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cultural algorithm-based quantum-behaved particle swarm optimization

INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2010年第10期87卷 2143-2157页

作者： Yang, Kaiqiao Maginu, Kenjiro Nomura, Hirosato Kyushu Inst Technol Dept Artificial Intelligence Iizuka Fukuoka Japan

A hybrid quantum-behaved particle swarm optimization (QPSO) based on cultural algorithm (CA), which we call cultural QPSO, is proposed. Although QPSO is a promising algorithm for many optimization problems, it is apt to lose the diversity of the swarm in the later period of the search and prematurely converges to the local optimum. Inspired by the structure of human society, this paper uses the CA model to diversify the QPSO population and improve the QPSO's performance. In this model, the swarm is divided into two sub-swarms: the common particle and the elite particle sub-swarm. If a particle comes from a common sub-swarm, it will evolve according to the QPSO method, and during the evolvement, it will be affected not only by the other common particles but also by the elites. For the elites, the differential evolution (DE) method is adopted for evolvement. After each generation, the elites will be re-elected from the whole swarm according to fitness values. The simulation results on benchmark functions demonstrate that cultural QPSO outperforms the original QPSO for many problems.

关键词： quantum-behaved PSO cultural algorithm differential evolution

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cultural algorithm Initializes Weights of Neural Network Model for Annual Electricity Consumption Prediction

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2020年第6期11卷 101-110页

作者： Phatai, Gawalee Chiewchanwattana, Sirapat Sunat, Khamron Khon Kaen Univ Fac Sci Dept Comp Sci Khon Kaen Thailand

The accurate prediction of annual electricity consumption is crucial in managing energy operations. The neural network (NN) has achieved a lot of achievements in yearly electricity consumption prediction due to its universal approximation property. However, the well-known back-propagation (BP) algorithms for training NN has easily got stuck in local optima. In this paper, we study the weights initialization of NN for the prediction of annual electricity consumption using the cultural algorithm (CA), and the proposed algorithm is named as NN-CA. The NN-CA was compared to the weights initialization using the other six metaheuristic algorithms as well as the BP. The experiments were conducted on the annual electricity consumption datasets taken from 21 countries. The experimental results showed that the proposed NN-CA achieved more productive and better prediction accuracy than other competitors. This result indicates the possible consequences of the proposed NN-CA in the application of annual electricity consumption prediction.

关键词： Neural network weights initialization metaheuristic algorithm cultural algorithm annual electricity consumption prediction

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cultural algorithm with Improved Local Search for Optimization Problems

Cultural Algorithm with Improved Local Search for Optimizati...

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IEEE Congress on Evolutionary Computation

作者： Awad, Noor H. Ali, Mostafa Z. Duwairi, Rehab M. Jordan Univ Sci & Technol Irbid 22110 Jordan

ISBN: (纸本)9781479904549;9781479904532

In this paper we propose an optimization algorithm for global optimization problems. The proposed algorithm is named (CA-ImLS) and is based on cultural algorithms and an improved local search approach for optimization over large-scale continuous spaces. In this paper, cultural algorithm and an improved sub-regional local search method are hybridized to form CA-ImLS. The original cultural algorithm is extended to have five parallel local searches that are rooted to its knowledge sources in the belief space component. This directs the search in multi-directions and improves the capability of its problem solvers in obtaining better-quality solutions. The distribution of new search agents is based on the success of the knowledge sources in which each knowledge source has its own local search for generating new agents with better fitness values and enhanced diversity to avoid stagnation. Experimental results are given for a set of benchmark optimization functions. Results indicate an average improvement of 2%-83% over the basic cultural algorithm framework.

关键词： cultural algorithm Local search knowledge source Hybrid algorithm Optimization problem

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cultural algorithm Based on Particle Swarm Optimization for Partner Selection of Virtual Enterprise

Cultural Algorithm Based on Particle Swarm Optimization for ...

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31st Chinese Control Conference

作者： Zhou Wei Bu Yan-ping East China Univ Sci & Technol Sch Business Shanghai 200237 Peoples R China Shanghai Jiao Tong Univ Sch Technol Shanghai 201101 Peoples R China

ISBN: (纸本)9789881563811

A novel cultural algorithm based on particle swarm optimization (PSO) algorithm was proposed in this paper. After analyzing the partner selection problems of virtual enterprise, the CPSO algorithm was presented to solve enterprise alliance problem within reasonable time and cost. There are certain number partners of each sub-task in virtual enterprise environment. The objective is, by selecting the optimal combination of partners, to minimize project's completion time and project's total cost. We tested the CPSO algorithm against the PSO method. Simulation results demonstrate that it can be superior to the regular PSO. We also tested the CPSO algorithm with the exhaustion method to show the algorithm's efficiency.

关键词： cultural algorithm particle swarm optimization algorithm virtual enterprise partner selection

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cultural algorithm Based on Particle Swarm Optimization for Clearance of Flight Control Law

Cultural Algorithm Based on Particle Swarm Optimization for ...

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6th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Meng, Wenyue Wei, Chen Tang, Jie Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China

ISBN: (纸本)9781424487554

A multiple optimization algorithm, the cultural algorithm based on Particle Swarm Optimization (CAPSO) was applied to flight control law clearance. The algorithm provides a more accurate performance in function optimization when compared with the traditional Particle Swarm Optimization (PSO). A short-period of longitudinal flight control system for a certain aircraft was designed before the validation. Two representative linear criterions were considered. The results indicate that the proposed algorithm provides an effective approach in search for worst combinations of parametric model uncertainties during process of flight clearance.

关键词： Particle Swarm Optimization cultural algorithm clearance worst combine of parameters

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An efficient hybrid of elephant herding optimization and cultural algorithm for optimal design of trusses

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 781-801页

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

In this article, a new hybrid algorithm is proposed which was based on the elephant herding optimization (EHO) and cultural algorithm (CA), known as elephant herding optimization cultural (EHOC) algorithm. In this process, the belief space defined by the cultural algorithm was used to improve the standard EHO. EHO is motivated by herding behavior of the elephant groups. These behaviors are modeled into two operators including clan updating operator and separating operator. In EHOC, based on belief space, the separating operator is defined, which is able to create new local optimums in search space, to improve the algorithm search ability and to create an algorithm with an optimal exploration-exploitation balance. The CA, EHO, and EHOC algorithms are applied to eight mathematical optimization problems and four truss weight minimization problems, and to assess the performance of the proposed algorithm, the results are compared. The results clearly indicate that EHOC is capable of accelerating the convergence rate effectively and can develop better solutions compared to the CA and EHO. In addition, it can produce competitive results in comparison with other metaheuristic algorithms in the literature.

关键词： Elephant herding optimization cultural algorithm Hybrid metaheuristic algorithms Mathematical optimization problems Truss weight minimization problems

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A hybrid multi-objective cultural algorithm for short-term environmental/economic hydrothermal scheduling

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ENERGY CONVERSION AND MANAGEMENT 2011年第5期52卷 2121-2134页

作者： Lu, Youlin Zhou, Jianzhong Qin, Hui Wang, Ying Zhang, Yongchuan Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Peoples R China

The short-term environmental/economic hydrothermal scheduling (SEEHS) with the consideration of multiple objectives is a complicated non-linear constrained optimization problem with non-smooth and non-convex characteristics. In this paper, a multi-objective optimization model of SEEHS is proposed to consider the minimal of fuel cost and emission effects synthetically, and the transmission loss, the water transport delays between connected reservoirs as well as the valve-point effects of thermal plants are taken into consideration to formulate the problem precisely. Meanwhile, a hybrid multi-objective cultural algorithm (HMOCA) is presented to deal with SEEHS problem by optimizing both two objectives simultaneously. The proposed method integrated differential evolution (DE) algorithm into the framework of cultural algorithm model to implement the evolution of population space, and two knowledge structures in belief space are redefined according to the characteristics of DE and SEEHS problem to avoid premature convergence effectively. Moreover, in order to deal with the complicated constraints effectively, new heuristic constraint handling methods without any penalty factor settings are proposed in this paper. The feasibility and effectiveness of the proposed HMOCA method are demonstrated by two case studies of a hydrothermal power system. The simulation results reveal that, compared with other methods established recently, HMOCA can get better quality solutions by reducing fuel cost and emission effects simultaneously. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Environmental/economic hydrothermal scheduling cultural algorithm Differential evolution Constraint handle

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Research on cultural algorithm for solving routing problem of mobile agent

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The Journal of China Universities of Posts and Telecommunications 2008年第4期15卷 121-125页

作者： MA Jun ZHANG Jian-pei YANG Jing CHENG Li-li College of Computer Science and Technology Harbin Engineering University Harbin 150001 China

The key idea behind cultural algorithm is to explicitly acquire problem-solving knowledge from the evolving population and in return apply that knowledge to guide the search. In this article, cultural algorithm-simulated annealing is proposed to solve the routing problem of mobile agent. The optimal individual is accepted to improve the belief space＇s evolution of cultural algorithms by simulated annealing. The step size in search is used as situational knowledge to guide the search of optimal solution in the population space. Because of this feature, the search time is reduced. Experimental results show that the algorithm proposed in this article can ensure the quality of optimal solutions, and also has better convergence speed. The operation efficiency of the system is considerably improved.

关键词： cultural algorithm mobile agent routing simulated annealing

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An efficient energy-aware method for virtual machine placement in cloud data centers using the cultural algorithm

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JOURNAL OF SUPERCOMPUTING 2019年第10期75卷 6904-6933页

作者： Mohammadhosseini, Mahdieh Haghighat, Abolfazl Toroghi Mahdipour, Ebrahim Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Islamic Azad Univ Qazvin Branch Dept Comp & Informat Technol Engn Qazvin Iran

Excessive consumption of energy in cloud data centers whose number is increasing day by day has led to substantial problems. Hence, offering efficient schemes for virtual machine (VM) placement to decrease energy consumption in cloud computing environments has become a significant research field in recent years. In this paper, with the goal of reducing energy consumption in cloud data centers, we present a VM placement method using the cultural algorithm. In the proposed algorithm called balance-based cultural algorithm for virtual machine placement (BCAVMP), a new fitness function is introduced to evaluate VM allocation solutions. In this function, by using the sum of balance vector lengths for each VM placement, balanced utilization of resources is considered. Also, by applying the amount of energy usage in the fitness function, solutions with lower energy consumption are intended. The performance of the proposed method is evaluated using CloudSim simulator. The simulation results indicate that by appropriate VM assignment and resource wastage reduction, energy consumption in cloud data centers can be decreased.

关键词： Cloud computing Virtual machine placement Balance vector Cloud data centers Energy consumption cultural algorithm

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Spatial forest resource planning using a cultural algorithm with problem-specific information

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ENVIRONMENTAL MODELLING & SOFTWARE 2015年 71卷 126-137页

作者： Liu, Wan-Yu Lin, Chun-Cheng Aletheia Univ Dept Tourism Informat New Taipei City 251 Taiwan Natl Chiao Tung Univ Dept Ind Engn & Management Hsinchu 300 Taiwan

A cultural algorithm (CA) is proposed for the spatial forest resource planning problem that aims at maximizing the total timber volume harvested over a harvest planning schedule, subject to constraints of minimum harvest age, minimum adjacency green-up age, and approximately even volume flow for each period of the schedule. To increase the solution-search ability, the CA extracts problem-specific information during the evolutionary solution search to update the belief space of each generation, which has cultural influences and guidance on the next generation. The key design of the proposed CA is to propose the cultural and evolutionary operators specifically for the problem. This work is of high value as a comprehensive experimental analysis shows that the proposed CA rooted from evolutionary algorithm (EA) obtains 0.44%-1.13% better fitness and performs more stably than the previous best-known simulated annealing (SA) approach, which was shown to perform better than the EA previously. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Forest planning Harvest scheduling Landscape design Evolutionary algorithm cultural algorithm

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