用于约束优化的人工免疫响应进化策略

A Novel Evolutionary Strategy Based on Artificial Immune Response for Constrained Optimizations

公茂果[1] 焦李成[1] 杜海峰[1,2] 马文萍[1]

基于克隆选择学说及生物免疫响应过程的相关机理，探讨一种新的人工免疫系统模型——人工免疫响应，提出用于解决约束优化问题的人工免疫响应进化策略；基于算法网络拓扑结构的分析表明，新算法比传统的进化策略（μ，λ）-ES具有更大的收敛概率．对10个标准测试问题的测试结果表明，与采用随机排序的进化策略和采用动态惩罚函数的进化策略相比，新算法在收敛速度和求解精度上均具有一定的优势．[著者文摘]

Based on the clonal selection theory and mechanisms of biological immune response, a novel artificial immune systems model, Artificial Immune Response （AIR）, is discussed. And based on Artificial Immune Response a novel evolutionary strategy for constrained optimizations is put forward. Analysis of its network framework shows that the new algorithm is convergent with a higher probability than （μ,λ） evolutionary strategy. The experiments on 10 benchmark problems show that when compared with the （μ,λ） evolutionary strategies adopting stochastic ranking method and dynamic penalty function method, the new evolutionary strategy is capable of improving the search performance significantly no matter in convergent speed or precision.[著者文摘]

一种求解有约束优化问题的遗传算法

A Mixed Genetic Algorithm for Solving Constraint Optimization Problems

王丽敏

遗传算法是模拟生物进化机制新发展起来的一种搜索和优化方法，它是基于自然进化机制并且在寻找目标函数或在目标函数附近解决优化问题。遗传算法已在有约束优化问题领域得到应用，并显示出良好的发展前景。本文介绍了一种有约束优化问题的混合遗传算法，并通过实例验证了此方法是可行的和有效的。[著者文摘]

Genetic Algorithms are such search and optimization methods, which have recently developed to stimulate the mechanism of natural evolution.Those methods are based on those of natural evolution, and are powerful in finding the. global or near global optimal solution of optimization problems.It has been used in constraint optimization problems solving and found to be of great use. This paper introduces a mix genetic algorithm for solving constraint optimization problems.and illustrates is feasible and valid.[著者文摘]

约束优化问题的一种投影梯度Lagrange乘子法

Projected Lagrange Multiplier Method for Constrained Optimized Problem

张燕新[1] 曹毅[2]

运用Lagrange乘子法，将一般约束优化转化为仅含等式约束的优化问题，然后就线性与非线性两种情况进行讨论，通过投影梯度法来求解优化子问题。对于线性的情况得到一种可以不用计算初始点的最优化算法，最后的数值算例说明了算法的可行性与有效性。[著者文摘]

Lagrange muhiplier can transfer ordinary constrained optimized problem into one which only includes equality constraints. The paper discusses the linear and nonlinear case respectively and solves the sub problem of optimization by using projected Lagrange multiplier method. For the linear case, a method which dose not use the initialized point in the algorithm is generated. Finally, the numerical examples prove the feasibility and effectiveness of the algorithm.[著者文摘]

带记忆的非单调无约束优化算法的全局收敛性

Global Convergence of a Memory Gradient Method with Nonmonotone Technique for Unconstrained Optimization

陈茜[1] 贺向阳[2]

自从非单调线搜索技巧引入非线性优化后，所得的算法得到了成功的应用与扩展。带记忆的梯度方法经常用来求解无约束优化问题，尤其是大规模的问题。将带记忆梯度法与Wolfe非单调线搜索技巧成功融合到一起得到了新算法。证明了该算法全局收敛。[著者文摘]

The technique of nonmonotone line search has received many successful applications and extensions since it was applied in the nonlinear optimization and the memory gradient method is often used for unconstrained optimization, especially large scale problems. This paper combines the memory gradient method and the nonmonotone wolfe line search and the global convergence is obtained.[著者文摘]

有约束优化中结合Fisher函数的梯度投影算法

Gradient Projection Algorithm with Fisher Function for Constrained Optimization

陈翠玲[1] 赵岩[2] 韦增欣[3]

利用Fisher函数的特性，对求解约束优化问题提出一种新的结合Fisher函数的梯度投影算法。并且证明在通常的假设条件下，该算法在非精确线搜索下具有全局收敛性。[著者文摘]

A new gradient projection algorithm is put forward to solve the constrained optimization problems by using the property of the Fisher function. Furthermore ,under usual assumptions ,the global converge of this algorithm is proved with the inexact line search.[著者文摘]

基于参数方程处理等式约束优化的粒子群算法

Particle swarm optimization algorithm based on parametric equation method to handle equality constraints

刘伟[1] 蔡前凤[1,2] 刘海林[1]

针对目前已有的粒子群优化算法求解有等式约束优化问题时对收敛速度和解的精度的影响,提出了一种新的基于参数方程的粒子群优化算法。它是粒子群在初始化和迭代进化过程中使用求解参数方程的方法处理等式约束设计出的粒子群优化算法。数值实验结果表明,新算法是有效的。它不仅提高了收敛速度和解的精度,而且是一种通用的智能算法。[著者文摘]

To improve the speed of convergence and the precision for most of current particle swarm optimization algorithms being used to solve equality-constrained optimization problems, a new particle swarm optimization algorithm based on parametric equation method is presented. Parametric equation method is taken to keep particles satisfying with equality constraints during the process of population initiation and evolution, and a new particle swarm optimization algorithm is proposed. The experimental results demonstrate that the new particle swarm optimization algorithm is effective. The proposed algorithm not only improves performance of the speed of convergence and the precision, but also is a general, effective and robust method.[著者文摘]