基于文化差分算法的无约束优化问题研究

无约束优化问题是一个较古老的数学问题,随着智能计算科学的发展,解决此类优化问题,除了使用经典数学方法外,还可以使用智能化方法进行寻优。本文使用经典文化算法双层进化结构,将差分进化算法引入信度空间的更新操作,实现差分进化算法在进化过程中形势知识更新,保证了种群合理的进化方向,从而引导种群空间中个体进行有效进化,使得寻优能力有所提高,并选用6个基准函数对改进前后的算法进行测试,实验表明优化性能得到了提高。     

基于差分进化算法的飞行控制律评估

针对传统文化算法进化后期收敛速度慢和差分进化算法在进化过程中缺乏对知识有效利用的问题,提出一种新的文化差分进化算法.该算法以文化算法为框架,将差分进化算法的变异、交叉和选择作为种群空间的进化操作,并通过信念空间的知识指导种群进化.根据飞行品质规范选取迎角响应限制准则,以飞机模型ADMIRE为研究对象,利用该算法对存在不确定条件下的飞行控制律进行非线性评估,克服传统网格评估方法在工程应用中的不足.仿真结果表明,与改进差分进化算法相比,文化差分进化算法在全飞行包线范围内找出最坏的不确定参数组合,具有更高的可靠性和效率.     

文化算法应用研究

文化算法模拟了人类社会文化的进化过程,在进化的群体空间上建立动态的信仰空间,用来指导数据的搜索。介绍了文化算法的原理和流程,根据问题的特点对文化算法进行了分类,并给出了不同类型问题的求解步骤。根据对文化算法进行的仿真实验结果,得出该算法不仅收敛速度快,而且所得结果更优,表明文化算法是一种全局优化搜索的进化算法,具有广泛的应用前景。     

背包问题的知识进化算法

知识进化算法是在分析知识进化机制基础上提出的一种新型优化算法。该文根据0-1背包问题的特点,提出用于求该问题的知识进化算法方案,阐明算法的具体实现过程。通过对其他文献中仿真实例的计算和结果比较,表明应用该算法求解背包问题取得了良好的效果。该算法同样可以应用于其他组合优化问题。     

求解约束优化问题的文化算法研究

文化算法的主要思想是明确地从进化种群中获得求解问题的知识 (即信念) 并用于指导搜索过程. 本文提出了一种基于多层信念空间的文化算法, 该算法通过对多层信念空间的择优选用将提取的知识用于提高进化计算性能来解决约束优化问题. 应用实例表明该算法具有较好的结果和较少的计算量.     

进化迁移优化算法综述

进化算法是模拟自然界生物进化的启发式算法,具有良好的搜索能力和灵活性且广泛用于复杂优化问题的求解,但在求解过程中默认问题先验知识为零,然而由于问题很少孤立存在,解决单一任务积累的经验可迁移至其他相关任务。进化迁移优化算法利用相关领域的知识学习和迁移,实现了更好的优化效率和性能。介绍进化迁移优化算法的基本分类,从源任务选择、知识迁移、缩小搜索空间差异、进化算法搜索、进化资源分配等5个角度出发对主流进化迁移优化算法的核心策略和优劣势进行梳理和分析。通过中国知网和WOS平台对2014年至2021年的进化迁移优化相关文献进行检索,运用知识图谱进行数据挖掘、信息处理、知识计量和图形绘制,根据进化迁移优化的发展趋势和经验分析总结了其面临的主要挑战和未来研究方向。     

基于蚁群算法的PID参数寻优

蚁群算法是一种新型的模拟进化算法,该算法用于离散空间问题的求解取得了较好的结果.该文将蚁群算法引入连续空间,研究了基于蚁群算法的PID参数优化问题,给出了仿真实例,结果表明蚁群算法用于解决连续空间优化问题是可行且有效的.蚁群算法具有较好的鲁棒性,它采用分布式计算,具有本质并行性.     

基于文化算法和改进差分进化算法的混合算法

改进差分进化算法不能有效利用进化过程中的知识,传统文化算法进化后期收敛速度较慢。针对这些问题提出一种基于文化算法和改进差分进化算法的混合算法,并将这一算法应用于约束求解问题。对基准函数和丁烯烷化生产调度问题进行仿真,结果表明该混合算法具有较好的实用性和稳健性,在寻优效率和优化结果方面都优于与之比较的算法,并降低了计算量。     

知识进化算法*

根据卡尔·波普尔的知识进化理论,建立了知识进化算法的基本框架,详细地阐述了该算法的原理和具体实施方案。知识进化算法主要由一个知识空间和多个群体空间组成,群体空间根据知识的指导通过选择操作不断地提出新的假说,并通过猜测操作和反驳操作与知识空间协同进化来不断地提高真理度（即不断地接近问题的最优解）。为了验证方法的有效性,选取了来自其他文献中的五个经典测试函数作为算法的测试对象,通过对其他文献中的仿真实例进行计算和结果比较,证明了算法的可行性和有效性。     

基于文化算法的生产过程建模法

文化算法是一种模拟文化进化过程的优化算法,它由基于个体和群体特性的信念空间和基于个体行为的种群空间组成,为进化搜索机制和知识存储的结合提供一个构架。建立基于生产过程输入输出数据的统计模型时,参数估计是其中的关键,文化算法为此提供了有效途径。本文研究用文化算法实现多变量优化的具体步骤、算法和关键环节的实施。建立裂解炉裂解深度的神经网络模型,并用文化算法优化网络参数,实验表明,文化算法比标准遗传算法搜索性能更优,搜索时间更快,同时得到了满意的裂解深度模型。     

进化算法研究进展

进化算法是一类借鉴生物界自然选择和自然遗传机制的随机搜索算法，主要包括遗传算法，（ｇｅｎｅｒｉｃａｌｇｏｒｉｔｈｍｓ，简记为ＧＡｓ）、进化规划（ｅｖｏｌｕｔｉｏｎａｒｙｐｒｏｇｒａｍｍｉｎｇ，简记为ＥＰ）和进化策略（ｅｖｏｌｕｔｉｏｎａｒｙｓｔｒａｔｅｇｉｅｓ，简记为ＥＳｓ），它们可以用解决优化和机器学习等问题，进化算法的两个主要特点中群体搜索策略及群体中个体之间的信息交换，进化算法不依赖于梯度信     

并行遗传算法与神经网络,模糊系统的结合

遗传算法是模拟自然界生物进化过程的计算模型。本文介绍了并行遗传算法的不同分类及不同并行策略，又将遗传算法分别与神经网络、模糊系统结合起来进行并行处理，并在曙光１０００系统上实现。算法分析表明，并行遗传算法可以有效地提高收敛速度。     

On three new approaches to handle constraints within evolution strategies

Evolutionary algorithms with a self-adaptive step control mechanism like evolution strategies (ES) often suffer from premature fitness stagnation on constrained numerical optimization problems. When the optimum lies on the constraint boundary or even in a vertex of the feasible search space, a disadvantageous success probability results in premature step size reduction. We introduce three new constraint-handling methods for ES on constrained continuous search spaces. The death penalty step control evolution strategy (DSES) is based on the controlled reduction of a minimum step size depending on the distance to the infeasible search space. The two sexes evolution strategy (TSES) is inspired by the biological concept of sexual selection and pairing. At last, the nested angle evolution strategy (NAES) is an approach in which the angles of the correlated mutation of the inner ES are adapted by the outer ES. All methods are experimentally evaluated on four selected test problems and compared with existing penalty-based constraint-handling methods.     

Leveraging saving-based algorithms by master-slave genetic algorithms

Saving-based algorithms are commonly used as inner mechanisms of efficient heuristic construction procedures. We present a general mechanism for enhancing the effectiveness of such heuristics based on a two-level genetic algorithm. The higher-level algorithm searches in the space of possible merge lists which are then used by the lower-level saving-based algorithm to build the solution. We describe the general framework and we illustrate its application to three hard combinatorial problems. Experimental results on three hard combinatorial optimization problems show that the approach is very effective and it enables considerable enhancement of the performance of saving-based algorithms.     

硬件进化中演化算法的研究及应用

详细介绍了硬件进化的概念,硬件进化的原理与实现思想,遗传算法与蚁群算法动态融合的基本原理,融合后算法中遗传算法及蚁群算法规则.融合过程中遗传算法与蚁群算法动态衔接问题以及融合后的算法在硬件进化中的应用过程.最后,分析了通过该算法进化后硬件的进化应用前景.     

Tackling Real-Coded Genetic Algorithms: Operators and Tools for Behavioural Analysis

Genetic algorithms play a significant role, as search techniques forhandling complex spaces, in many fields such as artificial intelligence, engineering, robotic, etc. Genetic algorithms are based on the underlying genetic process in biological organisms and on the naturalevolution principles of populations. These algorithms process apopulation of chromosomes, which represent search space solutions,with three operations: selection, crossover and mutation.Under its initial formulation, the search space solutions are coded using the binary alphabet. However, the good properties related with these algorithms do not stem from the use of this alphabet; other coding types have been considered for the representation issue, such as real coding, which would seem particularly natural when tackling optimization problems of parameters with variables in continuous domains. In this paper we review the features of real-coded genetic algorithms. Different models of genetic operators and some mechanisms available for studying the behaviour of this type of genetic algorithms are revised and compared.     

A study on scale factor in distributed differential evolution

This paper proposes the employment of multiple scale factor values within distributed differential evolution structures. Four different scale factor schemes are proposed, tested, compared and analyzed. Two schemes simply employ multiple scale factor values and two also include an update logic during the evolution. The four schemes have been integrated for comparison within three recently proposed distributed differential evolution structures and tested on several various test problems.Numerical results show that, on average, the employment of multiple scale factors is beneficial since in most cases it leads to significant improvements in performance with respect to standard distributed algorithms. Although proper choice of a scale factor scheme appears to be dependent on the distributed structure, any of the proposed simple schemes has proven to significantly improve upon the single scale factor distributed differential evolution algorithms.     

Genetic doping algorithm (GenD): theory and applications

Abstract: This paper describes an evolutionary algorithm, GenD, conceived by Buscema in 1998 at the Centro Ricerche di Scienze della Comunicazione – Semeion in Rome, where it is still successfully used and has been further developed. Unlike classic genetic algorithms, the GenD system maintains an inner instability during evolution, presenting a continuous evolution of the evolution and a natural increase in biodiversity during the progress of the algorithm. The theory which leads to defining the GenD system is outlined. Specific characteristics of GenD, such as the definition of a species‐health aware evolutionary law, the use of genetic operators and the adoption of a structured organization of individuals (tribes), are described. In order to measure GenD capabilities, we investigated also different problems, such as that known as the travelling sales person problem, which belongs to the class of full NP problems.     

A robust real-coded evolutionary algorithm with toroidal search space conversion

This paper presents a robust Real-coded evolutionary algorithm. Real-coded evolutionary algorithms (RCEAs), such as real-coded genetic algorithms and evolution strategies, are known as effective methods for function optimization. However, they often fail to find the optimum in case the objective function is multimodal, discrete or high-dimensional. It is also reported that most crossover (or recombination) operators for RCEAs has sampling bias that prevents to find the optimum near the boundary of search space. They like to search the center of search space much more than the other. Therefore, they will not work on functions that have their optima near the boundary of search space. In this paper, we apply two methods, genetic algorithm with search area adaptation (GSA) and toroidal search space conversion (TSC), to the function optimization for improving the robustness of RCEAs. The former method searches adaptively and the latter one removes the sampling bias. Through several experiments, we have confirmed that GSA works adaptively and it shows higher performance, and RCEAs with TSC show effectiveness to find the optima near the boundary of search space.