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3rd International Conference on Vision, Image and Signal Processing (ICVISP)

作者： Ravichandran, Ramya Kobti, Ziad Univ Windsor Sch Comp Sci Windsor ON Canada

ISBN: (纸本)9781450376259

The importance of dynamic multi-objective optimization problems (DMOPs) is on the rise, in complex systems. DMOPs have several objective functions and constraints that vary over time to be considered simultaneously. As a result, the Pareto optimal solutions (POS) and Pareto front (PF) will also vary with time. The desired algorithm should not only locate the optima but also track the moving optima efficiently. In this paper, we propose a new cultural algorithm (CA) based on decomposition (CA/D). The primary objective of the CA/D algorithm is to decompose DMOP into several scalar optimization subproblems and solve simultaneously. The subproblems are optimized utilizing the information shared only by its neighboring problems. The proposed CA/D is evaluated using CEC 2015 optimization benchmark functions. The results show that CA/D outperforms CA, Multi-population CA (MPCA), and MPCA incorporating game strategies (MPCA-GS), particularly in hybrid and composite benchmark problems.

关键词： Dynamic Multi-Objective Optimization cultural algorithm Decomposition

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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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A Parallel Framework for Multi-Population cultural algorithm and Its Applications in TSP 3rd

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3rd International Conference on Machine Learning, Optimization, and Data Science (MOD)

作者： Unold, Olgierd Tarnawski, Radoslaw Wroclaw Univ Sci & Technol Dept Comp Engn Fac Elect Wyb Wyspianskiego 27 PL-50370 Wroclaw Poland

ISBN: (纸本)9783319729268;9783319729251

This paper presents a novel parallel framework based on the Multi-Population cultural algorithm (MPCA) scheme for optimization problems. Contrary to the existing variants of cultural algorithm (CA), the proposed parallel framework for MPCA (PFMPCA) allows the use of any implemented metaheuristic both in a belief, and in a population space. Furthermore, the proposed approach permits CA to evolve simultaneously multiple population and belief sub-spaces, leveraging the dual inheritance mechanism and utilizing multi-population approach. Moreover, each sub-population (in population or belief space) is able to communicate between each other. PFMPCA has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. The performance of the developed framework was evaluated using asymmetric Travelling Salesman Problem (ATSP). The MPCA for TSP implemented by means of the parallel framework proves to have an extensible architecture designed to accommodate changes and good performances.

关键词： cultural algorithm Multi-Population GPU computing CUDA architecture Travelling Salesman Problem Ant Colony Optimization Genetic algorithm

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CADE: A hybridization of cultural algorithm and Differential Evolution for numerical optimization

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INFORMATION SCIENCES 2017年 378卷 215-241页

作者： Awad, Noor H. Ali, Mostafa Z. Suganthan, Ponnuthurai N. Reynolds, Robert G. Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Jordan Univ Sci & Technol Irbid 22110 Jordan Wayne State Univ Detroit MI 48202 USA

Many real-world problems can be formulated as optimization problems. Such problems pose a challenge for researchers in the design of efficient algorithms capable of finding the best solution with the least computational cost. In this paper, a new evolutionary algorithm is proposed that combines the explorative and exploitative capabilities of two evolutionary algorithms, cultural algorithm (CA) and Differential Evolution (DE) algorithm. This hybridization follows the HTH (High-level Teamwork Hybrid) nomenclature in which two meta-heuristics are executed in parallel. The new algorithm named as CADE, manages an overall population which is shared between CA and DE simultaneously. Four modified knowledge sources have been used in proposed CA which are: topographical, situational, normative and domain. The role of the used acceptance function in belief space is to select the knowledge of the best individuals to update the current knowledge. A novel quality function is used to determine the participation ratio for both CA and DE, and then a competitive selection takes place in order to select the proportion of function evaluations allocated for each technique. This collaborative synergy emerges between the DE and CA techniques and is shown to improve the quality of solutions, beyond what each of these two algorithms alone. The performance of the algorithm is evaluated on a set of 50 scalable optimization problems taken from two sources. The first set of 35 came from existing benchmark sets available in the literature. The second set came from the 2014 IEEE Single Function optimization competition. The overall results show that CADE has a favorable performance and scalability behaviors when compared to other recent state-of-the-art algorithms. CADE's overall performance ranked at number 1 for each of the two sets of problems. It is suggested that CADE's success across such a broad spectrum of problem types and complexities bodes well for its application to new and novel ap

关键词： Evolutionary algorithm cultural algorithm Differential evolution Numerical optimization

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An improved cultural algorithm and its application in image matching

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第13期76卷 14951-14968页

作者： Yan, Xuesong Song, Tao Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China China Univ Geosci Ctr Network & Educ Technol Wuhan 430074 Hubei Peoples R China WuHan Inst Technol Fac Comp Sci & Engn Wuhan 430074 Hubei Peoples R China

cultural algorithm (CA) are a class of computational models derived from observing the cultural evolution process in nature and is used to solve complex calculations of the new global optimization search algorithms. Aiming at the traditional cultural algorithm has poor precision and trap into local optimum of global optimization. In this paper, introduce the isolation niche technology into the traditional cultural algorithm. With improvements, the algorithm is less likely to trap in local optimum. According to the test of one set of benchmark function, the proposed algorithm has greater improvements than ordinal cultural algorithm in the aspects of convergence precision and stability. In this paper, introduce the proposed algorithm into the image matching problem, and the simulation test shows that the algorithm for image matching problem has made great effects in stability and convergence precision.

关键词： cultural algorithm Isolation niche technology Population Image matching

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Non-Convex Optimization of Combined Enviromental Economic Dispatch through the Third Version of the cultural algorithm (CA3)

Non-Convex Optimization of Combined Enviromental Economic Di...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Goudarzi, Arman Swanson, Andrew G. Tooryan, Fatemeh Ahmadi, Afshin Univ KwaZulu Natal Sch Elect Elect & Comp Engn Durban South Africa Clemson Univ Sch Elect Elect & Comp Engn Clemson SC USA

ISBN: (纸本)9781509066179

Economic load dispatch (ELD) is conventionally defined as the process of allocating generation among the running generating units such that the demand is satisfied and the fuel cost is minimized. Due to increased environmental regulation, emission constraints are being added to redefine the conventional ELD problem. It is important to minimize both the operating fuel cost and emission levels simultaneously while satisfying the load demand;this process is known as combined environmental economic dispatch (CEED). The resultant CEED becomes a challenging non-convex optimization problem, which is difficult to solve using the traditional methods such as lambda iteration and gradient method. To tackle this barrier the study has proposed a novel heuristic search technique based on a modified version of the cultural algorithm (CA3) to solve the CEED problem whilst considering transmission line losses and several physical constraints of generating units. In order to validate the aims of this study, the proposed approach has been applied in two different test systems;5 generating units and 20 generating units systems. The results of the simulations show that the proposed method of the study provides a lower generation cost for the CEED problem in comparison to the other studied techniques, it is also observed that the convergence of the CA3 remains considerably fast as the system complexity increases.

关键词： Combined environmental economic dispatch cultural algorithm prohibited operating zones ramp-rate limits valve-point effect

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Detecting faulty sensors in an array using symmetrical structure and cultural algorithm hybridized with differential evolution

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Frontiers of Information Technology & Electronic Engineering 2017年第2期18卷 235-245页

作者： Shafqat Ullah KHAN Ijaz Mansoor QURESHI Fawad ZAMAN Wasim KHAN School of Engineering & Applied Sciences ISRA University Islamabad 44000 Pakistan Electrical Department Air University Islamabad 44000 Pakistan Electrical Department COMSAT Instimte of lnformation Technology Attock 44000 Pakistan Electronic Engineering Department International Islamic University H-10 lslamabad 44000 Pakistan

The detection of fully and partially defective sensors in a linear array composed of N sensors is addressed. First, the symmetrical structure of a linear array is proposed. Second, a hybrid technique based on the cultural algorithm with differential evolution is developed. The symmetrical structure has two advantages：（1） Instead of finding all damaged patterns, only （N-1）/2 patterns are needed; （2） We are required to scan the region from 0° to 90°instead of from 0° to 180°. Obviously, the computational complexity can be reduced. Monte Carlo simulations were carried out to validate the performance of the proposed scheme, compared with existing methods in terms of computational time and mean square error.

关键词： cultural algorithm Differential evolution Linear symmetrical sensor array

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A modified cultural algorithm with a balanced performance for the differential evolution frameworks

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KNOWLEDGE-BASED SYSTEMS 2016年 111卷 73-86页

作者： Ali, Mostafa Z. Awad, Noor H. Suganthan, Ponnuthurai N. Reynolds, Robert G. Jordan Univ Sci & Technol Sch Comp & Informat Irbid 22110 Jordan Princess Sumaya Univ Technol King Hussein Fac Comp Sci Amman 11941 Jordan Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Wayne State Univ Coll Engn Detroit MI 48202 USA

Numerous different methodologies have been introduced in the last few decades to provide efficient solutions for complex real-world problems and other optimization problems. This work focuses on the development of a simple hybrid cultural learning theme with a balanced performance for differential evolution frameworks. It is intended to be always efficient for a diverse set of optimization tasks. As different optimization algorithms behave differently depending on the problems, the combination of the best behaviors from different search strategies seems desirable. The proposed work explores the combination of the explorative/exploitative strengths of two heuristic search techniques, which discretely provide competitive results. Differential evolution is used as the population space for cultural algorithm, and is used to guide knowledge dissemination from the knowledge sources in the belief space. Here, a new influence function is introduced that adjusts the membership of each of the knowledge sources. The algorithm has been tested with the conditions and benchmark problems defined for the IEEE CEC2013 special session and competition on real-parameter single objective optimization. The paper also investigates the application of the new algorithm to a set of real-life problems concerning optimizing the weight a tension/compression spring and minimizing the fabrication cost of a welded beam engineering problem. The proposed algorithm appears to have a significant impact on the algorithmic functioning as it reliably augments the performance of the differential evolution frameworks with which it is integrated. Benchmark results for most of the synthetic functions from the special session show that the balanced hybrid obtains superior performance compared to the other competent algorithms. It scales well with the increasing dimensionality and converges in the close proximity of the global optimum for complex functions. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Evolutionary algorithm cultural algorithm Differential evolution Optimization Hybrid algorithm

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Contaminant source identification of water distribution networks using cultural algorithm

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第24期29卷

作者： Yan, Xuesong Gong, Wenyin Wu, Qinghua China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Wuhan Inst Technol Fac Comp Sci & Engn Wuhan 430205 Hubei Peoples R China

In recent years, the drinking water pollution incident occurred frequently, a serious threat to social stability and security. By using the sensor networks real-time monitoring the urban water supply networks, the water pollution event probability can be greatly reduced. But knowing how to use the water quality monitor sensor networks to collect information to identify pollution sources is still a challenging problem. In this paper, we formulate the contaminant source identification problem into an optimization problem, and then design the cultural algorithm to solve it by considering different sizes of water supply networks as the experimental data. Finally, the experimental results verify the effectiveness and robustness of the proposed method.

关键词： contaminant source identification cultural algorithm optimization water distribution networks

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