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An improved multiobjective cultural algorithm with a multistrategy knowledge base

APPLIED INTELLIGENCE 2022年第2期52卷 1157-1187页

作者： Mao, Zhengyan Liu, Mandan East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Based on the dual-inheritance framework of cultural evolution, an improved multiobjective cultural algorithm (IMOCA) with a multistrategy knowledge base is presented in this paper. Inspired by the original versions of the cultural algorithm (CA), four basic types of knowledge sources, i.e., normative, situational, topographical and historical knowledge, are effectively utilized in the proposed IMOCA. Several modifications with the knowledge base of the IMOCA are made to tackle the characteristics of the multiobjective problem. Situational knowledge is used as an external repository for storing elite individuals, and the redesigned topographical knowledge functions as a search engine to broaden the expansion of the obtained solution set. The historical knowledge used in the IMOCA aims to select a productive knowledge source to generate new individuals. Furthermore, a simple mutation scheme is introduced into the knowledge base as an influence function for the purpose of fine tuning in the late stage of search. After configuring the parameters used in IMOCA, two classic benchmark suites, i.e., WFG and MaF, are used to assess the performance of the IMOCA in approaching the Pareto fronts (PFs) with accuracy and diversity. Nondominated solution sets obtained by the IMOCA are compared with 8 state-of-the-art multiobjective algorithms available in the literature. A statistical analysis is conducted, which reveals that, by modifying the basic knowledge structure of the CA, the proposed multiobjective cultural algorithm is competent enough to handle multiobjective problems with competitive performance.

关键词： Multiobjective optimization Evolutionary algorithm cultural algorithm Dual-inheritance framework Knowledge-based evolutionary computation Nondominated sorting

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Performance of multiple tuned inerter dampers optimized by a cultural algorithm to control response in buildings subjected to ground acceleration

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JOURNAL OF BUILDING ENGINEERING 2024年 92卷

作者： Lara-Valencia, Luis Echavarria-Montana, Sebastian Valencia-Gonzalez, Yamile Univ Nacl Colombia Dept Ingn Civil Medellin 75267 Colombia

The reduction of the response parameters of civil structures subjected to the action of wind or ground accelerations is one of the main objectives of the implementation of a structural control system. Many investigations have been developed to make progress and contributions to the design and implementation of effective, reliable, and economical control devices. Therefore, this paper explores the performance of different configurations of multiple Tuned Inerter Dampers (TIDs) in buildings excited by earthquakes. TIDs are passive control devices that enhance a viscous damper by incorporating a novel mechanism known as an inerter. The inerter amplifies an apparent mass that is used to dissipate energy in the controlled system. Four different control alternatives with one, two, three, and four TIDs in the structure are studied. The determination of the optimal design parameters of the different control alternatives was carried out using a novel and very promising optimization process based on a cultural algorithm (CA), which has great potential due to being a fast, precise, and reliable method, which is not traditionally associated with the optimization of seismic protection devices. The CA was tuned through a parametric study and compared in terms of performance with an optimization process based on an exhaustive search. A case study of a 12 -story building subjected to the action of different ground motions was developed in which the proposed optimal control alternatives were tested. The results showed a significant effectiveness of the control alternatives using multiple TIDs, achieving reductions in peak displacements, RMS values of the maximum displacements and the maximum interstory drifts of up to 54 %.

关键词： Tuned inerter damper cultural algorithm Optimal design Structural control of buildings Passive control Adjustment of the optimization process Response reduction

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Training Artificial Neural Network with a cultural algorithm

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NEURAL PROCESSING LETTERS 2024年第5期56卷 225-225页

作者： Ates, Kuebra Tuemay Kalkan, Ibrahim Erdem Sahin, Cenk Cukurova Univ Dept Ind Engn TR-01330 Adana Turkiye

Artificial neural networks are amongst the artificial intelligence techniques with their ability to provide machines with some functionalities such as decision making, comparison, and forecasting. They are known for having the capability of forecasting issues in real-world problems. Their acquired knowledge is stored in the interconnection strengths or weights of neurons through an optimization system known as learning. Several limitations have been identified with commonly used gradient-based optimization algorithms, including the risk of premature convergence, the sensitivity of initial parameters and positions, and the potential for getting trapped in local optima. Various meta-heuristics are proposed in the literature as alternative training algorithms to mitigate these limitations. Therefore, the primary aim of this study is to combine a feed-forward artificial neural network (ANN) with a cultural algorithm (CA) as a meta-heuristic, aiming to establish an efficient and dependable training system in comparison to existing methods. The proposed artificial neural network system (ANN-CA) evaluated its performance on classification tasks over nine benchmark datasets: Iris, Pima Indians Diabetes, Thyroid Disease, Breast Cancer Wisconsin, Credit Approval, Glass Identification, SPECT Heart, Wine and Balloon. The overall experimental results indicate that the proposed method outperforms other methods included in the comparative analysis by approximately 12% in terms of classification error and approximately 7% in terms of accuracy.

关键词： Metaheuristic optimization Hybrid algorithms Optimization techniques and training algorithms Artificial neural network cultural algorithm

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Design of Hybrid Power System Stabilizer for Dynamic Stability Improvement using cultural algorithm 4

Design of Hybrid Power System Stabilizer for Dynamic Stabili...

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4th IEEE Sustainable Power and Energy Conference (ISPEC)

作者： Patel, Jasmeen Das, Narottam Islam, Syed Cent Queensland Univ Sch Engn & Technol Melbourne Campus Melbourne Vic Australia Cent Queensland Univ Ctr Intelligent Syst Brisbane Qld 4000 Australia Federat Univ Sch Sci Engn & Informat Technol Ballarat Vic Australia

ISBN: (纸本)9781665485227

In power system the stability is an important property that varies on the operational condition and the interruption to which it is exposed. The power system network endangered to the similar disruption can be stable at one operational situation (e.g., in off-peak hours) and not stable at another (e.g., at peak times). Similarly, a network at one operational situation can be stable to one disturbance and not stable to another. Consequently, stability reports generally involve the analysis of several cases, in order to cover various disruptions of interest and the key points of operation of the system. This research recommends various stabilizers such as, Power system stabilizers (PSS), Proportional Integration Differentiation (PID) and Fractional Order PID (FOPID) to decrease oscillations due to small signal disruption. The PSS-Voltage stabilizer generates impulses at the time of speed-change which usually results in positive PSS output. Using the FOPID procedure in relation with PSS, this approach can decrease these impulses. Using this approach, the generated impulses are finally decreased. Genetic algorithm, particle swarm optimization and cultural algorithm are used for parameter fine-tuning of all the stabilizers. All these algorithms will help to tune parameters at soft computing level and then again tuned by two stabilizers which are FOPID and PSS. Now, again two stabilizers will tune and provide output needs to compare and average output to get required tuned values. Finally, the average of two stabilizers will field circuit and drive rotor according to the value given to it.

关键词： cultural algorithm Fractional Order Potential Integration Differentiation (FOPID) Genetic algorithm Particle Swarm Optimization Power System Stabilizers (PSS)

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An Alternative Approach Using the Firefly algorithm and a Hybrid Method Based on the Artificial Bee Colony and cultural algorithm for Reservoir Operation

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WATER 2024年第6期16卷 816页

作者： Phumiphan, Anujit Kosasaeng, Suwapat Sivanpheng, Ounla Hormwichian, Rattana Kangrang, Anongrit Univ Phayao Sch Engn Phayao 56000 Thailand Water Management & Maintenance Div Reg Irrigat Off 5 Udonthani 41000 Thailand Natl Univ Laos Fac Water Resources Viangchan 01020 Laos Mahasarakham Univ Fac Engn Maha Sarakham 44150 Thailand

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

关键词： optimization techniques cultural algorithm firefly algorithm artificial bee colony reservoir operation rule curves

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Optimal design of truss structures using a hybrid method based on particle swarm optimizer and cultural algorithm

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STRUCTURES 2021年 32卷 391-405页

作者： Jafari, Malihe Salajegheh, Eysa Salajegheh, Javad Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In the present study, an efficient hybrid algorithm is proposed based on the particle swarm optimizer (PSO) and the cultural algorithm (CA) for the optimal design of truss structures. In this method, the cultural space defined by the CA has been used to improve the PSO method. The cultural space models the cultural information about individuals in the population space. In the so-called particle swarm optimizer cultural (PSOC) algorithm, three modifications are made on the standard PSO. First, the components related to the best memory of each particle are modified according to the range of cultural space. Second, cultural space is used in order to modify the variables violating the allowable range. And third, with the introduction of "Personal Current Bound Strategy (PCBS)", any unnecessary structural analyzes, that consequently impose a lot of additional cost in the optimization process, are avoided. Therefore, if the particles move in an inappropriate direction, and in case a weaker situation is achieved, the relevant situation will initially be rectified using cultural space, and after ensuring that it has been placed in the upper limits of society, analysis will then be carried out on it. To show the computational advantages of the PSOC, several benchmark examples are presented. The results show that the PSOC algorithm can converge to a better solution and effectively accelerate the convergence rate compared to other variants of PSO and some other well-known metaheuristic methods.

关键词： Optimal design Truss structures Particle swarm optimizer cultural algorithm Hybrid algorithm

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Optimal Design of a Permanent Magnet Synchronous Motor Using the cultural algorithm 12

Optimal Design of a Permanent Magnet Synchronous Motor Using...

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12th Power Electronics, Drive Systems, and Technologies Conference (PEDSTC)

作者： Sharifi, Tohid Mirsalim, Mojtaba Amirkabir Univ Technol Dept Elect Engn Tehran Polytech Tehran Iran

ISBN: (纸本)9781665403665

In this paper, a permanent magnet synchronous motor (PMSM) structure is optimized by a single-objective optimization method known as the cultural algorithm (CA). The torque capability of the PMSM is high due to the presence of the permanent magnets (PMs) in its structure. However, in critical applications with limited space (especially with volume limitation), the torque density is a more prominent function than the torque. For this reason, in the optimization problem presented in this paper, the torque density is the objective function. Therefore, this study aims to maximize the torque capability at a specified space besides maintaining the torque ripple under a specified value (as a constraint). To this, the structural parameters of the machine are optimized by the CA. An additional decision variable is also considered, and an important effect is concluded from that. The mathematical basics of the CA, operators, and other settings of the algorithm are also introduced. The results of the optimization process would be presented and compared with the initial machine design. These results demonstrate that the CA increases the torque density of the machine and keeps the torque ripple under the specified value. After the optimization results and comparisons, the electromagnetic performance of the motor is presented.

关键词： cultural algorithm decision variables high torque density motor optimization permanent magnet synchronous motor

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Solving TSP Problem in Cloud Computing using Improved cultural algorithm 26

Solving TSP Problem in Cloud Computing using Improved Cultur...

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26th International Computer Conference of the Computer-Society-of-Iran

作者： Azarkasb, Seyed Omid Khasteh, Seyed Hossein Kashi, Saeed Sedighian KN Toosi Univ Technol Artificial Intelligence Tehran Iran KN Toosi Univ Technol Software Engn Tehran Iran

ISBN: (纸本)9781665412414

Traveling Salesman Problem (TSP), despite its simple appearance, is one of the classic and complex problems in Combinatorial Optimization and it is difficult to find an accurate answer for large samples. This problem is so important that many real-world problems can be turned into a TSP and solved. Optimization methods for solving difficult problems, such as TSP, mainly involve a large number of variables and constraints that reduce their practical efficiency in solving large-scale problems. An optimization algorithm includes factors that increase the speed of convergence, which can be inherited as a culture to the next generation. The basic idea of cultural algorithms is based on the theory that in advanced societies, in addition to the knowledge that parsons have in their genetic code and inherited from their ancestors, there is another element called culture for evolution. Culture is a set of accepted beliefs of community leaders. Of course, one of the disadvantages of this type of algorithm is the formation of a false culture and the adherence of all people to the same culture, which occasionally leads to local optimizations during the evolution process. The solution proposed in this paper to overcome this shortcoming is to select diverse leaders and consequently produce different subpopulations. This increases the diversity of people in the population and thus distributes the search throughout the problem space, and breaks the problem into smaller problems, and reduces the complexity of problem-solving temporality. In the meantime, cloud computing, given scalability and accessibility, provides us with good facilities. Using the capabilities of cloud computing, one problem can be divided into smaller sub-problems and solved in several virtual machines. Each of the virtual machines uses the improved culture algorithm technique proposed to solve their dedicated sub-problem. In the meantime, the nodes assigned to each machine are hidden from the other machine. Fina

关键词： Traveling Salesman Problem cultural algorithm Cloud Computing NP-Hard Problems

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Enhanced cultural algorithm to solve multi-objective attribute reduction based on rough set theory

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MATHEMATICS AND COMPUTERS IN SIMULATION 2020年 170卷 332-350页

作者： Abdolrazzagh-Nezhad, Majid Bozorgmehr Univ Qaenat Fac Engn Dept Comp Engn Qaen Iran

In extracting hidden information from a data, its high dimension can create challenges in the quality of the extracted information and the search space size. Attribute reduction based on minimizing both missed information and selected subset attributes is logical solution for the challenge. Rough set theory (RST) is an information recognition technique in uncertain data that it shows the value missed information for the selected attributes. In this paper, a multi-objective attribute reduction (MOAR) is modeled by designing a new effective cost function to optimize the minimum number of attributes with the maximum dependency coefficient of the RST. Due to the MOAR is an NP-hard problem, an enhanced draft of cultural algorithm, as a continuous optimization algorithm, is proposed to solve it, as a discrete problem for the first time. The cultural algorithm (CA) with a dual inheritance system is enhanced by utilizing just normative and situational components to generate new individuals and planning a novel heuristic to discrete population and belief spaces. With regard to design the research problem, the CA and five algorithms are implemented to compare their results on twelve well-known UCI datasets in three categories sizes;small, middle and large. The tuning algorithm's parameters to find the best possible values are done and different size of the population is considered to evaluate the sensitivity of the algorithms on the population size parameter. The experimental results show that the proposed algorithm is able to find competitive results when compared to the state-of-the-art algorithms. (C) 2019 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.Y. All rights reserved.

关键词： Multi-objective attribute reduction Rough set theory cultural algorithm Discretization Population sensitivity

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Artificial immune network clustering based on a cultural algorithm

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-17页

作者： Deng, Liyuan Yang, Ping Liu, Weidong Xian Res Inst High Technol Xian Peoples R China

Data mining technology has been applied in many fields. Prototype-based cluster analysis is an important data mining method, but its ability to discover knowledge is limited because of the need to know the number of target data categories and cluster prototypes in advance. Artificial immune evolutionary network clustering is a clustering method based on network structure. Compared with prototype-based cluster analysis, it has the advantage of realizing unsupervised learning and clustering without any prior knowledge of data. However, artificial immune evolutionary network clustering also has problems such as a lack of guidance in the clustering process, fuzzy boundary sensitivity, and difficulty in determining parameters. To solve these problems, an artificial immune network clustering algorithm based on a cultural algorithm is proposed. First, three kinds of knowledge are constructed: normative knowledge is used to regulate the spatial range of population initialization to avoid blindness;state knowledge is used to distinguish the type of antigen, and immune defense measures are taken to prevent the network structure caused by noise and boundaries from being unclear;and topology knowledge is used to guide the antigen for optimal antibody search. Second, topology knowledge in the cultural algorithm is used to characterize the distribution of antigens and antibodies in space, and elite learning is used to improve the traditional clone mutation operator. Based on the shadow set theory, a method for adaptively determining the compression threshold is proposed. Finally, the results of simulation experiments show that the proposed algorithm can effectively overcome the above problems, and the clustering performances on a synthetic dataset and an actual dataset are satisfactory.

关键词： Clustering Immune network cultural algorithm Immune defense Shadow set theory

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