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A hybrid co-evolutionary cultural algorithm based on particle swarm optimization for solving global optimization problems

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NEUROCOMPUTING 2012年 98卷 76-89页

作者： Sun, Yang Zhang, Lingbo Gu, Xingsheng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China

Intelligent evolutionary algorithms have been widely used to solve large-scale, complex global optimization problems. Co-evolutionary algorithm (CEA), cultural algorithm (CA), and particle swarm optimization (PSO) are all promising methods in the field of intelligent computation. In this paper, a hybrid co-evolutionary cultural algorithm based on particle swarm optimization (CECBPSO) is proposed. In CECBPSO, a novel space called shared global belief space (SGBS) is introduced into the co-evolutionary mechanism, and a new co-evolutionary cultural framework is built. Through the synergistic mechanism, the algorithm has higher probability of avoiding local optima and the whole swarm can find global optima more quickly. Factorial Design (FD) approach is used in this paper in order to get a guideline on how to tune the designed parameters in CECBPSO. Extensive computational studies are also carried out to evaluate the performance of CECBPSO on thirteen benchmark functions and three real-life optimization problems. The results show that the proposed algorithm has superior performance to other compared algorithms in terms of accuracy and convergence speed, especially on high-dimensional problems. (c) 2012 Elsevier B.V. All rights reserved.

关键词： Co-evolutionary algorithm cultural algorithm Particle swarm optimization Global optimization Factorial design Orthogonal test

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A multi-population cultural algorithm with adaptive diversity preservation and its application in ammonia synthesis process

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NEURAL COMPUTING & APPLICATIONS 2012年第6期21卷 1129-1140页

作者： Xu, Wei Wang, Raofen Zhang, Lingbo Gu, Xingsheng E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

A multi-population cultural differential evolution (MCDE) algorithm is proposed. Each of the populations is managed by its private cultural differential evolution algorithm, in which a center individual is introduced into the belief space and selection function follows a new method to select the offspring for the next generation. To accelerate the convergence speed, the populations exchange their knowledge with each other every given generations. An adaptive mechanism of population diversity preservation is put forward to prevent the populations from being trapped in local optima. In the adaptive mechanism, the idea of culture fusion between populations is used to know the convergence status, so that the diversity of populations is kept along the evolutionary process. The performance evaluation on MCDE using eleven constrained optimization problems shows that MCDE is a competitive approach. MCDE is further applied to a practical optimization problem in an ammonia synthesis system with the objective to maximize the net value of ammonia. The results achieved by MCDE are compared with those by two traditional differential evolution algorithms, which indicate that MCDE has more excellent performance and better effectiveness.

关键词： cultural algorithm Differential evolution Knowledge exchange Adaptive diversity preservation Ammonia synthesis

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Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems

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MATHEMATICS AND COMPUTERS IN SIMULATION 2009年第10期79卷 3136-3147页

作者： Coelho, Leandro dos Santos Thom Souza, Rodrigo Clemente Mariani, Viviana Cocco Pontifical Catholic Univ Parana Ind & Syst Engn Grad Program BR-80215910 Curitiba Parana Brazil Pontifical Catholic Univ Parana Dept Mech Engn BR-80215910 Curitiba Parana Brazil

Evolutionary algorithms (EAs) are general-purpose stochastic search methods that use the metaphor of evolution as the key element in the design and implementation of computer-based problems solving systems. During the past two decades, EAs have attracted much attention and wide applications in a variety of fields, especially for optimization and design. EAs offer a number of advantages: robust and reliable performance, global search capability. little or no information requirement, and others. Among various EAs, differential evolution (DE), which characterized by the different mutation operator and competition strategy from the other EAs, has shown great promise in many numerical benchmark problems and real-world optimization applications. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. To improve the global optimization property of DE, in this paper, a DE approach based on measure of population's diversity and cultural algorithm technique using normative and situational knowledge sources is proposed as alternative method to solving the economic load dispatch problems of thermal generators. The traditional and cultural DE approaches are validated for two test systems consisting of 13 and 40 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Simulation results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving economic load dispatch problems. (C) 2009 IMACS. Published by Elsevier B.V. All rights reserved.

关键词： Optimization Evolutionary algorithms Economic dispatch cultural algorithm Differential evolution

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A multipopulation cultural algorithm using fuzzy clustering

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APPLIED SOFT COMPUTING 2007年第2期7卷 506-519页

作者： Alami, J. El Imrani, A. Bouroumi, A. Univ Mohammed V Agdal Fac Sci Lab LCS Rabat Morocco

In this paper, we propose a new model for multimodal function optimization, based on the concepts of cultural algorithms and fuzzy clustering. Fuzzy clustering is used in order to partition a same population into communicating subpopulations which evolve in parallel, whilst cultural exchange ensures preserving population diversity. The ability of this model to identify global/ local optima without loss of diversity is demonstrated using several multimodal test functions, including a deceptive example. (c) 2006 Elsevier B. V. All rights reserved.

关键词： cultural algorithm multimodal function optimization fuzzy clustering

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Sensor Localization in Wireless Sensor Networks Using cultural algorithm

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2020年第4期11卷 106-122页

作者： Kulkarni, Vaishali Raghavendra Desai, Veena MS Ramaiah Univ Appl Sci Bangalore Karnataka India KLS Gogte Inst Technol Belgaum Karnataka India

Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.

关键词： Artificial Bee Colony algorithm cultural algorithm Localization Particle Swarm Optimization Wireless Sensor Networks

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Dielectric composite multimodal optimization using a multipopulation cultural algorithm

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INTELLIGENT DATA ANALYSIS 2008年第4期12卷 359-378页

作者： Alami, J. El Imrani, A. Fac Sci Rabat Lab Concept & Syst Rabat Morocco

Interest in multimodal optimization function is expanding rapidly since real-world optimization problems often require the location of multiple optima in the search space. In this context, a new model based on cultural algorithm and fuzzy clustering procedure is proposed. This model promotes the formation and maintenance of subpopulations and implements the concept of cultural exchanges among them. The validity of this model is confirmed using some well-known test function. Moreover, an electromagnetic benchmark is solved to show the usefulness of the proposed approach in real world optimization.

关键词： fuzzy clustering cultural algorithm multimodal optimization electromagnetic optimization problem

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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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Solution to Economic - Emission Load Dispatch by cultural algorithm Combined With Local Search: Case Study

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IEEE ACCESS 2018年 6卷 64023-64040页

作者： Oliveira De Freitas, Carlos Alberto Limao De Oliveira, Roberto Celio Azevedo Da Silva, Deam James Leite, Jandecy Cabral Brito Junior, Jorge De Almeida Fed Univ Para Postgrad Program Elect Engn BR-66075110 Belem Para Brazil Fed Univ Western Para Inst Engn & Geosci BR-68040255 Santarem Brazil Inst Technol & Educ Galileo Amazon Res Dept BR-69020030 Manaus Amazonas Brazil

Economic-emission load dispatch uses the fuel cost variables and gas emission in a minimized way to obtain an optimal operation in generation units in a power plant, guaranteeing the supply of demand. The first variable is definitive to ensure business continuity and the second to comply with environmental legislation and no degradation of the environment. This paper analyzes the use of a new computational optimization algorithm based on the cultural algorithm (CA), improved with local search techniques simulated annealing and Tabu search, using data from a real power plant with 10 generators and the system of the IEEE with 13 generating units. The application has two options of operation: the classic one, which operates with all generators seeking to minimize the cost and emission meeting the specified demand;and the controlled one, which turns off the generators that have the highest incremental fuel cost but guaranteeing the demand and reducing the emission of gases. Simulations were performed on the six possible options in this application. The results obtained were compared with each other and with the results of other techniques reported in the literature. The local search that improved the CA and the new way of updating topographic knowledge allowed the results to be better than those found by other metaheuristics that solved the same problem of the real plant and the IEEE system.

关键词： Economic load dispatch emission cultural algorithm power plants simulated annealing Tabu search

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A balanced fuzzy cultural algorithm with a modified Levy flight search for real parameter optimization

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INFORMATION SCIENCES 2018年 447卷 12-35页

作者： Ali, Mostafa Z. Awad, Noor H. Reynolds, Robert G. Suganthan, Ponnuthurai N. Jordan Univ Sci & Technol Scool Comp & Informat Technol Irbid 22710 Jordan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Wayne State Univ Sch Engn Detroit MI 48202 USA

Over the last few decades, a plethora of improved evolutionary algorithms was developed with exquisite performance on numerical and real-world problems. Among such algorithms, the cultural algorithm is a hyper-heuristic evolutionary algorithm, which explicitly utilizes the knowledge represented in the belief space as an essential component to guide the evolutionary search, In this paper, a new enhanced cultural algorithm incorporates a fuzzy system with a modified Levy flight search that is introduced as a new component. The new algorithm namely, b-fCA+mLF, utilizes a balanced search mode using a customized belief space with a quality function to harmonize how the knowledge sources work in parallel. The communication protocols between the population space and the belief space are established through the modified fuzzy acceptance and influence functions. Using these new functions, the best individuals are selected to create new knowledge in an effective manner. Similarly, the best knowledge is selected to evolve the individuals in the population space and guide the evolutionary search towards the promising regions. A modified Levy flight search is proposed and utilizes the information from the belief space as an input to support the evolution process to generate better solutions. The algorithm is tested on the benchmark suite taken from the IEEE-CEC'15 competition on learning-based real-parameter single objective optimization, and is compared with other algorithms including the best performer algorithms in this competition. The results suggest that the proposed algorithm is statistically better and is able to produce higher quality solutions than the other state-of-the-art algorithms. A case study on the well-known 120-bar dome truss design problem is also presented to test the validity of the proposed algorithm for the solution of complex design problems. The results of this problem show the ability of the proposed algorithm to generate good solutions with fewer fun

关键词： Evolutionary algorithm cultural algorithm Fuzzy system Knowledge source Levy flight Single objective optimization

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Construction of Internet Culture Framework Based on cultural algorithm

Construction of Internet Culture Framework Based on Cultural...

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1st IEEE Symposium on Web Society (SWS 2009)

作者： Shao Li He Mingsheng Harbin Inst Technol Sch Humanities & Social Sci Harbin 150006 Peoples R China East China Univ Political Sci & Law Dept Sociol Shanghai Peoples R China

ISBN: (纸本)9781424441563

The researches of Internet Culture followed two dimidiate aspects: technological determinism or cultural determinism in the view of stable ideas. Internet culture is changing following by social transferring and users' behaviour. cultural algorithm is the technique that adds domain knowledge to evolutionary computation methods. This dissertation is going to present internet culture as a dynamic system in the social environment, using analysis framework of the cultural algorithm The dissertation will construct a framework of internet culture within belief spaces and population spaces.

关键词： internet culture cultural system cultural algorithm belief space population space

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