基于DE 和SA 的Memetic 高维全局优化算法

针对高维复杂多模态优化问题,传统的进化算法存在收敛速度慢,求解精度低等缺点,提出一种面向高维优化问题的Memetic全局优化算法。算法通过全局搜索和局部搜索结合的混合搜索策略,采用多模式并行差分进化算法进行全局搜索,基于高斯分布估计的模拟退火算法进行局部搜索。改进后的Memetic算法不仅继承了差分进化算法能发现全局最优解的优点,而且能大幅度提高搜索效率。最后,通过对4个高维多峰值Benchmark函数进行仿真实验,实验结果表明本文算法有效提高了算法的收敛速度和求解精度。     

混合量子差分进化算法及应用

量子进化算法基于量子旋转门更新量子比特状态影响了算法搜索性能．提出一种差分进化（DE）与和声搜索（Hs）相结合更新量子比特状态的混合量子差分进化算法（HQDE）．该方法采用实数量子角形式编码染色体,设计一种由差分进化计算更新量子位状态的量子差分进化算法（QDE）和一种由和声搜索更新量子位状态的量子和声搜索（QHS）,并相互机制融合,采用两种不同进化策略共同作用产生种群新量子个体以克服常规算法中早熟及收敛速度慢等缺陷;在此基础上,算法还引入量子非门算子对当前最劣个体以一定概率选中的量子比特位进行变异操作增强算法跳出局部最优解能力．理论分析证明该算法收敛于全局最优解．0／1背包问题及旅行商问题实例测试结果验证了该方法有效性．     

求解高维多模优化问题的正交小生境自适应差分演化算法

针对传统优化算法在求解高维多模态优化问题时存在收敛速度慢、求解精度低的问题,提出一种基于正交设计与小生境精英策略的自适应差分进化算法ONDE。首先利用正交表产生初始种群,然后采用小生境精英策略来产生小生境种群(NP),并用小生境种群更新精英个体;接着应用拥挤裁剪避免种群陷入局部搜索,最后利用自适应差分变异算子改进了差分进化(DE)算法。通过对7个benchmark函数仿真验证,实验结果表明,算法在收敛速度、求解精度和稳定性方面都有较大优势。     

一种新的蛋白质结构预测多模态优化算法

针对现阶段药物设计中对于蛋白质结构多模态的需求,提出了一种基于排挤差分进化策略的多模态优化算法.为了降低蛋白质构象空间求解的复杂度,算法采用能量极小化过程,有效缩小了可行域的搜索空间;同时,为了有效地平衡多模态优化问题的局部收敛性和模态多样性,在排挤差分进化算法的框架下,在保证算法收敛速度的前提下,算法采用空间局部性原理,同时随机选取不同交叉策略的集结思想又有效改善了种群的多样性.以脑啡肽为例,算法不仅得到了其全局最稳定结构,还获得了一系列局部最优结构.     

动态小生境半径两阶段多模态差分进化算法

针对多模态优化问题, 提出一种动态小生境半径两阶段多模态差分进化算法. 基于构象空间退火思想, 设计一种两阶段退火策略来动态调整小生境半径, 并根据退火过程将整个优化过程分为两个阶段. 在第1 阶段, 通过差分限制变异策略生成高质量的新个体来维持种群的多样性, 促进多模收敛; 在第2 阶段, 利用种子邻近变异策略对已探测到的生境高度搜索, 加快算法的收敛速度. 实验结果表明, 所提出算法能够有效实现从全局探测到局部增强的自适应平滑过渡, 是一种有效的多模态优化算法.

     

基于广义凸下界估计的多模态差分进化算法

针对多模态优化问题,提出了基于广义凸下界估计模型的改进差分进化算法。首先,基于模型变换方法将原优化问题转变为单位单纯形约束条件下的严格递增射线凸优化问题;其次,基于广义凸理论,利用差分进化算法中更新个体的适应度知识,建立原优化问题广义凸下界估计模型,设计实现了基于 N-叉树的估计模型快速计算方法;进而,综合考虑原问题目标值与其估计值之间的差异,提出一种基于有偏采样的小生境指标,并设计区域进化树更新策略来保证算法的局部搜索能力。数值实验结果表明,提出的算法能够有效地发现并维持一定数量的满意解模态,动态地实现全局模态搜索到模态内局部增强的自适应平滑过渡。对于给出的测试问题,能够发现所有的全局最优解以及一些较好的局部极值解。     

基于知识引导的自适应动态多模态差分进化算法

为充分利用问题求解过程知识,提升动态多模态优化算法的计算资源利用效率,提出一种基于知识引导的自适应动态多模态差分进化算法.首先,利用自组织映射神经网络实现种群自聚类,形成稳定的小生境;然后,通过对种群全局知识和个体邻域知识的综合学习,设计一种基于知识引导的自适应差分进化算法,在对种群进化状态进行实时监测和分析的基础上,逐层递进地引导不同种群个体自适应地选择最符合当前进化需求的变异方式,提升种群搜索效率,平衡种群多样性与收敛性;最后,针对问题动态特性,设计一种基于历史动态过程知识引导的自适应动态响应机制,通过对历史寻优经验的自适应学习,预测生成新环境下的潜在精英个体,引导种群实现精准快速的多峰定位.实验结果表明,所提出算法能够有效解决动态多模态优化问题,且在不同动态环境设置下其求解性能均优于对比算法.     

基于并行分区搜索的多模态多目标优化及其应用

基于分区搜索的多模态多目标优化属于一种决策空间分解策略,因此它具有天然的并行性.为提高求解效率,提出了一种并行分区搜索(Parallel Zoning Search,PZS)方法来辅助多模态多目标进化算法.在PZS中,首先将多模态多目标优化问题的整个决策空间划分为多个子空间,然后利用并行计算技术来实现选定的多模态多目标...     

多模态优化问题的邻域低密度个体差分进化算法

针对多模态优化问题(MultiModal Optimization Problems, MMOPs)的求解,提出了一种基于邻域低密度个体的差分进化算法.该算法在每一代,首先使用密度峰值聚类的方法求得每一个个体的密度,然后,将当前个体邻域范围内密度更低的个体作为变异算子的基向量,随着种群的进化,算法将会自动从探索阶段转化为收敛阶段,进而平衡算法的探索与收敛能力.将提出的算法应用于CEC2013多模态基准测试函数并进行仿真实验,结果表明本文算法在评价指标峰值比和稳定性上与其它基于差分进化的多模态优化算法相比具有明显的优势,并随着测试函数的维度与复杂性的增大,优势就更加明显,其性能优于许多现有的基于差分进化的多模态优化算法.     

组合分布估计和差分进化的多目标优化算法

为了提高多目标优化算法的收敛能力及求解精度,提出了一种组合分布估计和差分进化的多目标优化算法.该方法用分布估计算法和差分进化算法共同生成种群中的粒子,利用选择因子来控制每个粒子的产生方式,并且根据迭代次数的增加来改变2种算法的使用比例,搜索初期利用分布估计算法进行快速定位,然后用差分进化算法进行精确搜索.并对差分进化算法的变异因子进行了改进,定义了一个可变的变异因子,来控制不同搜索时期中差分进化算法的变异范围.用4个测试函数对算法进行了仿真测试,并同NSGA-Ⅱ和RM-MEDA进行了比较.实验结果表明,该算法具有良好的收敛性和分布性,并且效果稳定.     

A novel efficient algorithm for mobile robot localization

Being autonomous is one of the most important goals in mobile robots. One of the fundamental works to achieve this goal is giving the ability to a robot for finding its own correct position and orientation. Different methods have been introduced to solve this problem. In this paper, a novel method based on the harmony search (HS) algorithm for robot localization through scan matching is proposed. Simulation results show that the proposed method in comparison with a genetic algorithm-based approach has better accuracy and higher performance. Furthermore a new hybrid algorithm based on harmony search and differential evolution (DE) algorithms is proposed and evaluated on different benchmark functions. Finally the hybrid algorithm has been applied for mobile robot localization and it outperformed the HS-based approach.     

Distributed memetic differential evolution with the synergy of Lamarckian and Baldwinian learning

As a population-based optimizer, the differential evolution (DE) algorithm has a very good reputation for its competence in global search and numerical robustness. In view of the fact that each member of the population is evaluated individually, DE can be easily parallelized in a distributed way. This paper proposes a novel distributed memetic differential evolution algorithm which integrates Lamarckian learning and Baldwinian learning. In the proposed algorithm, the whole population is divided into several subpopulations according to the von Neumann topology. In order to achieve a better tradeoff between exploration and exploitation, the differential evolution as an evolutionary frame is assisted by the Hooke–Jeeves algorithm which has powerful local search ability. We incorporate the Lamarckian learning and Baldwinian learning by analyzing their characteristics in the process of migration among subpopulations as well as in the hybridization of DE and Hooke–Jeeves local search. The proposed algorithm was run on a set of classic benchmark functions and compared with several state-of-the-art distributed DE schemes. Numerical results show that the proposed algorithm has excellent performance in terms of solution quality and convergence speed for all test problems given in this study.     

A self-adaptive differential evolution algorithm for continuous optimization problems

This paper proposes a new self-adaptive differential evolution algorithm (DE) for continuous optimization problems. The proposed self-adaptive differential evolution algorithm extends the concept of the DE/current-to-best/1 mutation strategy to allow the adaptation of the mutation parameters. The control parameters in the mutation operation are gradually self-adapted according to the feedback from the evolutionary search. Moreover, the proposed differential evolution algorithm also consists of a new local search based on the krill herd algorithm. In this study, the proposed algorithm has been evaluated and compared with the traditional DE algorithm and two other adaptive DE algorithms. The experimental results on 21 benchmark problems show that the proposed algorithm is very effective in solving complex optimization problems.     

一种具有混合编码的二进制差分演化算法

差分演化(DE)是Storn和Price于1997年提出的一种基于个体差异重组思想的演化算法,非常适用于求解连续域上的最优化问题.首先引入"差异算子"等概念,给出DE的一种简洁算法描述,并分析了它所具有的特性.然后,为了使DE能够求解离散域上的最优化问题,基于数学变换思想引入"辅助搜索空间"和"个体混合编码"等概念,通过定义一个特殊的满射变换,在辅助搜索空间的作用下将连续域上的高效差分演化搜索变换为离散域上的同步演化搜索,由此提出了第1个二进制差分演化算法:具有混合编码的二进制差分演化算法(HBDE).接着,给出了HBDE的依概率收敛和完全收敛的定义,并利用离散Markov随机理论证明了HBDE是完全收敛的. HBDE不仅完全具有DE的各种特性和所有优点,而且非常适用于求解离散域上的最优化问题,对随机生成的大规模3-SAT问题实例和典型0/1背包问题实例的数值计算表明:该算法具有很好的全局收敛性和稳定性,其性能远远超过二进制粒子群优化算法和遗传算法.     

混合模式搜索的分布式memetic差分进化算法

针对差分进化（DE）算法存在的早熟收敛与搜索停滞的问题,提出memetic分布式差分进化（DDE）算法。将memetic算法的思想融入到差分进化算法中,采用分布式的种群结构以及memetic算法中的混合策略,前者将初始种群分为多个子种群,子种群间根据冯·诺依曼拓扑结构周期性地实现信息交流,后者将差分进化算法作为进化的主要框架,模式搜索作为辅助手段,从而平衡算法的探索与开发能力。所提算法充分利用了模式搜索和差分进化算法的优势,建立了有效的搜索机制,增强了算法摆脱局部最优的能力,能够满足搜索过程对种群多样性及收敛速度的需求。将所提算法与几种先进的差分进化算法相比较,对标准测试函数进行优化的实验结果显示：所提算法在解的质量和收敛性能方面,均优于其他几种相比较的先进的差分进化算法。     

小推力火星探测器发射时机搜索方法

利用微分进化(DE)算法对小推力火星探测器发射时机进行搜索.首先建立了动力学方程,推导出最优化模型,然后使用两种策略采用DE算法与间接法相结合的方式对火星探测器发射机会进行了搜索,最后进行了仿真分析.研究结果表明:使用DE算法结合间接法的方式能够搜索到最优的发射机会;同时,针对所采用的发动机模型,小推力火星探测器发射机...     

An improved global-best harmony search algorithm for faster optimization

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.     

求解高维多模优化问题的自适应差分进化算法

在基变量选择方差理论分析的基础上,提出一种自适应差分进化算法(ADE).ADE算法通过设计自适应收敛因子构建自调整的权重质心变异策略,同时在交叉策略中引入发射、收缩两种单纯形操作算子,保证算法全局搜索能力的同时,能钉效提高算法后期的局部增强能力.30个优化问题的数值研究结果表明ADE算法具有比DE、DERL以及DERB三种算法更快的收敛速度和可靠性,尤其适合于高维多模优化问题的求解.     

Study of differential search algorithm based automatic generation control of an interconnected thermal-thermal system with governor dead-band

An attempt has been made to the effective application of a recently introduced, powerful optimization technique called differential search algorithm (DSA), for the first time to solve load frequency control (LFC) problem in power system. In this paper, initially, DSA optimized classical PI/PIDF controller is implemented to an identical two-area thermal-thermal power system and then the study is extended to two more realistic power systems which are widely used in the literature. To assess the usefulness of DSA, three enhanced competitive algorithms namely comprehensive learning particle swarm optimization (CLPSO), ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE), and success history based DE (SHADE) are studied in this paper. Moreover, the superiority of proposed DSA optimized PI/PID/PIDF controller is validated by an extensive comparative analysis with some recently published meta-heuristic algorithms such as firefly algorithm (FA), bacteria foraging optimization algorithm (BFOA), genetic algorithm (GA), craziness based particle swarm optimization (CRPSO), differential evolution (DE), teaching-learning based optimization (TLBO), particle swarm optimization (PSO), and quasi-oppositional harmony search algorithm (QOHSA). A case of robustness and sensitivity analysis has been performed for the concerned test system under parametric uncertainty and random load perturbation. Furthermore, to demonstrate the efficacy of proposed DSA, the system nonlinearities like reheater of the steam turbine and governor dead band are included in the system modeling. The extensive results presented in this article demonstrate that proposed DSA can effectively improve system dynamics and may be applied to real-time LFC problem.     

Three modified versions of differential evolution algorithm for continuous optimization

Differential evolution (DE) is one simple and effective evolutionary algorithm (EA) for global optimization. In this paper, three modified versions of the DE to improve its performance, to repair its defect in accurate converging to individual optimal point and to compensate the limited amount of search moves of original DE are proposed. In the first modified version called bidirectional differential evolution (BDE), to generate a new trial point, is used from the bidirectional optimization concept, and in the second modified version called shuffled differential evolution (SDE), population such as shuffled frog leaping (SFL) algorithm is divided in to several memeplexes and each memeplex is improved by the DE algorithm. Finally, in the third modified version of DE called shuffled bidirectional differential evolution (SBDE) to improve each memeplex is used from the proposed BDE algorithm. Three proposed modified versions are applied on two types of DE and six obtained algorithms are compared with original DE and SFL algorithms. Experiments on continuous benchmark functions and non-parametric analysis of obtained results demonstrate that applying bidirectional concept only improves one type of the DE. But the SDE and the SBDE have a better success rate and higher solution precision than original DE and SFL, whereas those are more time consuming on some functions. In a later part of the comparative experiments, a comparison of the proposed algorithms with some modern DE and the other EAs reported in the literature confirms a better or at least comparable performance of our proposed algorithms.