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Study on Economic Dispatch of the Combined Cooling Heating and Power Microgrid Based on Improved Sparrow Search algorithm

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ENERGIES 2022年第14期15卷 5174-5174页

作者： Qiao, Mengmeng Yu, Zexu Dou, Zhenhai Wang, Yuanyuan Zhao, Ye Xie, Ruishuo Liu, Lianxin Shandong Univ Technol Sch Elect & Elect Engn Zibo 255000 Peoples R China

The reasonable and efficient use of the abundant biomass resources in rural areas has not been realized. Therefore, the concept of a combined cooling, heating, and power (CCHP) microgrid system, considering biomass pyrolysis and gasification, has been developed by researchers. A biomass gasification device can fully use biomass resources and can play a role in absorbing wind energy. Meanwhile, in order to minimize the operating cost of each micropower supply unit, as well as the environmental pollution costs, researchers have also established an optimal scheduling model for CCHP microgrids, which uses the sparrow search algorithm. In this paper, we have improved upon the traditional sparrow algorithm to solve the problems of its uneven population distribution, poor global search ability, and the risk of falling into local optima, through the development of the random walk sparrow search algorithm (RSSA). First, a sinusoidal chaotic map is used to generate the early-generation sparrow population with a uniform distribution in space. Second, in this study we add a sharing factor to the discoverer's optimization process to enhance information sharing and the global research capability among individuals in this field. Finally, a random walk strategy is used to form new participants to improve the algorithm's skill in locally searching for optimal locations. Taking the CCHP microgrid with grid-connected action as a case study, we concluded that compared with the optimization outcomes of the SSA, the total costs incurred by RSSA in summer and winter were reduced by 2.2% and 3.1%, respectively. Compared with the optimization findings for the chaotic SSA algorithm, the total costs incurred using the RSSA algorithm under typical summer and winter days were reduced by 0.14% and 0.13%, respectively. The productiveness of the RSSA algorithm for solving the CCHP microgrid economic dispatch issues has thus been verified.

关键词： microgrids power generation dispatch combined cooling heating and power (CCHP) system swarm intelligence algorithm optimization

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Metaheuristics for the positioning of 3D objects based on image analysis of complementary 2D photographs

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MACHINE VISION AND APPLICATIONS 2021年第5期32卷 105-105页

作者： Flori, Arnaud Oulhadj, Hamouche Siarry, Patrick Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France

Today, advances in 3D modeling make it possible to identically reproduce objects, animals, humans and even entire scenes. The broad applications concern video games, virtual reality or augmented reality and cinema, for example. In this article, we propose a new method to build a 3D scene directly from several complementary photographs. The positions of the objects for which we already have a 3D model will be determined by triangulation, thanks to the information extracted from the photographs, such as the outline of the objects on the images. Each pixel of the images is converted into a value that gives its distance to the nearest outline. The 3D model of the objects is then projected on the converted images, and the triangulation is done using a cost function that gives the distance of each projection of the objects to their respective outlines. A projection is considered perfect when its distance to its outlines is null, which means that the cost function gives a score of zero as well. We propose to solve this optimization problem by means of two algorithms, namely Simulated Annealing (SA) and quantum particle swarm optimization (QUAPSO).

关键词： Simulated Annealing Particle swarm optimization swarm intelligence algorithm Auto-adaptive algorithm 3D model

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A novel Discrete Artificial Bee Colony algorithm combined with adaptive filtering to extract Fetal Electrocardiogram signals

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 247卷

作者： Chai, Qing-Wei Kong, Lingping Pan, Jeng-Shyang Zheng, Wei-Min Shandong Univ Sci & Technol Shandong 266590 Peoples R China VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava Czech Republic

The Fetal Electrocardiogram (FECG) signal plays a crucial role in monitoring the health of the fetus, but there are numerous challenges in eliminating the maternal thorax signal and reducing noise interference. This paper proposes a novel objective function that combines a Least Mean Squares (LMS) adaptive filter with a heuristic algorithms to enhance the quality of the extracted FECG signal. To achieve better results, we introduce the Discrete Artificial Bee Colony (DABC) algorithm with a new initialization strategy, a random wavelet basic function strategy, and Gaussian distribution. These improvements enhance global search capabilities and ensure a faster convergence rate. The application of heuristic algorithms can reduce noise signals and provides clearer and more accurate results compared to the traditional LMS filter. Furthermore, the effectiveness of this innovative algorithm is compared with other widely used heuristic algorithms. The experiment results demonstrate that the novel algorithm significantly enhances performance by up to 8% compared to other conventional extraction methods in some indicators.

关键词： Heuristic algorithms Artificial Bee Colony swarm intelligence algorithm Signal extraction

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An improved grey wolf optimizer for solving engineering problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 166卷 113917-113917页

作者： Nadimi-Shahraki, Mohammad H. Taghian, Shokooh Mirjalili, Seyedali Islamic Azad Univ Najafabad Branch Fac Comp Engn Najafabad Iran Islamic Azad Univ Najafabad Branch Big Data Res Ctr Najafabad Iran Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Adelaide SA Australia Yonsei Univ Yonsei Frontier Lab Seoul South Korea

In this article, an Improved Grey Wolf Optimizer (I-GWO) is proposed for solving global optimization and engineering design problems. This improvement is proposed to alleviate the lack of population diversity, the imbalance between the exploitation and exploration, and premature convergence of the GWO algorithm. The IGWO algorithm benefits from a new movement strategy named dimension learning-based hunting (DLH) search strategy inherited from the individual hunting behavior of wolves in nature. DLH uses a different approach to construct a neighborhood for each wolf in which the neighboring information can be shared between wolves. This dimension learning used in the DLH search strategy enhances the balance between local and global search and maintains diversity. The performance of the proposed I-GWO algorithm is evaluated on the CEC 2018 benchmark suite and four engineering problems. In all experiments, I-GWO is compared with six other state-ofthe-art metaheuristics. The results are also analyzed by Friedman and MAE statistical tests. The experimental results and statistical tests demonstrate that the I-GWO algorithm is very competitive and often superior compared to the algorithms used in the experiments. The results of the proposed algorithm on the engineering design problems demonstrate its efficiency and applicability.

关键词： Optimization Metaheuristic swarm intelligence algorithm Grey wolf optimizer Improved grey wolf optimizer Engineering optimization problems algorithm Artificial intelligence

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A Mayfly algorithm for cardinality constrained portfolio optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期230卷

作者： Zheng, Xuanyu Zhang, Changsheng Zhang, Bin Northeastern Univ Shenyang 110819 Peoples R China Shenyang Inst Financial Informat Ind Technol Shenyang 110167 Peoples R China

Portfolio optimization is an essential issue in quantitative investing, which aims to find the best set of portfolios by allocating the proportion of assets. One of the most widely studied portfolio optimization models is the cardinality constrained mean-variance model, which incorporates real-world constraints on the number of selected assets and lower and upper bounds on the proportion of each asset. This paper presents a novel metaheuristic algorithm based on the Mayfly algorithm to solve the cardinality constrained mean-variance portfolio optimization problem. To better adapt to this problem, we design and introduce some new features to the proposed algorithm, including (1) a new cardinality constraint handling strategy;(2) a new local search strategy;and (3) changes to the crossover operator. We have designed comparison experiments for the proposed metaheuristic and evaluated its performance using five commonly used performance metrics. The experimental results show that the proposed approach achieves competitive performance on datasets of different sizes. The results also demonstrate the feasibility of this approach in solving the cardinality constrained mean-variance portfolio optimization problem.

关键词： Mayfly algorithm Portfolio optimization Cardinality constraint swarm intelligence algorithm

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An evolutionary crow search algorithm equipped with interactive memory mechanism to optimize artificial neural network for disease diagnosis

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年 90卷

作者： Zamani, Hoda Nadimi-Shahraki, Mohammad H. Islamic Azad Univ Fac Comp Engn Najafabad Branch Najafabad *** Iran Islamic Azad Univ Big Data Res Ctr Najafabad Branch Najafabad *** Iran

Artificial neural network (ANN) is an information processing paradigm that loosely models the thinking patterns of the human brain with specifications such as real-time learning, self-adaption, and self -organization. The learning process of ANNs is complex and tackles shortcomings such as a slow convergence rate, learning timeconsuming, and local minimum trapping, especially when using gradient -based optimization techniques. Although many metaheuristics have been proposed to arm ANN and solve these weaknesses, ANN learning still needs solution quality. Therefore, this study proposes an evolutionary crow search algorithm (ECSA) to optimize the hyperparameters of ANNs for diagnosing chronic diseases. ECSA introduces an evolutionary search strategy, self -adaptive adaptive flight length, and an interactive memory mechanism to alleviate the canonical crow search algorithm's shortcomings. The evolutionary search strategy and self -adaptive flight length provide a meaningful search strategy in which crows effectively explore and exploit problem spaces by maintaining population diversity. During the search process, the interactive memory mechanism records the best solution obtained during optimization. The performance of ECSA was evaluated and compared with well-known metaheuristic algorithms in terms of local and global search abilities, local optima avoidance, and convergence speed towards the promising area. Then, the results were statistically analyzed. Ultimately, the effectiveness of an adaptation of ECSA named ECSA-MLP for optimizing hyperparameters of the multilayer perceptron network for diagnosing diseases including coronavirus, breast cancer, diabetes, cardiovascular, cervical cancer, Parkinson's, mammography, and acute inflammation was compared with state-of-the-art competitor algorithms. The experimental results indicated the superiority of ECSA over competitor algorithms in optimizing the network.

关键词： Artificial Neural Networks Multilayer perceptron network Hyperparameters Metaheuristic algorithm swarm intelligence algorithm Evolutionary optimization algorithms

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Multi-strategy firefly algorithm with selective ensemble for complex engineering optimization problems

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APPLIED SOFT COMPUTING 2022年第0期120卷 108634-108634页

作者： Peng, Hu Xiao, Wenhui Han, Yupeng Jiang, Aiwen Xu, Zhenzhen Li, Mengmeng Wu, Zhijian Jiujiang Univ Sch Comp & Big Data Sci Jiujiang 332005 Peoples R China Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. Firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size alpha. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems. (c) 2022 Elsevier B.V. All rights reserved.

关键词： swarm intelligence algorithm Firefly algorithm Multi-strategy Selective ensemble Complex engineering optimization problems

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Improvement of the Firework algorithm for Classification Problems

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Journal of Cyber Security 2020年第4期2卷 191-196页

作者： Yu Xue Sow Alpha Amadou Yan Zhao School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China

Attracted numerous analysts’consideration,classification is one of the primary issues in Machine *** evolutionary algorithms(EAs)were utilized to improve their global search *** the previous years,many scientists have attempted to tackle this issue,yet regardless of the endeavors,there are still a few *** on solving the classification problem,this paper introduces a new optimization classification model,which can be applied to the majority of evolutionary computing(EC)*** algorithm(FWA)is one of the EC methods,Although the Firework algorithm(FWA)is a proficient algorithm for solving complex optimization *** proficient of the FWA isn't fulfilled when being utilized for solving the classification *** this paper we previously proposed optimization classification model according to the classification *** that point we legitimately utilize the model with FWA to solve the classification ***,to investigate the performance of our model,we select 4 datasets in the experiments,and the results indicate that an improved FWA can upgrade the classification accuracy by using this model.

关键词： swarm intelligence algorithm firework algorithm evolutionary algorithm classification

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An Improved Multi-Objective Quantum-Behaved Particle swarm Optimization for Railway Freight Transportation Routing Design

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IEEE ACCESS 2019年 7卷 157353-157362页

作者： Zhang, Qianqian Liu, Shifeng Gong, Daqing Zhang, Hankun Tu, Qun Beijing Jiaotong Univ Sch Econ & Management Beijing 100044 Peoples R China Beijing Technol & Business Univ Business Sch Beijing 100048 Peoples R China

With the development of railway transportation, the railway transportation enterprises expand their freight transportation from station-to-station transportation to door-to-door transportation, which makes the routing design more complicated. The existing classical optimization algorithms are difficult to meet the needs of practical applications. Therefore, the paper introduces an Improved Multi-objective Quantumbehaved Particle swarm Optimization algorithm (IMOQPSO). Then based on the continuous coding for the Railway Freight Transportation Routing Design, the proposed improved algorithm was applied to solve the problem to verify the performance of algorithm. Finally, the paper compared the performance of Improved Multi-objective Quantum-behaved Particle swarm Optimization algorithm with other four continuous multiobjective swarm intelligence algorithms. The results shown that the proposed algorithm obtained the best Pareto front which is closer to the real Pareto front of Railway Freight Transportation Routing Design. Hence, the proposed Improved Multi-objective Quantum-behaved Particle swarm Optimization algorithm can provide support for the railway transport enterprises routing design decisions to some extent.

关键词： IMOQPSO algorithm routing design swarm intelligence algorithm multi-objective optimization.

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BACSSOC: A novel clustering method for mobile forest protection using wireless sensor network with lower energy consumption and lower latency

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SIMULATION MODELLING PRACTICE AND THEORY 2023年第1期128卷

作者： Xie, Jianpeng Zhang, Mingyue Jin, Bo Zhai, Jiahui Wang, Zongyang Xiao, Jing Zhou, Jie Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China Cent South Univ Forestry & Technol Changsha Peoples R China

The utilization of Mobile Forest Protection Using Wireless Sensor Network (Mobile-FPWSN) has exhibited notable advantages in mitigating economic losses in forestry and enhancing the efficiency of fire prevention, thereby leading to its widespread adoption in the forestry protection industry. However, in the current developmental stage of Mobile-FPWSN, the impact of sensor nodes on the system is often disregarded in forest fire prevention and control. Latest studies have indicated that clustering techniques exhibit noteworthy benefits, including a decrease in energy consumption and an extension of system life. However, current clustering methodologies do not comprehensively address the concerns of latency, energy consumption, and delay while operating under Mobile-FPWSN conditions. In order to tackle these challenges, a novel clustering method called Boltzmann adaptive chaotic salp swarm optimization clustering (BACSSOC) has been proposed for Mobile-FPWSN. This method aims to significantly prolong the system's lifetime, efficiently reduce energy consumption, and minimize system latency to the greatest extent possible. BACSSOC dramatically reduces the energy consumption of cluster heads during the adaptive clustering phase of Mobile-FPWSN, thereby decelerating the depletion of node energy and improving the forest fire monitoring time in comparison with other state-of-the-art algorithms. To evaluate the efficacy of BACSSOC in comparison to ML-AEFA, PCFMO, and SOA-EACR algorithms in mobile FPWSNs, we have conducted a unique forest fire simulation experiment based on a realistic forest fire scenario. The results have unequivocally demonstrated that BACSSOC surpasses ML-AEFA, PCFMO, and SOA-EACR algorithms, exhibiting advantages of at least 15% in reducing node energy consumption, 7% in decreasing system latency, and 9% in increasing system lifespan. These findings highlight the remarkable potential of BACSSOC as an effective network clustering solution in mobile FPWSNs.

关键词： Wireless sensor network Clustering Mobile-FPWSN swarm intelligence algorithm

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