近邻场优化算法研究与应用综述

近邻场优化算法(neighborhood field optimization,NFO)是一种受生物个体向邻居学习行为启发的新型群体智能优化算法,该算法具有参数较少、结构简单和局部寻优性能强等优点,吸引了国内外众多学者的关注和研究.简单阐述NFO算法的寻优原理和搜索步骤,并分析了现有的算法的改进研究,包括混合算法、编码...     

基于新型蛙跳算法的低碳柔性作业车间调度

针对低碳柔性作业车间调度问题(flexible job shop scheduling problem,FJSP),提出一种新型蛙跳算法(shuffled frog leaping algorithm,SFLA)以总碳排放最小化,该算法运用记忆保留搜索所得一定数量的最优解,并采取基于种群和记忆的种群划分方法,应用新的搜索策略如全局搜索与局部搜索的协调优化以实现模因组内的搜索,取消种群重组使算法得到简化.采用混合遗传算法和教–学优化算法作为对比算法,大量仿真对比实验验证了SFLA对于求解低碳FJSP具有较强的搜索能力和竞争力.     

基于改进PSO和FCM的模糊辨识

为了提高T-S模糊模型的辨识精度和效率,本文提出了一种改进的粒子群算法和模糊C均值聚类算法相结合的模糊辨识新方法。在该方法中,针对粒子群算法在处理高维复杂函数时容易陷入局部极值的问题,提出了一种粒子群局部搜索和全局搜索动态调整的全新优化算法。模糊C均值聚类算法是模糊辨识最常用的方法之一,该算法简单,计算效率高,但是对初始化特别敏感,容易陷入局部最优。为了解决这一问题,利用改进粒子群算法的全局搜索能力优化聚类中心,显著地提高了算法的辨识精度和效率。最后,针对非线性系统进行建模仿真,仿真结果表明了本文方法的有效性和优越性。     

一种快速有效的多模态函数寻优方法─—双群体遗传算法

为了解决多模态函数优化问题中全局搜索和局部优化的矛盾，本文提出一种模仿社会分工现象的观群体遗传算法，该算法用一个群体搜索，另一个群体优化，仿真结果表明：和现有方法相比，该算法不仅不会陷入局部极小点，而且收敛速度极快，是一种多模态函数寻优的有效方法。     

基于混沌蚁群算法的机器人路径规划

研究了全局静态复杂环境的机器人导航问题;针对传统蚁群极易陷入局部最优解,引入混沌理论改善个体质量,利用混沌扰动避免在搜索过程中陷入局部极值;构建了一个新的机器人路径规划算法的数学模型,在组织变量的影响下,蚂蚁由最初的混沌行为逐渐过渡为群体智能行为,最终完成机器人全局最优路径的搜索;仿真结果表明,即使在障碍物非常复杂的环境中,该模型也能找出一条全局最优或近似最优的路径,且能安全避障,仿真效果理想。     

MaxSAT-based large neighborhood search for high school timetabling

High School Timetabling (HSTT) is a well known and wide spread problem. The problem consists of coordinating resources (e.g. teachers, rooms), times, and events (e.g. lectures) with respect to various constraints. Unfortunately, HSTT is hard to solve and just finding a feasible solution for simple variants of HSTT have been proven to be NP-complete. We propose a new algorithm for HSTT which combines local search with a novel maxSAT-based large neighborhood search. A local search algorithm is used to drive an initial solution into a local optimum and then more powerful large neighborhood search (LNS) techniques based on maxSAT are used to further improve the solution. We prove the effectiveness of our approach with experimental results on instances taken from the Third International Timetabling Competition 2011 and the XHSTT-2014 benchmark archive. We were able to model 27 out of 39 instances. The remaining 12 instances were not modeled because the currently used maxSAT formulation for XHSTT does not support resource assignments in general. For the instances which could be modeled, our algorithm shows good performance when compared to other XHSTT state-of-the-art solvers and for several instances new best known upper bounds have been computed.     

A parallel boundary search particle swarm optimization algorithm for constrained optimization problems

During the past decade, considerable research has been conducted on constrained optimization problems (COPs) which are frequently encountered in practical engineering applications. By introducing resource limitations as constraints, the optimal solutions in COPs are generally located on boundaries of feasible design space, which leads to search difficulties when applying conventional optimization algorithms, especially for complex constraint problems. Even though penalty function method has been frequently used for handling the constraints, the adjustment of control parameters is often complicated and involves a trial-and-error approach. To overcome these difficulties, a modified particle swarm optimization (PSO) algorithm named parallel boundary search particle swarm optimization (PBSPSO) algorithm is proposed in this paper. Modified constrained PSO algorithm is adopted to conduct global search in one branch while Subset Constrained Boundary Narrower (SCBN) function and sequential quadratic programming (SQP) are applied to perform local boundary search in another branch. A cooperative mechanism of the two branches has been built in which locations of the particles near boundaries of constraints are selected as initial positions of local boundary search and the solutions of local boundary search will lead the global search direction to boundaries of active constraints. The cooperation behavior of the two branches effectively reinforces the optimization capability of the PSO algorithm. The optimization performance of PBSPSO algorithm is illustrated through 13 CEC06 test functions and 5 common engineering problems. The results are compared with other state-of-the-art algorithms and it is shown that the proposed algorithm possesses a competitive global search capability and is effective for constrained optimization problems in engineering applications.     

An improved artificial bee colony algorithm based on the strategy of global reconnaissance

The artificial bee colony (ABC) algorithm is a recently introduced swarm intelligence optimization algorithm based on the foraging behavior of a honeybee colony. However, many problems are encountered in the ABC algorithm, such as premature convergence and low solution precision. Moreover, it can easily become stuck at local optima. The scout bees start to search for food sources randomly and then they share nectar information with other bees. Thus, this paper proposes a global reconnaissance foraging swarm optimization algorithm that mimics the intelligent foraging behavior of scouts in nature. First, under the new scouting search strategies, the scouts conduct global reconnaissance around the assigned subspace, which is effective to avoid premature convergence and local optima. Second, the scouts guide other bees to search in the neighborhood by applying heuristic information about global reconnaissance. The cooperation between the honeybees will contribute to the improvement of optimization performance and solution precision. Finally, the prediction and selection mechanism is adopted to further modify the search strategies of the employed bees and onlookers. Therefore, the search performance in the neighborhood of the local optimal solution is enhanced. The experimental results conducted on 52 typical test functions show that the proposed algorithm is more effective in avoiding premature convergence and improving solution precision compared with some other ABCs and several state-of-the-art algorithms. Moreover, this algorithm is suitable for optimizing high-dimensional space optimization problems, with very satisfactory outcomes.     

基于修正蚁群算法的多机器人气味源定位策略研究

为了使多机器人系统能够模仿蚁群寻找食物源的行为方式来搜索室内环境中存在的气味源,通过对蚁群算法的修正,形成一种新的多机器人协作策略．修正的蚁群算法包括局部遍历搜索、全局随机/概率搜索和信息素更新三个阶段．为了实现多个气味源的定位,在迭代搜索中加入了气味源确认机制．仿真结果表明,局部遍历搜索能够保证机器人逐步靠近气味源,而在全局搜索中设置气味浓度检测阈值可以避免机器人“群聚”现象的形成．最后验证了从不同入口点分散进入搜索区域时,机器人对多个气味源的搜索定位效果．     

双种群分子动理论优化算法

针对传统分子动理论优化算法存在寻优精度差、易陷入局部极值等不足,提出了一种双种群分子动理论优化算法。该算法将种群分为精英和普通两个子群：普通子群采用传统分子动理论优化算法搜索策略进行大范围搜索,而精英子群则通过协同合作实现精细化搜索,以提高算法收敛精度;基于个体迁移实现子群间的信息交流,两个子群通过分工合作共同完成搜索过程。实验结果表明：改进算法在收敛速度、精度和算法稳定性等方面都有明显改善。     

A PSO-based adaptive fuzzy PID-controllers

In this paper, a novel design method for determining the optimal fuzzy PID-controller parameters of active automobile suspension system using the particle swarm optimization (PSO) reinforcement evolutionary algorithm is presented. This paper demonstrated in detail how to help the PSO with Q-learning cooperation method to search efficiently the optimal fuzzy-PID controller parameters of a suspension system. The design of a fuzzy system can be formulated as a search problem in high-dimensional space where each point represents a rule set, membership functions, and the corresponding system’s behavior. In order to avoid obtaining the local optimum solution, we adopted a pure PSO global exploration method to search fuzzy-PID parameter. Later this paper explored the improved the limitation between suspension and tire deflection in active automobile suspension system with nonlinearity, which needs to be solved ride comfort and road holding ability problems, and so on. These studies presented many ideas to solve these existing problems, but they need much evolution time to obtain the solution. Motivated by above discussions this paper propose a novel algorithm which can decrease the number of evolution generation, and can also evolve the fuzzy system for obtaining a better performance.     

基于加强概率控制策略的SAT局部搜索算法

局部搜索算法是目前求解SAT问题比较有效的方法,而Sattime算法是在SAT国际大赛中获得大奖的一种典型局部搜索算法。在Sattime算法的求解过程中,记录变元翻转事件流数据库,通过数据分析与模式挖掘,发现Sattime算法的局部搜索行为中会出现相邻搜索步选择同一个变元的现象,即所谓的回环现象,从而降低了求解效率。为解决此问题,提出两种概率控制策略：加强子句选择策略和加强变元选择策略,并将这两种策略应用到Sattime算法中,形成新的局部搜索算法Sattime-P。实验结果表明,与Sattime算法相比,改进后的Sattime-P算法求解效率有显著的提升。该方法也对其他局部搜索算法的改进具有参考价值。     

基于定向爬山的遗传算法

爬山法是一种局部搜索能力相当好的算法,主要是因为它是通过个体的优劣信息来引导搜索的。而传统的遗传算法作为一种全局搜索算法,在搜索过程中却没有考虑个体间的信息,而仅依靠个体适应度来引导搜索,使得算法的收敛性受到限制。将定向爬山机制应用于遗传算法,提出了一种基于定向爬山的遗传算法（OHCGA）。该算法结合了爬山法与遗传算法的优点,通过比较个体的优劣,使用定向爬山操作引导算法向更优秀的解区域进行搜索。实验结果表明,与传统遗传算法（TGA）相比,OHCGA较大地提高了算法的收敛速度和搜索最优解的能力。     

An improved fruit fly optimization algorithm for solving the multidimensional knapsack problem

This paper presents an improved fruit fly optimization algorithm (IFFOA) for solving the multidimensional knapsack problem (MKP). In IFFOA, the parallel search is employed to balance exploitation and exploration. To make full use of swarm intelligence, a modified harmony search algorithm (MHS) is proposed and applied to add cooperation among swarms in IFFOA. In MHS, novel pitch adjustment scheme and random selection rule are developed by considering specific characters of MKP and FOA. Moreover, a vertical crossover is designed to guide stagnant dimensions out of local optima and further improve the performance. Extensive numerical simulations are conducted and comparisons with other state-of-the-art algorithms verify that the proposed algorithm is an effective alternative for solving the MKP.     

多智能体布谷鸟算法的网络计划资源均衡优化

网络计划资源均衡属于组合优化问题,为了能快速有效地求解此类问题,提出了一种多智能体布谷鸟算法。针对标准布谷鸟算法缺乏信息共享的缺陷,将多智能体系统引入布谷鸟算法中。多智能体的邻域竞争合作算子实现智能体间信息的交流,加快算法收敛速度;变异算子扩大搜索范围增加种群多样性;自学习算子提高局部寻优的能力;布谷鸟算法的Levy飞行进化机制能有效地跳出局部最优实现全局收敛。实例仿真结果证实了,与其他算法相比多智能体布谷鸟算法能更有效地求解网络计划资源均衡优化问题。     

动态串行机制多元宇宙优化算法

为优化多元宇宙算法求解函数最优值的性能,提出一种改进搜索机制的全局优化多元宇宙算法(G-MVO).针对标准算法存在单一搜索机制导致算法易陷入局部最优以及过早收敛的缺陷,提出三种学习策略来增强算法性能,通过多策略交互协作降低算法复杂度并提高求解精度,设计自适应参数动态选择最佳策略,全局优化算法性能.为验证算法的有效性,算法在不同维度的八个基准函数上进行仿真实验.结果表明,该算法表现出更佳的求解精度以及收敛速度.     

基于度值和聚类系数的跨单元调度问题优化

针对工业互联网大环境下的跨单元调度存在协作效率差、生产成本过高等问题,在机器设备归置存在重叠的情况下,首先使用分层网络设计思想构造以机器和制造单元为节点的双层有向加工网络,通过分析网络中全局协作效率、单元间冗余加工路径与一阶度值的相关性,构建最小化平均度值、完工时间和加工成本的多目标调度模型.其次根据麻雀搜索算法局部搜索能力强的特点,提出了一种非支配排序遗传算法和麻雀搜索算法融合策略以及基于聚类系数的初始解生成机制.最后通过实例计算说明网络特征与跨单元调度目标呈相关性,所提模型和算法求解质量更高.     

方向自学习遗传算法

为克服准遗传算法收敛速度慢、早熟收敛等缺点,提出一种方向自学习遗传算法,该算法在局部搜索中引入方向信息,利用函数的伪梯度来指导搜索方向。算法通过个体之间的竞争、合作与学习来不断更新最优个体,为增加种群的多样性提出一种消亡算子,避免早熟收敛,提高算法收敛速度。采用4个二维函数和多个无约束高维函数对算法进行测试,与3个新提出的算法进行比较,实验数据和理论分析表明,该算法在解的质量上和计算复杂度上都优于上述3个算法,充分证明该算法的有效性。     

战时维修保障资源优化调度的μPSO算法研究

将战时装备维修保障资源调度决策问题视作多任务多资源竞争与协调的多目标组合优化问题,构建出合理完善的资源调度决策模型。针对传统PSO算法搜索能力弱、易陷入局部最小等不足,提出一种在算法结构上改进的μPSO方法用于模型求解,它通过对一般粒子和当代最优粒子的不同速度、位置计算方式增强算法搜索能力;利用排斥项避免搜索进程的早熟收敛。最后通过算例证明μPSO算法对求解该类问题是可行有效的。     

设备定位问题局部搜索算法的实验

讨论设备问题的局部搜索近似算法及其在实际计算中表现出的新性质。主要讨论局部搜索算法中初始解的产生方法,设备价值与服务价值大小对算法求解性能的影响。实验表明：约有99%以上的实例可直接利用局部搜索算法求得最优解;贪心算法产生初始解的局部搜索算法求解时间明显短于随机算法产生初始解的方法,但两者求解质量相当;设备价值和服务价值数值范围越大,局部搜索算法越容易求得最优解。