基于混合和声搜索算法求解竞争选址问题

针对竞争选址问题,提出一种新的混合和声搜索算法。混合和声搜索算法初始化和声记忆库时结合了贪婪算法,降低了初始解的不可行性概率。在寻优过程中,引入了鱼群算法的觅食行为,提高了算法跳出局部最优解的能力和收敛速度。即兴产生一个新的和声时,充分考虑了当前最优解的指导作用,提出了新的基因调整方法,增强了算法的探索能力。在竞争选址问题上对所提出的算法进行了测试,仿真结果验证了所提出算法的有效性。     

一种新的智能优化及其改进研究

研究了一种新的进化算法-和声搜索(HS)算法,针对其在处理复杂函数优化问题时容易陷入局部最优、收敛精度低的缺点,提出一种改进的和声搜索算法,算法在保留和声搜索的搜索机理的同时,把混合蛙跳算法中的局部搜索策略引入其中,维持了和声库的多样性,从而提高了对复杂问题的搜索效率.与同类算法相比,本文提出的和声搜索算法全局搜索能力强,收敛速度快,数值实验结果验证了算法的有效性和鲁棒性.     

和声搜索-分布估计混合算法求解多目标优化问题

针对和声搜索算法不能很好地求解多目标优化问题的缺陷,提出一种多目标和声搜索—分布估计混合算法(MHS-EDA)。该算法一方面利用分布估计的采样操作对和声记忆库内进行搜索,拓宽了和声记忆库内空间;另一方面对和声记忆库外进行外部档案搜索,实现群体间信息交换,从而提高了多目标和声算法的全局搜索能力。数值实验选取六个常用测试函数,并与多目标遗传算法、多目标分布估计算法、多目标和声搜索算法进行比较,测试结果表明提出的混合算法能够有效地解决多目标优化问题。     

基于离散和声搜索与模拟退火的混合算法

和声搜索是一种启发式优化算法,对和声搜索算法进行离散化,使其能够适用于组合优化问题,就离散和声搜索算法进行两点改进,针对离散和声搜索算法容易陷入局部最优的缺陷,提出一种离散和声搜索与模拟退火算法的混合策略。实验结果表明,基于改进离散和声搜索与模拟退火的混合算法具有较高的求解质量。     

基于和声搜索的阻塞流水车间调度问题的算法优化

针对以最大完工时间为目标的阻塞流水车间调度问题,提出了一种通过混合和声搜索改进遗 传算法的优化求解算法.构建了阻塞流水车间调度原理图并描述了运算方法;详细论述了混合算法的实施流程和关键问题,并使用NEH方法和局部搜索对混合算法进行了改进.仿真结果表明,改进后的混合算法能显著提高优化阻塞流水车间调度问题的解.     

混合变邻域和声搜索的独立任务调度问题研究

结合和声搜索和变邻域搜索算法的特点,提出混合的和声变邻域搜索算法,并将混合算法用于解决多处理机独立任务调度问题.混合算法采用列表调度方法对和声解进行编码,把和声分量转换为基于优先级的独立任务调度模型,利用变邻域搜索算法对和声解进行局部搜索以提高和声算法的搜索效率和解质量,利用模拟退火算法中的Metropolis准则作为新解接受准则,防止算法陷入局部极值.仿真实验对比结果表明,混合算法在解决独立任务的多处理机调度中具有更强的全局搜索能力和更快的收敛速度,并且能够跳出局部极小获得更高质量的解.     

求解批量流水线调度问题的改进和声搜索算法

针对以最大完工时间为目标的批量流水线调度问题,提出一种改进的和声搜索优化算法。该算法采用ROV规则的编码方式,使具有连续本质的和声搜索算法能直接应用于调度问题。对和声库的初始化和候选解的产生方式进行改进。针对该算法容易陷入局部最优的缺陷,将其与阈值接受算法结合,得到2种混合算法。仿真结果证明了该算法的有效性。     

求解批量流水线调度问题的和声算法

针对以最大完工时间和总流经时间为目标的批量流水线调度问题,提出了改进的和声调度算法。该算法采用基于最大位置值(LPV)规则的编码方式,使具有连续性质的和声算法应用于求解调度问题;提出新的初始化方法,应用了多种群进化的思想更新和声库,并结合和声算法和模拟退火算法各自的特点,给出了两种混合调度算法。仿真实验表明所提算法的可行性和有效性。     

基于改进离散和声算法的批量流水线调度研究

针对批量流水线调度问题,提出了以总流经时间为目标的改进离散和声算法。与基本的和声算法相比,该算法首先采用了基于工件序列的编码方式,使其直接应用于调度问题,同时运用NEH和SWAP方法产生初始和声库,保证了初始种群具有较高的质量和多样性。使用自适应和声微调概率参数和INSERT方法产生新解,提高了算法的优化性能。为了提高算法的局部搜索能力,结合交换扰动策略和插入邻域搜索算法给出了两种混合求解策略。仿真实验表明所提算法的有效性。     

一种改进的和声搜索算法

针对基本和声搜索算法的不足,提出一种改进的和声搜索算法.首先在和声搜索算法的记忆库中加入权重,减少搜索的随机性;其次让扰动方程中的带宽具有自适应性,从而提高原算法的鲁棒性和收敛速度.算法在计算机上予以实现,并通过一系列测试函数求解,验证了改进算法与基本和声搜索算法及其他智能算法相比,能得到更多的最优解和较小的方差.     

Cross-docking and milk run logistics in a consolidation network: A hybrid of harmony search and simulated annealing approach

In this paper, we develop a novel integer programming model for the transportation problem of a consolidation network where a set of vehicles are used to transport goods from suppliers to their corresponding customers via three transportation systems: direct shipment, shipment through cross-dock (indirect shipment) and milk run. Since the proposed problem formulation is NP-hard, we offer a hybrid of harmony search (HS) and simulated annealing (SA) based heuristics (HS-SA algorithm) in order to solve the problem. The objective of this problem is to minimize the total shipping cost in the network, so it is tried to reduce the number of required vehicles using an efficient vehicle routing strategy in the algorithm. Solving several numerical examples demonstrates that our solving approach performs much better than GAMS/CPLEX in reducing both the shipping cost in the network and computational time requirement, especially for large size problem instances.     

A hybrid harmony search algorithm for the spread spectrum radar polyphase codes design problem

In this paper we present the application of a hybrid harmony search (HS) algorithm to the Spread-Spectrum Radar Polyphase (SSRP) codes design. Such a design can be formulated as a non-linear max–min optimization problem, hard to be solved using classical numerical techniques. Soft-computing approaches have then been successfully applied to solve the SSRP in the past, such as evolutionary computation techniques, variable neighborhood approaches or tabu search algorithms. In this paper we elaborate on the proposed hybrid HS approach, which consists of a naive implementation of the HS algorithm along with an adaptive-step gradient-guided local search procedure. Intensive computer simulations show that the proposed hybrid HS algorithm is able to outperform existing algorithms for the SSRP design problem (including the best reported so far), with significant differences in large-size SSRP instances.     

Application of heuristic and hybrid-GASA algorithms to tool-path optimization problem for minimizing airtime during machining

In this paper, heuristic algorithms such as simulated annealing (SA), genetic algorithm (GA) and hybrid algorithm (hybrid-GASA) were applied to tool-path optimization problem for minimizing airtime during machining. Many forms of SA rely on random starting points that often give poor solutions. The problem of how to efficiently provide good initial estimates of solution sets automatically is still an ongoing research topic. This paper proposes a hybrid approach in which GA provides a good initial solution for SA runs. These three algorithms were tested on three-axis-cartesian robot during milling of wood materials. Their performances were compared based on minimum path and consequently minimum airtime. In order to make a comparison between these algorithms, two cases among the several milling operations were given here. According to results obtained from these examples, hybrid algorithm gives better results than other heuristic algorithms alone. Due to combined global search feature of GA and local search feature of SA, hybrid approach using GA and SA produces about 1.5% better minimum path solutions than standard GA and 47% better minimum path solutions than standard SA.     

一种模拟退火和粒子群混合优化算法

针对粒子群优化算法(PSO)容易陷入局部极值点、进化后期收敛慢和优化精度较差等缺点.把模拟退火技术(SA)引入到PSO箅法中,提出了一种混合优化算法.混合优化算法在各温度下依次进行PSO和SA搜索,是一种两层的串行结构.由于PSO提供了并行搜索结构,所以,混合优化算法使SA转化成并行SA算法.SA的概率突跳性保证了种群的多样性,从而防止PSO算法陷入局部极小.混合优化算法保持了PSO算法简单容易实现的特点,改善了算法的全局优化能力,提高了算法的收敛速度和计算精度.仿真结果表明,混合优化算法的优化性能优于基本PSO算法.     

基于模拟退火的花朵授粉优化算法

针对花朵授粉算法寻优精度低、收敛速度慢、易陷入局部极小的不足,提出一种把模拟退火(SA)融入到花朵授粉算法中的混合算法。该算法通过SA的概率突跳策略使其避免陷入局部最优,并利用SA的全域搜索的性能增强算法的全局寻优能力。通过6个标准测试函数进行测试,仿真结果表明,改进算法在4个测试函数中能够找到理论最优值,其收敛精度、收敛速度、鲁棒性均比基本的花朵授粉算法(FPA)、蝙蝠算法(BA)、粒子群优化(PSO)算法及改进的粒子群算法有较大的提高;同时,对非线性方程组问题进行求解的算例应用也验证了改进算法的有效性。     

Parallel-machine scheduling problems with sequence-dependent setup times using an ACO,SA and VNS hybrid algorithm

This paper proposes a hybrid metaheuristic for the minimization of makespan in scheduling problems with parallel machines and sequence-dependent setup times. The solution approach is robust, fast, and simply structured, and comprises three components: an initial population generation method based on an ant colony optimization (ACO), a simulated annealing (SA) for solution evolution, and a variable neighborhood search (VNS) which involves three local search procedures to improve the population. The hybridization of an ACO, SA with VNS, combining the advantages of these three individual components, is the key innovative aspect of the approach. Two algorithms of a hybrid VNS-based algorithm, SA/VNS and ACO/VNS, and the VNS algorithm presented previously are used to compare with the proposed hybrid algorithm to highlight its advantages in terms of generality and quality for large instances.     

基于改进遗传与模拟退火融合的RISP软硬件划分

软硬件划分是可重构指令集处理器在软硬件协同设计中的关键问题,通过对比遗传算法和经典模拟退火算法的优缺点,提出改进遗传算法的适应度函数,同时将Tsallis接受准则引入到经典模拟退火当中;其思路是用遗传算法的结果来制约模拟退火算法产生的随机状态,然后由模拟退火的接受准则以及产生的随机状态函数对遗传算法的种群进行更新,从而找到全局近似最优解;实验结果证明,改进算法与单一遗传算法以及经典模拟退火算法相比,其收敛速度和适应度更好,找到全局近似最优解的概率更大。     

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO–K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO–SA), combination of ACO and SA (ACO–SA), combination of PSO and ACO (PSO–ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.     

GSA-based maximum likelihood estimation for threshold vector error correction model

The log-likelihood function of threshold vector error correction models is neither differentiable, nor smooth with respect to some parameters. Therefore, it is very difficult to implement maximum likelihood estimation (MLE) of the model. A new estimation method, which is based on a hybrid algorithm and MLE, is proposed to resolve this problem. The hybrid algorithm, referred to as genetic-simulated annealing, not only inherits aspects of genetic-algorithms (GAs), but also avoids premature convergence by incorporating elements of simulated annealing (SA). Simulation experiments demonstrate that the proposed method allows to estimate the parameters of larger cointegrating systems. Additionally, numerical results show that the hybrid algorithm does a better job than either SA or GA alone.