Description: 一种新的随机优化技术:基于群落动态分配的粒子群优化算法(Community Dynamic Assignation-based Particle Swarm Optimization,CDAPSO)。新算法通过动态改变粒子群体的组织结构和分配特征来维持寻优过程中启发信息的多样性,从而使其全局收搜索能力得到了显著提高,并且能够有效避免早熟收敛问题。-a new stochastic optimization techniques : Community-based dynamic allocation of PSO algorithm (Dynamic Community Assigna tion-based Particle Swarm Optimization, CDAPSO). New Algorithm for dynamic change particle group's organizational structure and distribution to maintain the optimization process enlightening information diversity, thus the overall admission search capability has been significantly improved, and can effectively prevent premature convergence. Platform: |
Size: 6744 |
Author:wuyuqian |
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Description: Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters. Platform: |
Size: 365745 |
Author:sky |
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Description: Swarm intelligence algorithms are based on natural
behaviors. Particle swarm optimization (PSO) is a
stochastic search and optimization tool. Changes in the
PSO parameters, namely the inertia weight and the
cognitive and social acceleration constants, affect the
performance of the search process. This paper presents a
novel method to dynamically change the values of these
parameters during the search. Adaptive critic design
(ACD) has been applied for dynamically changing the
values of the PSO parameters.-Swarm intelligence algorithms are based on naturalbehaviors. Particle swarm optimization (PSO) is astochastic search and optimization tool. Changes in thePSO parameters, namely the inertia weight and thecognitive and social acceleration constants, affect theperformance of the search process. This paper presents anovel method to dynamically change the values of theseparameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing thevalues of the PSO parameters. Platform: |
Size: 365568 |
Author:sky |
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Description: < MATLAB遗传算法工具箱及应用>>介绍了如何在MATLAB中完成遗传算法的应用。遗传算法[Genetic Arithmatic，简称GA]是以自然选择和遗传理论为基础，将生物进化过程中适者生存规则与群体内部染色体的随机信息交换机制相结合的高效全局寻优搜索算法。GA摒弃传统的搜索方式，模拟自然界生物进化过程，采用人工进化的方式对目标空间进行随机优化搜索。MATLAB是MATHWORKS公司的一套高性能的数值计算和可视化软件。MATLAB遗传算法工具箱及应用
-Genetic Algorithm [Genetic Arithmatic, referred to as GA] is based on natural selection and genetic theory, the process of biological evolution survival of the fittest rules and groups of chromosomes within the clearing-house mechanism of the random combination of efficient global optimization search algorithm. GA to abandon the traditional search methods to simulate the process of natural biological evolution, artificial evolution approach on the target stochastic optimization search space. Mathworks Inc. MATLAB is a high-performance numerical computation and visualization software. MATLAB genetic algorithm toolbox and its application Platform: |
Size: 6146048 |
Author:吴晓晖 |
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Description: 遗传算法是一种模拟生物进化机制的随机全局优化搜索方法，具有很强的全局优化能力及鲁棒性。遗传算法属于直接搜索法，对适应函数基本无限制，既不要求连续，也不要求函数可微，而且不需要初始信息可以寻求全局最优解克服了单纯形算法初始条件影响大，易陷入局部最小等缺点，操作方便，速度快，不需要复杂的规则，且可用于多目标寻优，在解空间进行高效启发式搜索，可以提高运算速度。-The genetic algorithm is one simulation organic evolution mechanism stochastic global optimization reconnaissance method, has the very strong global optimization ability and robustness. The genetic algorithm belongs to the direct search method, to adapts the function basically unlimited, also does not request continual, also does not request the function differentiable, moreover did not need the initial information to be possible to seek the globally optimal solution to overcome the simplex algorithm initial condition to affect in a big way, easy to fall into is partially smallest and so on shortcomings, the ease of operation, the speed is quick, did not need the complex rule, and might use in the multi-objective optimizations, carried on the highly effective heuristic search in the solution space, might raise the operating speed. Platform: |
Size: 3072 |
Author:大同小异 |
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Description: 《Evolutionary Algorithms for Solving Multi-Objective Problems》这是一本有关多目标进化的非常值得一看的书，里面有测试标准，测试函数等内容-The solving of multi-objective problems (MOPs) has been a continuing effort by humans in many diverse areas, including computer science, engineering, economics, finance, industry, physics, chemistry, and ecology, among others. Many powerful and deterministic and stochastic techniques for solving these large dimensional optimization problems have risen out of operations research, decision science, engineering, computer science and other related disciplines. The explosion in computing power continues to arouse extraordinary interest in stochastic search algorithms that require high computational speed and very large memories. A generic stochastic approach is that of evolutionary algorithms (EA). Such algorithms have been demonstrated to be very powerful and generally applicable for solving different single objective problems. Their fundamental algorithmic structures can also be applied to solving many multi-objective problems. In this book, the various features of multi-objective evoluti Platform: |
Size: 12280832 |
Author:海 |
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Description: JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques.-JavaGenes is an evolutionary software system written in Java. It implements the genetic algorithm, simulated annealing, stochastic hill climbing and other search techniques. Platform: |
Size: 11746304 |
Author:jim |
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Description: This a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. -This is a demonstration of how to find a minimum of a non-smooth
objective function using the Genetic Algorithm (GA) function in the
Genetic Algorithm and Direct Search Toolbox. Traditional derivative-based
optimization methods, like those found in the Optimization Toolbox, are
fast and accurate for many types of optimization problems. These methods
are designed to solve smooth , i.e., continuous and differentiable,
minimization problems, as they use derivatives to determine the direction
of descent. While using derivatives makes these methods fast and
accurate, they often are not effective when problems lack smoothness,
e.g., problems with discontinuous, non-differentiable, or stochastic
objective functions. When faced with solving such non-smooth problems,
methods like the genetic algorithm or the more recently developed pattern
search methods, both found in the Genetic Algorithm and Direct Search
Toolbox, are effective alternatives. Platform: |
Size: 18432 |
Author:gao |
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Description: 模拟退火算法是基于蒙特卡罗迭代求解法的一种启发式随机搜索过程。本文给出了该算法的详细介绍和伪代码。-Monte-Carlo simulated annealing algorithm is based on a heuristic iterative method for solving stochastic search process. This paper gives a detailed description of the algorithm and pseudo code. Platform: |
Size: 5120 |
Author:Royal |
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Description: ntroduction to Stochastic Search and Optimization, 2003
This program runs a GA with real-number coding. Elitism is used
and the mutation operator is simply the addition of a Gaussian
random vector to the non-elite elements.
The user is expected to set a variable expect_fn representing the
expected number of function evaluations allowed.-ntroduction to Stochastic Search and Optimization, 2003
This program runs a GA with real-number coding. Elitism is used
and the mutation operator is simply the addition of a Gaussian
random vector to the non-elite elements.
The user is expected to set a variable expect_fn representing the
expected number of function evaluations allowed. Platform: |
Size: 3072 |
Author:shahnaz |
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Description: Differential evolution (DE) is an efficient and powerful population-based stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DEin solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trial-and-error scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a self-adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions. Platform: |
Size: 135168 |
Author:Blue |
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Description: ：仿生优化算法是模拟自然界中生物行为的随机搜索算法，可以用来解决现实中的许多优化问题。简要介绍了目前比
较流行的四种新型仿生优化算法(蚁群算法、微粒群算法、人工免疫算法以及人工鱼群算法)的基本原理；然后深入分析了这
些仿生优化算法的异同之处-Bionic optimization algorithms aye stochastic search methods that mimic the natural biological behavior
of species．They age mainly applied to solve various optimization problems．This paper proposes the formulation of
four recent biology—based algorithms：an t colony algorithm，particle swarm optimization algorithm，artificial immune
algorithm，and artificial fish—swarln algorithm Platform: |
Size: 293888 |
Author:zhangyan |
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Description: In the early 1970s, genetic algorithms initially proposed by Holland, his colleagues, and his students at university Michigan as stochastic search techniques based on the mechanism of natural selection and natural genetics.-In the early 1970s, genetic algorithms initially proposed by Holland, his colleagues, and his students at university Michigan as stochastic search techniques based on the mechanism of natural selection and natural genetics. Platform: |
Size: 23410688 |
Author:Tran |
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Description: 遗传算法是一种借鉴生物界自然选择和自然遗传机制的随机搜索算法。它简单、鲁棒性好，具有自组织
性、自适应性、自学习性，其本质是一种高效、并行、全局搜索的方法，它能在搜索过程中自动获取和积累有
关搜索空间的知识，并自适应地控制搜索过程以求得最优解，
-Genetic algorithm is a stochastic search algorithm which is based on natural selection and natural genetic mechanism. It is simple, robust and self-organizing
Self-adaptability, self-learning, and its essence is an efficient, parallel, global search method, it can automatically search and accumulate in the process of
Off search space knowledge, and adaptive control of the search process in order to obtain the optimal solution, Platform: |
Size: 4096 |
Author:shishi |
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Search - Stochastic Search