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BAT-CLARA: BAT-inspired algorithm for Clustering LARge Applications

IFAC-PapersOnLine 2016年第12期49卷 243-248页

作者： Aboubi, Yasmine Drias, Habiba Kamel, Nadjet LRIA USTHB USTHB BP 32 El Alia http://www.lria.usthb.dz Bab Ezzouar Algiers Algeria

Bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. Clustering remains one of the most difficult challenges in data mining. In this paper, an overview of literature methods is undertaken followed by the presentation of a new algorithm called BAT-CLARA for clustering large data sets. It is based on bat behavior and k-medoids partitioning. The new technique is compared to the well-know partitioning algorithms PAM, CLARA, CLARANS and CLAM, a recent algorithm found in the literature. Experimental results show that, for the same tested datasets, BAT-CLARA is more effective and more efficient than previous clustering methods. © 2016

关键词： Clustering algorithms Data mining optimization Bat algorithms Clustering methods continuous optimization problems Large datasets Medoids Meta heuristics Meta heuristic optimizations Partitioning algorithms

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Application of differential evolution with selective pressure to economic dispatch optimization problems

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IFAC-PapersOnLine 2019年第13期52卷 1566-1571页

作者： Stanovov, Vladimir Akhmedova, Shakhnaz Semenkin, Eugene Reshetnev Siberian State University Krasnoyarsk Krasnoyarsk region660037 Russia

Differential evolution is an evolutionary algorithm for continuous optimization problems, known for its robustness and quick convergence. In this paper, differential evolution with a modified mutation strategy and a parameter adaptation scheme is applied to several economic dispatch optimization problems. The modification of differential evolution involves the application of selective pressure to the archive set and the ordering of vectors by fitness values. The proposed algorithm is used to solve static economic load dispatch, dynamic economic dispatch and the hydrothermal scheduling problem. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Electric load dispatching Evolutionary algorithms Genetic algorithms optimization Scheduling continuous optimization problems Differential Evolution Dynamic economic dispatch Economic Dispatch Economic load dispatch Hydrothermal scheduling problems optimization problems Selective pressure

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Parallel simulated annealing with adaptive neighborhood determined by GA

Parallel simulated annealing with adaptive neighborhood dete...

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IEEE International Conference on Systems, Man and Cybernetics (SMC 03)

作者： Miki, M Hiroyasu, T Fushimi, T Doshisha Univ Knowledge Engn Dept Kyoto 6100321 Japan

ISBN: (纸本)0780379527

Simulated annealing (SA) is an effective general heuristic method for solving many optimization problems. This paper deals with the two problems in SA. One is the long computational time of the numerical annealings, and the solution to it is the parallel processing of SA. The other one is the determination of the appropriate neighborhood range in SA, and the solution to it is the introduction of an adaptive mechanism for changing the neighborhood range. The multiple SA processes are performed in multiple processors, and the neighborhood range in the SA processes are determined by a genetic algorithms. The proposed method is applied to solve many continuous optimization problems, and it is found that the method is very useful and effective.

关键词： simulated annealing genetic algorithm adaptive neighborhood range continuous optimization problems

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Simpler and Enhanced Multifactorial Evolutionary Algorithms for continuous optimization Tasks

Simpler and Enhanced Multifactorial Evolutionary Algorithms ...

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IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)

作者： Chang, Yangyang Sobelman, Gerald E. Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

ISBN: (纸本)9781665420358

Multifactorial optimization has become one of the most promising paradigms for evolutionary multitasking within the field of computational intelligence. This methodology can improve the performance results for multiple, simultaneous optimization problems by exploiting the transfer of genetic information between them. In this paper, we present an in-depth analysis of this approach by considering several variations of the standard multifactorial evolutionary algorithm (MFEA). By using a simpler structure together with some enhanced operators, two new multifactorial evolutionary algorithms are proposed. We demonstrate that, compared with the traditional MFEA, our approach produces better results on a set of continuous optimization benchmark problems.

关键词： single-objective optimization evolutionary computation multifactorial optimization continuous optimization problems multifactorial evolutionary algorithm

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An improvement of database with local search mechanisms for genetic algorithms in large-scale computing environments

An improvement of database with local search mechanisms for ...

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IEEE Congress on Evolutionary Computation

作者： Hanada, Y Hiroyasu, T Miki, M Doshisha Univ Grad Sch Engn Kyoto 6100321 Japan

ISBN: (纸本)0780393635

Recently, GA that uses large-scale computer systems comprised of massive processors has become feasible because of the emergence of super PC clusters and Grid computation environments. Mechanisms to use massive computation resources laconically and to search effectively are necessary if large-scale computer systems are available. In this study, a new GA-specific database with the local search mechanism to assure the scalability of search performances against the number of computing resources is proposed. Our database possesses information of searched space, and in addition, the local search for non-searched spaces is applied using individuals stored in the database. To embed our.-database in a GA enables us to comprehend the quantitative rate of a searched region during searches. Applying this local search, the searched space can be expanded linearly in accordance with the increase in computing resources and the exhaustive search is guaranteed under infinite computations. The features of the introduced GA are discussed with reference to several types of experiments. This method was applied to primitive functions and test functions of continuous optimization problems. Through such experiments, it was shown that our method ensures an effective exhaustive search and has almost the same performance as a conventional GA.

关键词： computational complexity database management systems genetic algorithms large-scale systems search problems GA specific database continuous optimization problems large-scale computer systems local search

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An Adaptive Boundary Genetic Algorithm for continuous optimization Problem 1

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International Conference on Artificial Intelligence and Computational Intelligence (AICI 2012)

作者： Liu, Jianguo Congqing Bussiness & Technol Univ Appl Tech Coll Chongqing 40067 Peoples R China

ISBN: (数字)9783642342400

ISBN: (纸本)9783642342394

An adaptive boundary genetic algorithm is proposed to improve the solution precision and to increase the convergence rate of genetic algorithms for continuous optimization problems by gradually reducing the search space along with the evolution process. The proposed technique is tested over on benchmark continuous function, and the results show that the proposed technique is superior to simple genetic algorithms.

关键词： Adaptive Boundary Genetic Algorithms continuous optimization problems Search Space

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Dynamic Multi-swarm Global Particle Swarm optimization

Dynamic Multi-swarm Global Particle Swarm Optimization

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Tang, Yichao Li, Xiong Zhang, Yinglong Xia, Xuewen Gui, Ling East China Jiaotong Univ Sch Software Nanchang 330013 Jiangxi Peoples R China Minnan Normal Univ Coll Phys & Informat Engn Zhangzhou 363000 Peoples R China

ISBN: (纸本)9781728121536

This paper proposes a dynamic multi-swarm global particle swarm optimization (DMS-GPSO) that consists of two novel strategies to balance the exploration and exploitation abilities. In DMS-GPSO, the entire population is divided into a global sub-swarm and dynamic multiple sub-swarms. During the evolutionary process. the global sub-swarm focus on exploitation under the guidance of the optimal particle in the entire population, while the dynamic multiple sub-swarms focus on exploration under the guidance of the neighbors best-so-far position. Moreover, the store-reset strategy of the global sub-swarm is applied to save computational resource and increase population diversity, aiming to improve the exploration ability of DMS-GPSO at the initial evolutionary stage. At the later evolutionary stage, some favorable particles of the global sub-swarm stored in an archive are combined with particles in the DMS sub-swarms as a single population to search for optimal solutions, intending to enhance the exploitation ability. The comparison results between DMS-GPSO and other 7 peer algorithms on the CEC2013 test suite demonstrate that DMS-GPSO can avoid the premature convergence when solving multimodal problems, and yields more effective performance in complex problems.

关键词： particle swarm optimization dynamic multi-swarm strategy continuous optimization problems

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Study on Cluster-Structured Spiral optimization

Study on Cluster-Structured Spiral Optimization

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Suzuki, Kimihiro Tamura, Kenichi Yasuda, Keiichiro Tokyo Metropolitan Univ Dept Elect & Elect Engn 1-1 Minamiosawa Hachioji Tokyo 1920397 Japan

ISBN: (纸本)9781479938407

In recent years, the authors proposed a new multipoint metaheuristics, called Spiral optimization (SPO), based on analogy of spiral phenomena for continuous optimization problems. The search points moving toward the common center with logarithmic spiral trajectories can find better solutions. In this paper, we propose a Cluster-structured SPO that aims to enhance diversification property to search the solution space more globally than the original SPO. The effectiveness and search performance of the proposed method are confirmed through simulation for typical benchmark functions.

关键词： optimisation search problems SPO benchmark functions cluster-structured spiral optimization continuous optimization problems logarithmic spiral trajectory multipoint metaheuristics search points spiral phenomena Benchmark testing Convergence Genetic algorithms optimization Search problems Spirals Trajectory

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Solving the shortest-path problem in logistics distribution for vehicle-routing optimization 12

Solving the shortest-path problem in logistics distribution ...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Jia, Baoxian Wang, Yong Yuan, Jinhai Li, Junqing Liaocheng Univ Coll Comp Sci Liaocheng 252059 Peoples R China Dongbei Univ Coll Comp Sci Shenyang 110006 Peoples R China

ISBN: (纸本)9781509040933

The classical harmony search (HS) algorithm has been improved. The algorithm effectively prevents it from becoming trapped by a local optimal solution. It allows to be used to deale the shortest-path issue. To solve the shortest route problem, we improved the classical harmony algorithm and seek the optimal solution in the global scope. Second, by the application of the dynamic priority value encoding scheme, we constructed the path based on the priority value of correspondent nodes of variables in the harmonic vector. Through the iterative update of the harmony memory banks, we finally obtained the shortest route. Finally the simulation experiments show us that the proposed algorithm is better than PSO algorithm and HS algorithm on the performance.

关键词： harmony search algorithm continuous optimization problems constrained optimization problems penalty function model shortest-path problem

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An improved bat algorithm hybridized with extremal optimization and Boltzmann selection

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 175卷 114812-114812页

作者： Chen, Min-Rong Huang, Yi-Yuan Zeng, Guo-Qiang Lu, Kang-Di Yang, Liu-Qing South China Normal Univ Sch Comp Sci Guangzhou 510631 Peoples R China Jinan Univ Coll Cyber Secur Guangzhou 510632 Peoples R China Zhejiang Univ Inst Cyber Syst & Control Hangzhou 310018 Peoples R China

As a meta-heuristic algorithm, bat algorithm (BA) is based on the characteristics of bat-based echolocation and has been widely used in various aspects of optimization problems since it appeared. However, the original BA still has many shortcomings, such as insufficient local search ability, lack of diversity and poor performance on high-dimensional optimization problems. To overcome these weaknesses, this paper proposes an improved BA with extremal optimization (EO) algorithm (IBA-EO) to improve the performance of BA. In IBA-EO, an improved update strategy is proposed to obtain the solutions generating from the random selected bats to enhance the global search capability. The exploitation ability is improved by EO algorithm with excellent local search capability. Furthermore, Boltzmann selection and a monitor mechanism are employed to keep suitable balance between exploration ability and exploitation ability. To testify the performance of IBA-EO in handling various optimization problems, this study considers four groups of contrast experiments. Extensive simulation results demonstrate that IBA-EO can achieve a strong competitive performance by comparing with other fifteen wellestablished algorithms in terms of accuracy, reliability and statistical tests.

关键词： Bat algorithm Extremal optimization Boltzmann selection strategy continuous optimization problems

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