournament), and elTSA (elitism based). In the experimental studies, the hybrid approach, which includes the crossover and mutation operators, is successfully applied in all of the algorithms under equal conditions for a fair comparison. According to experimental results performed using the dataset, it can be clearly stated that especiall

关键词： swarm intelligence algorithms Evolutionary algorithms Hybrid approach Spatial-based crossover and mutation operators Efficient TSA Urban land readjustment

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Design on a wireless mechanomyography acquisition equipment and feature selection for lower limb motion recognition

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Zhang, Hanyang Wang, Xinping Zhang, Yue Cao, Gangsheng Xia, Chunming East China Univ Sci & Technol Dept Mech Engn Shanghai 200237 Peoples R China Shanghai Univ Engn Sci Sch Mech & Automot Engn Shanghai 201620 Peoples R China

Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption.

关键词： Mechanomyography Wireless acquisition Feature selection swarm intelligence algorithms Pattern recognition

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Adaptive Tuning of Power System Stabilizer Using a Damping Control Strategy Considering Stochastic Time Delay

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IEEE ACCESS 2020年 8卷 124254-124264页

作者： Lala, Jose Antonio Oscullo Gallardo, Carlos Fabian Natl Polytech Sch Fac Elect & Elect Elect Engn Dept Quito 170525 Ecuador

The operation of a modern electrical system presents complex operating conditions, and uncertainty factors increased by the presence of variable and flexible loads. This stochastic operating point variation can promote low-frequency oscillations, and like the conventional power system stabilizer (PSS) features static tuning, it does not provide sufficient damping. Today, an electrical system is monitored through online measurements from the wide-area measurement system (WAMS);moreover, it enables stochastic dynamics to be tracked as the remote signals of WAMS system, the time delay due to the communication channel. It is interesting to analyze the incorporation of the PSS of signals from the WAMS without the time delay effect that degrades the small-signal stability (SSS) and, in turn, allows adaptive tuning. Through two stages, at first, the subspace of the operating point is determined based on a distribution of consumption levels. Each operating point considered a random time delay. For each subspace, establish a bank of tuned PSSs. Second, through the optimal classification and the regression decision tree (CART), the classification rules for the subspace are determined, allowing classifying the measurements of WAMS. The proposed methodology is applied to the New England of 66-bus system and Ecuadorian electric system to demonstrate that the adaptive tuning of PSSs significantly improves SSS under different scenarios and is probabilistic robust, considering the time delay.

关键词： Stochastic power system operating power oscillations adaptive damping control swarm intelligence algorithms wide area damping controller

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Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

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APPLIED SOFT COMPUTING 2020年 92卷 106234-106234页

作者： Nagaballi, Srinivas Kale, Vijay S. Visvesvaraya Natl Inst Technol Elect Engn Dept Nagpur 440010 Maharashtra India

This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS's are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optima

关键词： swarm intelligence algorithms Improved raven roosting optimization Distributed generation Radial distribution system Technical and economical issues Game theory Pareto optimality

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