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8th International Conference on Swarm Intelligence (ICSI)

作者： Luo, Jie Chen, Huiling Wang, Kejie Tong, Changfei Li, Jun Cai, Zhennao Wenzhou Univ Coll Phys & Elect Informat Wenzhou Peoples R China

ISBN: (纸本)9783319618241;9783319618234

Grey wolf optimization (GWO) algorithm is a novel nature-inspired heuristic paradigm. GWO was inspired by grey wolves, which mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. It has exhibited promising performance in many fields. However, GWO algorithm has the drawback of slow convergence and low precision. In order to overcome this drawback, we propose an improved version of GWO enhanced by the Levy-flight strategy, termed as LGWO. Levy-flight strategy was introduced into the GWO to find better solutions when the grey wolves fall into the local optimums. The effectiveness of LGWO has been rigorously evaluated against ten benchmark functions. The experimental results demonstrate that the proposed approach outperforms the other three counterparts.

关键词： Grey wolf optimization function optimization Levy-flight

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Competitive co-evolutionary algorithms can solve function optimization problems

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ARTIFICIAL LIFE AND ROBOTICS 2009年第3期14卷 440-443页

作者： Sato, Tatsuya Arita, Takaya Nagoya Univ Grad Sch Informat Sci Chikusa Ku Furo Cho Nagoya Aichi 4648601 Japan

Competitive co-evolutionary algorithms (CCEAs) have many advantages, but their range of applications has been crucially limited. This study provides a simple, nonproblem-specific framework to extend that range. The framework has two co- evolving populations, one of candidate solutions and one of criteria, in which these populations competitively co-evolve with each other. The framework aims to avoid candidate solutions getting stuck in a local optimum by changing the fitness landscape dynamically. Moreover, the framework has a mechanism which will establish and maintain a proper arms race. We have conducted experiments on two function optimization problems, the 1-dimensional function maximization problem and the Rastrigin function minimization problem, in order to investigate the basic properties of the framework. The results of the experiments showed that a CCEA achieves a performance which is comparable to that of a GA.

关键词： Competitive co-evolutionary algorithm Genetic algorithm Evolutionary computation function optimization Artificial evolution

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A high-efficiency hybrid evolutionary algorithm for solving function optimization problem

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11th Joint International Computer Conference (JICC 2005)

作者： Dai, GM Zhan, W China Univ Geosci Sch Comp Wuhan 430074 Peoples R China

ISBN: (纸本)9812565329

Based on the GUO's Algorithm a high-efficiently hybrid evolutionary algorithm is proposed. The new algorithm has two main characteristics: first, introduce the Gauss mutation operator of Evolution Strategies (ES);second, introduce variable searching subspace. In order to avoid premature of population, the Gauss mutation operator is used;at the same time, for accelerating convergence, the searching subspace can be reduced automatically when the population's evolutionary value is very close to the global best value of the population. Numerical experiments show that the new algorithm is high-efficiency and the precision of results is very high, at the same time, the experiments' results of several test functions exceed the best value recoded in the references.

关键词： hybrid evolutionary algorithm Gauss mutation operator variable searching subspace function optimization

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The LEM3 implementation of learnable evolution model and its testing on complex function optimization problems 06

The LEM3 implementation of learnable evolution model and its...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Wojtusiak, Janusz Michalski, Ryszard S. George Mason Univ Fairfax VA 22030 USA

ISBN: (纸本)9781595931863

Learnable Evolution Model (LEM) is a form of non-Darwinian evolutionary computation that employs machine learning to guide evolutionary processes. Its main novelty are new type of operators for creating new individuals, specifically, hypothesis generation, which learns rules indicating subareas in the search space that likely contain the optimum, and hypothesis instantiation, which populates these subspaces with new individuals. This paper briefly describes the newest and most advanced implementation of learnable evolution, LEM3, its novel features, and results from its comparison with a conventional, Darwinian-type evolutionary computation program (EA), a cultural evolution 'algorithm (CA), and the estimation of distribution algorithm (EDA) on selected function optimization problems (with the number of variables varying up to 1000). In every experiment, LEM3 outperformed the compared programs in terms of the evolution length (the number of fitness evaluations needed to achieved a desired solution), sometimes more than by one order of magnitude.

关键词： evolutionary computation learnable evolution model function optimization machine learning

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Whale Swarm Algorithm for function optimization 1

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13th International Conference on Intelligent Computing (ICIC)

作者： Zeng, Bing Gao, Liang Li, Xinyu Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China

ISBN: (数字)9783319633091

ISBN: (纸本)9783319633091;9783319633084

Increasing nature-inspired metaheuristic algorithms are applied to solving the real-world optimization problems, as they have some advantages over the classical methods of numerical optimization. This paper proposes a new nature-inspired metaheuristic called Whale Swarm Algorithm for function optimization, which is inspired from the whales' behavior of communicating with each other via ultrasound for hunting. The proposed Whale Swarm Algorithm is compared with several popular metaheuristic algorithms on comprehensive performance metrics. According to the experimental results, Whale Swarm Algorithm has a quite competitive performance when compared with other algorithms.

关键词： Whale Swarm Algorithm Ultrasound Nature-inspired Metaheuristic function optimization

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An Improved Lightning Attachment Procedure optimization Algorithm for function optimization 10

An Improved Lightning Attachment Procedure Optimization Algo...

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10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems - Technology and Applications (IDAACS)

作者： Sun, Shuang Ye, Zhiwei Sun, Yiheng Zhan, Sikai Yu, Han Yao, Quanfeng Hubei Univ Technol Sch Comp Sci Wuhan 430068 Peoples R China Wuhan Fiberhome Tech Serv Co LTD Wuhan Peoples R China

ISBN: (纸本)9781728140698

The hybridization of different meta-heuristic algorithms is for expanding the synergies of a single optimization method used alone and achieving a better optimum search performance. In this work, we proposed a hybrid optimization method combining lightning attachment procedure optimization algorithm (LAPO) and the gravitational search algorithm (GSA), and applied to the function optimization. In order to integrate the excellent exploitation performance of LAPO with the great exploration capability of the GSA to synthesize the strength of each algorithm, we utilized series hybrid mode and some benchmark test functions were employed for evaluating and comparing the performance with the standard mode. Meanwhile, the most commonly used algorithm, particle swarm optimization algorithm is also used for contrast. The experiment results show that the hybrid algorithm obtains better time efficiency and convergence capacity, also have a great ability to avoid local optimums.

关键词： hybrid optimization lightning attachment procedure optimization algorithm gravitational search algorithm function optimization

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Dynamic Clonal and Chaos-Mutation Evolutionary Algorithm for function optimization

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3rd International Conference on Intelligence Computation and Applications

作者： Yang, Ming Guan, Jing China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Wuhan 430074 Peoples R China

ISBN: (纸本)9783540921363

This paper introduced a dynamic-clone and chaos-mutation evolutionary algorithm (DCCM-EA), which employs dynamic clone and chaos Mutation methods, for function optimization. The number of clone is direct proportion to "affinity" between individuals and the chaos sequence can search the points all over the solution space, so DCCM-EA can make all points get equal evolutionary probability, to get the global optimal solution most possibly. In the experiments, taking 23 benchmark functions to test, it can be seen that DCCM-EA if effective for solving function optimization.

关键词： function optimization evolutionary algorithm dynamic clone chaos mutation

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A hybrid Particle Swarm optimization algorithm for function optimization

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EvoWorkshops 2008

作者： Sevkli, Zulal Sevilgen, F. Erdogan Fatih Univ Dept Comp Engn Buyukcekmece Istanbul Turkey Gebze Inst Technol Dept Comp Engn Gebze Turkey

ISBN: (纸本)9783540787600

In this paper, a new variation of Particle Swarm optimization (PSO) based on hybridization with Reduced Variable Neighborhood Search (RVNS) is proposed. In our method, general flow of PSO is preserved. However, to rectify premature convergence problem of PSO and to improve its exploration capability, the best particle in the swarm is randomly re-initiated. To enhance exploitation mechanism, RVNS is employed as a local search method for these particles. Experimental results on standard benchmark problems show sign of considerable improvement over the standard PSO algorithm.

关键词： Particle Swarm optimization reduced variable neighborhood search function optimization

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A Novel Variable-Boundary-Coded Quantum Genetic Algorithm for function optimization

A Novel Variable-Boundary-Coded Quantum Genetic Algorithm fo...

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8th IEEE International Conference on Dependable, Autonomic and Secure Computing

作者： Xiong, Hegen Tang, Qiuhua Xiong, Kai Wuhan Univ Sci & Technol Coll Mech Automat Engn Wuhan Peoples R China Univ Elect Sci & Technol China Sch Automat Engn Chengdu Peoples R China

ISBN: (纸本)9780769539294

Quantum genetic algorithm is a recently proposed new optimization algorithm combining quantum algorithm with genetic algorithm. It characterizes good population diversity, rapid convergence and good global search capability and so attracts serious and wide attentions. This paper proposes a novel quantum genetic algorithm called variable-boundary-coded quantum genetic algorithm (vbQGA) in which qubit chromosomes are collapsed into variable-boundary-coded chromosomes instead of binary-coded chromosomes. In this way we can obtain much shorter chromosome strings. The method of encoding and decoding of chromosome is first described before a new adaptive selection scheme for angle parameters used for rotation gate is put forward based on the core ideas and principles of quantum computation. Eight typical functions are selected to optimize to evaluate the effectiveness and performance of vbQGA against standard genetic algorithm (sGA) and genetic quantum algorithm (GQA) proposed by Han in [6]. The results show that vbQGA is significantly superior to sGA in all aspects and outperforms GQA in robustness and solving velocity, especially for multidimensional and complicated functions.

关键词： quantum genetic algorithm variable-boundary coding function optimization adaptive quantum rotation gate

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Promising search regions of crossover operators for function optimization

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20th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems

作者： Someya, Hiroshi Inst Stat Math Minato Ku Tokyo 106 Japan

ISBN: (纸本)9783540733225

Performance of a genetic algorithm for function optimization, often appeared in real-world applications, depends on its crossover operator strongly. Existing crossover operators are designed for intensive search in certain promising regions. This paper, first, discusses where the promising search regions are on the basis of some assumptions about the fitness landscapes of objective functions and those about a state of a population, and this discussion reveals that existing crossover operators intensively search some of the promising regions but not all of them. Then, this paper designs a new crossover operator for searching all of the promising regions. For utilizing the advantageous features of this crossover operator, a new selection model considering characteristic preservation is also introduced. Several experiments have shown the proposed method has worked effectively on various test functions.

关键词： genetic algorithms crossover operators function optimization

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