A block-based evolutionary algorithm for flow-shop scheduling problem

Combinatorial problems like flow shop scheduling, travel salesman problem etc. get complicated and are difficult to solve when the problem size increases. To overcome this problem, we present a block-based evolutionary algorithm (BBEA) which will conduct evolutionary operations on a set of blocks instead of genes. BBEA includes the block mining and block recombination approaches. A block mining algorithm is developed to decompose a chromosome into a set of blocks and rest of genes. The block is with a fixed length and can be treated as a building block in forming a new chromosome later on. To guide the block mining process, a gene linkage probability matrix is defined that shows the linkage strength among genes. Therefore the blocks can be further evolved during the evolutionary processes using this matrix. In the block recombination approach, the blocks along with the rest of genes are recombined to form a new chromosome. This new evolutionary approach of BBEA is tested on a set of discrete problems. Experimental results show that BBEA is very competitive when compared with traditional GA, EA or ACGA and HGIA approaches and it can largely improve the performance of evolutionary algorithm and save a fair amount of computational times simultaneously.     

A rock-paper-scissors evolutionary algorithm for the TDMA broadcast scheduling problem

In wireless ad-hoc networks, the broadcast scheduling problem (BSP) is commonly viewed as a well-known NP-complete combinatorial optimization problem. The purpose of the BSP is to achieve a transmission schedule with collision-free time slots in a time division multiple access ad-hoc network. The transmission schedule is optimized by minimizing the frame length of the node transmissions and maximizing the utilization of the shared channel. In this work, we propose a new evolutionary algorithm to solve the BSP by adopting the rock-paper-scissors dynamics found in natural systems. We use three types of species with strategies of different levels of intensification and diversification to simulate the rock-paper-scissors dynamics. Based on this evolutionary game, in which the success of one species relies on the behavior of others, the dynamic coexistence of three species can be achieved to control the balance between intensification and diversification. The experimental results demonstrate that our algorithm is efficient and effective at solving large instances of the BSP.     

A multi-objective evolutionary algorithm to exploit the similarities of resource allocation problems

The complexity of a resource allocation problem (RAP) is usually NP-complete, which makes an exact method inadequate to handle RAPs, and encourages heuristic techniques to this class of problems for obtaining approximate solutions in polynomial time. Different heuristic techniques have already been investigated for handling various RAPs. However, since the properties of an RAP can help in characterizing other RAPs, instead of individual solution techniques, the similarities of different RAPs might be exploited for developing a common solution technique for them. Two RAPs of quite different nature, namely university class timetabling and land-use management, are considered here for such a study. The similarities between the problems are first explored, and then a common multi-objective evolutionary algorithm (a kind of heuristic techniques) for them is developed by exploiting those similarities. The algorithm is problem-dependent to some extent and can easily be extended to other similar RAPs. In the present work, the algorithm is applied to two real instances of the considered problems, and its properties are derived from the obtained results.     

用多目标演化优化算法解决约束选址问题

约束选址问题是一个多目标约束优化问题,传统算法（加权法）一次只能得到一个候选解,用多目标演化优化算法对其进行求解,可以一次得到多个候选解,给决策者提供更多的选择余地,以期获得更大的利益,数字试验表明,该方法优于传统多目标优化方法。     

Multi-objective genetic algorithm for berth allocation problem considering daytime preference

Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed.     

柔性作业调度的串并行模型对比与求解

作业处理中的柔性使得作业调度更为灵活,作业中操作的执行顺序满足拓扑排序是作业调度的前提。是否允许没有优先关系的操作在不同的机器上同时执行是区分串行和并行调度的条件。文中以共生进化算法求解一个复杂的作业调度模型为例,给出了算法实现串行调度和并行调度的具体区别,并给出了串行和并行调度的结果。结果表明,并行相对于串行对算法效率的提高与柔性大小相关,与作业的规模成反比。     

一种求解旅行商问题的热力学演化算法

在综合国内外演化计算研究现状的基础上,基于热力学中的自由能极小化原理,设计了一个全新的热力学演化算法,并通过对于流动旅行商问题求解的数值实验,测试了热力学演化算法的优良性能,实验结果表明了热力学演化算法求出的解比一般演化算法求出的解更加接近于全局最优。     

一种求解Shubert函数优化问题的演化算法

综合国内外演化计算研究现状,基于热力学中的自由能极小化原理, 设计了一个全新的热力学演化算法,并通过对于Shubert函数优化问题求解的数值试验,测试了热力学演化算法的优良性能,实验结果表明了热力学演化算法求出的解比一般演化算法求出的解更加接近于全局最优。     

A parallel double-level multiobjective evolutionary algorithm for robust optimization

Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.     

求解人力资源分配问题的多目标微粒群优化算法*

提出一种改进的多目标微粒群优化算法来求解人力资源分配问题.通过对种群进行正交初始化,保证了个体在整个可行解空间上的均匀分散,使得算法能够在整个可行解空间上进行均匀搜索;通过基于网格技术的外部存档非劣解删选策略,有效地保留了逼近Pareto前沿的非劣解;引入一种广义的学习策略来提升粒子向Pareto前沿收敛的概率.实验结...     

货架分配问题的一个有效混合遗传算法

针对货架分配问题提出了一个遗传算法与模拟退火算法及一个局部搜索算法混合的算法。首先,设计了一种比较直观的编码方法,用一个矩阵作为一种货架分配方案。第二,设计了与编码相应的杂交和变异算子,并且杂交、变异都能生成可行解,不需要对解进行修正。第三,为了能够生成好的初始种群,定义了一个阀值,这个阀值不仅反映了解的适应值的信息,而且还反映解的结构的信息。第四,为了增加算法的局部搜索能力,同时又尽量不增加计算的复杂度,让模拟退火算法和一种局部搜索算法并行作用于相应的子群。通过大量的数据模拟实验及与其他的几种算法模拟结果进行比较,实验显示,该算法不论是计算结果还是算法的稳定性都优于其他算法。     

蚁群算法求解分布式系统任务分配问题

蚁群算法是受自然界蚂蚁觅食过程中,基于信息素的最短路径搜索食物行为的启发提出的一种智能优化算法.研究表明,在求解复杂优化问题方面该算法具有一定的优越性.任务分配问题是一类典型的组合优化问题.应用蚁群算法来解决多处理器分布式系统上的任务分配问题,一个任务只能分配给一个处理器处理,而一个处理器可以处理多个任务,其中每个处理器都有固定成本和能力限制.仿真结果表明,该算法比禁忌搜索和随机方法具有更好的求解能力.     

一类工作调度问题的回溯解法

回溯法是解决组合搜索问题的重要方法,该方法的搜索通过一个多阶段的确定过程来实现,在每一阶段都需要从一些选择中选择一个分支,一旦发现前面的选择不可能获得一个解,则算法进行回溯,即重新回到刚搜索过的选择点,并选择该结点另一个没有被试过的分支,如果该点处所有的分支都已试过,则算法回溯到该结点之前被选择的点.首先对一类分配调度问题进行了分析,然后提出一种基于回溯法的解决方案,并给出了算法的具体实现过程,最后对所提出算法的复杂度进行了分析.实验结果验证了方法的有效性.     

A hybridization of an evolutionary algorithm and a parallel branch and bound for solving the capacitated single allocation hub location problem

In this study, we propose a hybrid optimization method, consisting of an evolutionary algorithm (EA) and a branch-and-bound method (BnB) for solving the capacitated single allocation hub location problem (CSAHLP). The EA is designed to explore the solution space and to select promising configurations of hubs (the location part of the problem). Hub configurations produced by the EA are further passed to the BnB search, which works with fixed hubs and allocates the non-hub nodes to located hubs (the allocation part of the problem). The BnB method is implemented using parallelization techniques, which results in short running times. The proposed hybrid algorithm, named EA-BnB, has been tested on the standard Australia Post (AP) hub data sets with up to 300 nodes. The results demonstrate the superiority of our hybrid approach over existing heuristic approaches from the existing literature. The EA-BnB method has reached all the known optimal solutions for AP hub data set and found new, significantly better, solutions on three AP instances with 100 and 200 nodes. Furthermore, the extreme efficiency of the implementation of this hybrid algorithm resulted in short running times, even for the largest AP test instances.     

多目标遗传算法求解认知无线电SUL维持问题

当某个一级用户重新出现时,二级用户必须马上让出属于此一级用户的频谱,此时就存在着如何维持二级用户链接（Secondary Users’Link,SUL）的问题。基于CORVUS系统的“冗余子信道”模型在SUL原有的N个子信道基础上再使用X个冗余子信道进行二级用户之间的通信。只要传输过程中受干扰的子信道个数少于X,接收端就可以从N+X个子信道中恢复出正确信息来,从而解决了SUL维持的问题。但尚未有一种有效的办法以求解最优的N和X。将SUL维持问题建模为一个多目标优化问题,提出了一种基于多目标遗传算法的SUL维持算法SULEA。SULEA能够根据不同的用户服务需求动态地选择适应度函数来求解最优的N和X,Matlab实验证明了SULEA的正确性和有效性。     

An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows

The vehicle routing problem with time windows is a complex combinatorial problem with many real-world applications in transportation and distribution logistics. Its main objective is to find the lowest distance set of routes to deliver goods, using a fleet of identical vehicles with restricted capacity, to customers with service time windows. However, there are other objectives, and having a range of solutions representing the trade-offs between objectives is crucial for many applications. Although previous research has used evolutionary methods for solving this problem, it has rarely concentrated on the optimization of more than one objective, and hardly ever explicitly considered the diversity of solutions. This paper proposes and analyzes a novel multi-objective evolutionary algorithm, which incorporates methods for measuring the similarity of solutions, to solve the multi-objective problem. The algorithm is applied to a standard benchmark problem set, showing that when the similarity measure is used appropriately, the diversity and quality of solutions is higher than when it is not used, and the algorithm achieves highly competitive results compared with previously published studies and those from a popular evolutionary multi-objective optimizer.     

求解QoS组播路由问题的改进型量子进化算法

QoS组播路由问题是一个非线性的组合优化问题,已证明了该问题是NP完全问题。提出一种将基于量子计算原理的量子进化算法用于此类问题求解的算法,该算法对基本的量子进化算法进行改进,采用进化方程对量子门进行调整,采用量子变异阻止未成熟收敛,使之更适合于QoS组播路由的求解。仿真结果显示,该算法能快速搜索并收敛到全局（近似）最优解,且随着网络规模的增大算法保持了良好的特性,在寻优速度上与解的质量上优于其他进化算法与基本的量子进化算法。     

A multi-objective evolutionary algorithm for examination timetabling

This paper considers the scheduling of exams for a set of university courses. The solution to this exam timetabling problem involves the optimization of complete timetables such that there are as few occurrences of students having to take exams in consecutive periods as possible but at the same time minimizing the timetable length and satisfying hard constraints such as seating capacity and no overlapping exams. To solve such a multi-objective combinatorial optimization problem, this paper presents a multi-objective evolutionary algorithm that uses a variable-length chromosome representation and incorporates a micro-genetic algorithm and a hill-climber for local exploitation and a goal-based Pareto ranking scheme for assigning the relative strength of solutions. It also imports several features from the research on the graph coloring problem. The proposed algorithm is shown to be a more general exam timetabling problem solver in that it does not require any prior information of the timetable length to be effective. It is also tested against a few influential and recent optimization techniques and is found to be superior on four out of seven publicly available datasets.     

一种求解复杂优化问题的新型人工鱼群算法

受自然界群体生物繁衍生息行为的启发,提出了一种新型人工鱼群算法。新算法将鱼群行为概括为：觅食行为、繁衍行为和逃逸行为。其中,繁衍行为是指利用进化算法的选择和交叉算子赋予了人工鱼繁衍能力;逃逸行为利用了云模型云滴的随机性和稳定倾向性的特点,由基本云发生器实现人工鱼变异操作。新算法还采用了双曲正切函数建立了步长参数自适应模型,从而动态调整算法寻优能力。通过10个标准测试函数的计算验证和分析比较,表明了提出的新型自适应混合人工鱼群算法具有计算精度高、搜索速度快等特点。