检索结果-内蒙古大学图书馆

A parallel immune optimization algorithm for numeric function optimization

EVOLUTIONARY INTELLIGENCE 2008年第3期1卷 171-185页

作者： Lau, Henry Y. K. Tsang, Wilburn W. P. Univ Hong Kong Dept Ind & Mfg Syst Engn Pokfulam Rd Hong Kong Hong Kong Peoples R China

Immune optimization algorithms show good performance in obtaining optimal solutions especially in dealing with numeric optimization problems where such solutions are often difficult to determine by traditional techniques. This article presents the parallel suppression control algorithm (PSCA), a parallel algorithm for optimization based on artificial immune systems (AIS). PSCA is implemented in a parallel platform where the corresponding population of antibodies is partitioned into subpopulations that are distributed among the processes. Each process executes the immunity-based algorithm for optimizing its subpopulation. In the process of evolving the solutions, the activities of antibodies and the activities of the computation agents are regulated by the general suppression control framework (GSCF) which maintains and controls the interactions between the populations and processes. The proposed algorithm is evaluated with benchmark problems, and its performance is measured and compared with other conventional optimization approaches.

关键词： Artificial immune systems Immune optimization algorithm function optimization Parallel implementation

来源：评论

学校读者我要写书评

暂无评论

Improved fireworks algorithm with information exchange for function optimization

引用

KNOWLEDGE-BASED SYSTEMS 2019年第Jan.1期163卷 82-90页

作者： Cheng, Rong Bai, Yanping Zhao, Yu Tan, Xiuhui Xu, Ting North Univ China Sch Sci Taiyuan 030051 Shanxi Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Shanxi Peoples R China

The fireworks algorithm, which is inspired by the explosion of fireworks, is a new swarm-based meta heuristic algorithm for global optimization. This work proposes an improved fireworks optimization algorithm (IFWA) based on the enhanced fireworks algorithm (EFWA). Three aspects of improvement are presented after an analysis of the drawbacks of EFWA. These improvements are a new explosion scheme, GS-Gaussian explosion operator, and deep information exchange strategy. The proposed IFWA is tested on 23 benchmark function optimization problems and a real engineering problem, namely, optimal controller design for automotive active suspension. optimization results prove that IFWA has competitive advantage compared with EFWA and other popular meta-heuristic algorithms and demonstrates the potential to solve real problems effectively. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Fireworks algorithm Swarm intelligence function optimization LQR controller

来源：评论

学校读者我要写书评

暂无评论

Particle swarm optimization for function optimization in noisy environment

引用

APPLIED MATHEMATICS AND COMPUTATION 2006年第2期181卷 908-919页

作者： Pan, Hui Wang, Ling Liu, Bo Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Shandong Univ Weihai Sch Informat Engn Weihai 264209 Peoples R China

As a novel evolutionary searching technique, particle swarm optimization (PSO) has gained wide research and effective applications in the field of function optimization. However, to the best of our knowledge, most studies based on PSO are aimed at deterministic optimization problems. In this paper, the performance of PSO for function optimization in noisy environment is investigated, and an effective hybrid PSO approach named PSOOHT is proposed. In the PSOOHT, the population-based search mechanism of PSO is applied for well exploration and exploitation, and the optimal computing budget allocation (OCBA) technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good particles. Meanwhile, hypothesis test (HT) is also applied in the hybrid approach to reserve good particles and to maintain the diversity of the swarm as well. Numerical simulations based on several well-known function benchmarks with noise are carried out, and the effect of noise magnitude is also investigated as well. The results and comparisons demonstrate the superiority of PSOOHT in terms of searching quality and robustness. (c) 2006 Elsevier Inc. All rights reserved.

关键词： particle swarm optimization hypothesis test optimal computing budget allocation function optimization noisy environment

来源：评论

学校读者我要写书评

暂无评论

Dynamic small world particle swarm optimizer for function optimization

引用

NATURAL COMPUTING 2018年第4期17卷 901-917页

作者： Vora, Megha Mirnalinee, T. T. Anna Univ SSN Coll Engn Dept Comp Sci & Engn Madras Tamil Nadu India

Performance of particle swarm optimization technique is highly influenced by the population topology. It determines the way in which particles communicate and share information within a swarm. If path length is too small, it implies that a particle communicates with other particles in its close proximity leading to exploitation. On the contrary, if path length is large then the particle interacts with other remote particles leading to exploration. There needs to be a balance between exploration and exploitation and Small world network fits to this need of ours. In this paper, dynamic small world network has been proposed with the objective to have a balanced trade-off between exploration and exploitation. In order to make learning process dynamic linearly decreasing inertia weight has been employed. Experimental study is performed on a set of 23 test functions using different performance evaluation measures. Results obtained are compared with other state of the art techniques demonstrating the effectiveness of the proposed approach.

关键词： Small world Particle swarm optimization function optimization

来源：评论

学校读者我要写书评

暂无评论

A new metaheuristic for numerical function optimization: Vortex Search algorithm

引用

INFORMATION SCIENCES 2015年 293卷 125-145页

作者： Dogan, Berat Olmez, Tamer Istanbul Tech Univ Dept Elect & Commun Engn Istanbul Turkey

In this study, a new single-solution based metaheuristic, namely the Vortex Search (VS) algorithm, is proposed to perform numerical function optimization. The proposed VS algorithm is inspired from the vortex pattern created by the vortical flow of the stirred fluids. To provide a good balance between the explorative and exploitative behavior of a search, the proposed method models its search behavior as a vortex pattern by using an adaptive step size adjustment scheme. The proposed VS algorithm is tested over 50 benchmark mathematical functions and the results are compared to both the single-solution based (Simulated Annealing, SA and Pattern Search, PS) and population-based (Particle Swarm optimization, PSO2011 and Artificial Bee Colony, ABC) algorithms. A Wilcoxon-Signed Rank Test is performed to measure the pair-wise statistical performances of the algorithms, the results of which indicate that the proposed VS algorithm outperforms the SA, PS and ABC algorithms while being competitive with the PSO2011 algorithm. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Metaheuristics Global optimization function optimization Simulated annealing Artificial bee colony Particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

SFO: A Toolbox for Submodular function optimization

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2010年第3期11卷 1141-1144页

作者： Krause, Andreas CALTECH Pasadena CA 91125 USA

In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems. We present SFO, a toolbox for use in MATLAB or Octave that implements algorithms for minimization and maximization of submodular functions. A tutorial script illustrates the application of submodularity to machine learning and AI problems such as feature selection, clustering, inference and optimized information gathering.

关键词： .Structure Learning function optimization SFO Toolbox submodular functions

来源：评论

学校读者我要写书评

暂无评论

A hybrid SA-MFO algorithm for function optimization and engineering design problems

引用

COMPLEX & INTELLIGENT SYSTEMS 2018年第3期4卷 195-212页

作者： Sayed, Gehad Ismail Hassanien, Aboul Ella Cairo Univ Fac Comp & Informat Cairo Egypt SRGE Cairo Egypt

This paper presents a hybrid algorithm based on using moth-flame optimization (MFO) algorithm with simulated annealing (SA), namely (SA-MFO). The proposed SA-MFO algorithm takes the advantages of both algorithms. It takes the ability to escape from local optima mechanism of SA and fast searching and learning mechanism for guiding the generation of candidate solutions of MFO. The proposed SA-MFO algorithm is applied on 23 unconstrained benchmark functions and four well-known constrained engineering problems. The experimental results show the superiority of the proposed algorithm. Moreover, the performance of SA-MFO is compared with well-known and recent meta-heuristic algorithms. The results show competitive results of SA-MFO concerning MFO and other meta-heuristic algorithms.

关键词： Simulated annealing algorithm Moth flame optimization algorithm function optimization Constrained optimization problems

来源：评论

学校读者我要写书评

暂无评论

Grey wolf optimization with momentum for function optimization

引用

ARTIFICIAL LIFE AND ROBOTICS 2021年第3期26卷 304-311页

作者： Muto, Takuya Maeda, Michiharu Fukuoka Inst Technol Fukuoka Japan

Grey wolf optimization (GWO) is one of the metaheuristics, which imitates the hierarchy structure and hunting mechanism in nature. In this paper, we propose an algorithm of grey wolf optimization with momentum (GWOM). The momentum has a movement vector of search point from a previous position to a current position. In the proposed algorithm, the momentum is only applied to wolves with the better fitness than that of the previous step. Therefore, wolves are enhanced probability for searching better positions and tend to avoid to local optima. To show the effectiveness of the proposed algorithm, we compare it to existing algorithms by test functions.

关键词： Metaheuristics Grey wolf optimization Momentum function optimization

来源：评论

学校读者我要写书评

暂无评论

Two-stage ensemble memetic algorithm: function optimization and digital IIR filter design

引用

INFORMATION SCIENCES 2013年 220卷 408-424页

作者： Wang, Yu Li, Bin Weise, Thomas Univ Sci & Technol China Dept Elect Sci & Technol Hefei Anhui Peoples R China

The research on optimal design of infinite-impulse response (IIR) filters based on optimization techniques has gained much attention in recent years. However, due to the limited performance of the applied optimization techniques, the orders of the filters, which can be obtained, are very low in the previous research. Memetic algorithms (MM) are widely recognized to have better convergence capability than their conventional counterparts. However, the universality of the MM, e.g. the ability of solving diverse kinds of digital IIR filter designs, is still limited. In this paper, we design a Two-Stage ensemble Memetic Algorithm (TSMA) framework to more appropriately synthesize the strengths of the evolutionary global search and local search techniques. In the first optimization stage, a competition is held among the candidate local search techniques. Its major idea is to choose the best local search technique and to obtain good initial state. Inheriting the good information of the first stage, the second optimization stage is to implement effective adaptive MA to pursue high-quality solution. The experimental studies presented in this paper contain three aspects: (1) the benefits of the TSMA framework are experimentally investigated by comparing TSMA with its sub-optimizers and recent effective evolutionary algorithms (EM) on 26 test functions;then (2) TSMA is compared with 4 MM on the CEC05 functions to comprehensively show the advantages of TWA;and (3) the TSMA and 6 state-of-the-art algorithms are applied to design high-order digital infinite-impulse response (IIR) filters. The experimental results definitely demonstrate the excellent effectiveness, efficiency and reliability of TSMA on both function optimization and digital IIR filter design tasks. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Digital infinite-impulse response (IIR) filter Memetic computing Evolutionary computation function optimization Two-stage ensemble

来源：评论

学校读者我要写书评

暂无评论

Enhancing Differential Evolution performance with local search for high dimensional function optimization 05

Enhancing Differential Evolution performance with local sear...

引用

Genetic and Evolutionary Computation Conference

作者： Noman, Nasimul Iba, Hitoshi Univ Tokyo Grad Sch Frontier Sci Tokyo Japan

ISBN: (纸本)1595930108

In this paper, we proposed Fittest Individual Refinement (FIR), a crossover based local search method for Differential Evolution (DE). The FIR scheme accelerates DE by enhancing its search capability through exploration of the neighborhood of the best solution in successive generations. The proposed memetic version of DE (augmented by FIR) is expected to obtain an acceptable solution with a lower number of evaluations particularly for higher dimensional functions. Using two different implementations DEfirDE and DEfirSPX we showed that proposed FIR increases the convergence velocity of DE for well known benchmark functions as well as improves the robustness of DE against variation of population. Experiments using multimodal landscape generator showed our proposed algorithms consistently outperformed their parent algorithms. A performance comparison with reported results of well known real coded memetic algorithms is also presented.

关键词： algorithms experimentation performance Differential Evolution local search memetic algorithm function optimization landscape generator

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：