用鱼群算法求解多级递阶物流中转运输系统优化问题

建立了解决多级递阶物流中转运输系统优化问题的大规模非线性最优规划模型。在优化模型中，在兼顾变量约束条件的空间限制和求解精度的情况下将求解空间离散化，方法是将变量空间划分成一定大小的网格，各级中转站的最优位置将在限定区域内的一些已知点上选取。该问题维数太高，采用改进的鱼群算法对该问题进行了求解。在算法中建立了各级中转站与网格点关系矩阵和相邻两级中转站间的关系矩阵来消除约束条件和压缩变量数；使用了基于相似性的演化算法来融合全局搜索和局部搜索；使用了自适应delta变异算子、双算术交叉算子、峰跳操作算子等多种算子改进人工鱼的各种行为。应用结果表明，该算法计算速度、可靠性和稳定性大幅度提高。     

On the robustness of population-based versus point-basedoptimization in the presence of noise

Practical optimization problems often require the evaluation of solutions through experimentation, stochastic simulation, sampling, or even interaction with the user. Thus, most practical problems involve noise. We address the robustness of population-based versus point-based optimization on a range of parameter optimization problems when noise is added to the deterministic objective function values. Population-based optimization is realized by a genetic algorithm and an evolution strategy. Point-based optimization is implemented as the classical Hooke-Jeeves pattern search strategy and threshold accepting as a modern local search technique. We investigate the performance of these optimization methods for varying levels of additive normally distributed fitness-independent noise and different sample sizes for evaluating individual solutions. Our results strongly favour population-based optimization, and the evolution strategy in particular     

Goal and predictive coordination in two level hierarchical systems

The multilevel system theory applies two general methods of coordination, which influences the manner of subsystem management—goal and predictive coordinations. Both these general types of coordination perform multiple data transfer between the hierarchical levels and delay the evaluation and implementation of a global optimal solution of a control problem. The paper demonstrates a coordination policy, which decreases the information transfer in the hierarchical system, titled “noniterative” coordination. The last is developed both for goal and predictive coordination strategies. The mathematical foundations of these two noniterative coordination strategies are presented. Comparative analysis is performed to identify peculiarities and drawbacks for the real time management of two level hierarchical systems. Assessment of the computational workload and speed of the coordination, expressed as “flops” numbers is done for the case of nonlinear optimization problems. Both the noniterative coordination strategies benefit the real time operation in the multilevel system by reducing the iterative computations and the data transfer between the hierarchical levels. The predictive coordination has potential in speeding the management process and resource allocation, due to the decomposition approach, which is applied.     

Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria

This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems.     

Memetic search in artificial bee colony algorithm

Artificial bee colony (ABC) optimization algorithm is relatively a simple and recent population based probabilistic approach for global optimization. ABC has been outperformed over some Nature Inspired Algorithms (NIAs) when tested over benchmark as well as real world optimization problems. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the solution search equation of ABC, there is a enough chance to skip the true solution due to large step size. In order to balance between diversity and convergence capability of the ABC, a new local search phase is integrated with the basic ABC to exploit the search space identified by the best individual in the swarm. In the proposed phase, ABC works as a local search algorithm in which, the step size that is required to update the best solution, is controlled by Golden Section Search approach. The proposed strategy is named as Memetic ABC (MeABC). In MeABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. MeABC is established as a modified ABC algorithm through experiments over 20 test problems of different complexities and 4 well known engineering optimization problems.     

Global replacement-based differential evolution with neighbor-based memory for dynamic optimization

Dynamic optimization problems challenge the evolutionary algorithms, owing to the diversity loss or the low search efficiency of the algorithms, especially when the problems change frequently. This paper presents a novel differential evolution algorithm to address the dynamic optimization problems. Unlike the most used “DE/rand/1” mutation operator, in this paper, the “DE/best/1” mutation is employed to generate a mutant individual. In order to enhance the search efficiency of differential evolution, the classical differential evolution algorithm is modified by a novel replacement operator, in which the worst individual in the whole population is replaced by the newly generated trial vector as a “steady-state” manner. During optimizing, some newly generated solutions are stored into a memory set, in which these stored solutions are located around the current best solution. When the environmental change is detected, the stored solutions are expected to guide the reinitialized solutions to track the new location of global optimum as soon as possible. The performance of the proposed algorithm is compared with six state-of-the-art dynamic evolutionary algorithms over some benchmark problems. The experimental results show that the proposed algorithm clearly outperforms the competitors.     

An adaptive optimization technique for dynamic environments

The use of evolutionary algorithms (EAs) is beneficial for addressing optimization problems in dynamic environments. The objective function for such problems changes continually; thus, the optimal solutions likewise change. Such dynamic changes pose challenges to EAs due to the poor adaptability of EAs once they have converged. However, appropriate preservation of a sufficient level of individual diversity may help to increase the adaptive search capability of EAs. This paper proposes an EA-based Adaptive Dynamic OPtimization Technique (ADOPT) for solving time-dependent optimization problems. The purpose of this approach is to identify the current optimal solution as well as a set of alternatives that is not only widespread in the decision space, but also performs well with respect to the objective function. The resultant solutions may then serve as a basis solution for the subsequent search while change is occurring. Thus, such an algorithm avoids the clustering of individuals in the same region as well as adapts to changing environments by exploiting diverse promising regions in the solution space. Application of the algorithm to a test problem and a groundwater contaminant source identification problem demonstrates the effectiveness of ADOPT to adaptively identify solutions in dynamic environments.     

An efficient decomposed multiobjective genetic algorithm for solving the joint product platform selection and product family design problem with generalized commonality

Product family optimization involves not only specifying the platform from which the individual product variants will be derived, but also optimizing the platform design and the individual variants. Typically these steps are performed separately, but we propose an efficient decomposed multiobjective genetic algorithm to jointly determine optimal (1) platform selection, (2) platform design, and (3) variant design in product family optimization. The approach addresses limitations of prior restrictive component sharing definitions by introducing a generalized two-dimensional commonality chromosome to enable sharing components among subsets of variants. To solve the resulting high dimensional problem in a single stage efficiently, we exploit the problem structure by decomposing it into a two-level genetic algorithm, where the upper level determines the optimal platform configuration while each lower level optimizes one of the individual variants. The decomposed approach improves scalability of the all-in-one problem dramatically, providing a practical tool for optimizing families with more variants. The proposed approach is demonstrated by optimizing a family of electric motors. Results indicate that (1) decomposition results in improved solutions under comparable computational cost and (2) generalized commonality produces families with increased component sharing under the same level of performance. A preliminary version of this paper was presented at the 2007 AIAA Multidisciplinary Design Optimization Specialists Conference.     

数据驱动选择策略的多目标差分进化算法

针对多目标差分进化算法求解复杂多目标优化问题时,最优解选择策略中非支配排序计算复杂度高的问题,提出一种数据驱动选择策略的多目标差分进化(MODE-DDSS)算法.首先,设计多目标差分进化算法的优化解排序等级评估准则,建立基于评估准则的优化解排序等级评估库;其次,设计基于优化解双向搜索机制和无重复比较机制的数据驱动选择策略,实现优化解的高效搜索和快速排序;最后,构建数据驱动选择策略的多目标差分进化算法,降低算法在最优解选择操作中的时间复杂度,提高算法的寻优效率.实验结果表明,所提出的MODE-DDSS算法能够有效减少最优解在选择过程中的比较次数,提升多目标差分进化算法解决复杂多目标优化问题的寻优效率.     

一种求解约束优化问题的线性进化算法

提出一种基于实数编码处理约束优化问题的线性算法,并对其复杂度和收敛性进行分析.该算法将约束优化问题的高维搜索空间通过线性变换映射到二维空间,在二维空间中探索原优化问题的解,从数学分析的角度给出一种线性适应度函数.算法中融入一种基于密度函数的交叉算子和变异算法,采用基于分级聚类的平均联接方式以维持Pareto最优解集个体数目.3组典型优化问题的测试表明,该算法是可行和有效的,解集分布的均匀性与多样性均较理想.     

基于自适应学习的多目标粒子群优化算法

将进化算法应用于某些多目标优化问题时,采用增加种群规模和进化代数的方法往往耗费大量的目标函数计算开销,且达不到提高种群进化效率的目的,为此提出了一种基于自适应学习最优搜索方向的多目标粒子群优化算法。采用自适应惯性权值平衡算法的全局和局部搜索能力,采用聚类排挤方法保持Pareto非支配解集的分布均匀性,使用最近邻学习方法为每个粒子在Pareto非支配解集中寻找一个最优飞行目标来提高其收敛速度并保持粒子群搜索方向的多样性。实验结果表明,提出的算法可在显著地降低函数评估成本的前提下实现快速的搜索,并使粒子群均匀地逼近Pareto最优面。     

大规模3D并行分层可扩展矩阵乘法的递阶优化方法

为进一步提高大规模平台上可扩展矩阵乘法的并行计算效率,提出一种并行分层可扩展矩阵乘法的递阶优化方法。首先,在可扩展矩阵乘法算法(SMM)算法枢轴行和枢轴列通信研究基础上,利用分层方式在更高等级上对网格进行矩形群划分,实现矩阵乘法的二维计算向三维计算转变,并设计对应的集群内通信和集群间通信过程,实现SMM乘法的递阶并行优化(HSMM);其次,对所提HSMM算法进行理论分析,分情况对其通信成本进行分析和预测,推导出最佳计算成本的集群数选取方式;最后,通过在Grid5000和BlueGene/P测试平台实验,验证了所提算法有效性和理论分析的正确性。     

逐维改进的布谷鸟搜索算法

布谷鸟搜索(cuckoo search,简称CS)算法是一种新兴的仿生智能算法,对解采用整体更新评价策略.在求解多维函数优化问题时,由于各维之间相互干扰,采用整体更新评价策略将恶化算法的收敛速度和解的质量.为了弥补此缺陷,提出了基于逐维改进的布谷鸟搜索算法.在改进算法的迭代过程中,针对解采用逐维更新评价策略.该策略将各维的更新值与其他维的值组合成新的解,并采用贪婪方式接受能够改善解质量的更新值.实验结果说明,改进策略能够有效地提高CS 算法的收敛速度并改善解的质量.与相关的改进布谷鸟搜索算法以及其他演化算法的比较结果表明,改进算法在求解连续函数优化问题上是具有竞争力的.     

动态优化问题的瞬态自适应麻雀搜索算法求解

动态优化普遍存在于工业过程控制领域,是实现系统稳态与产值最大化的重要手段,应用并发展更加高效的动态优化方法逐渐成为了当前研究的热点。鉴于此,提出一种基于瞬态自适应麻雀搜索算法（TASSA）的动态优化问题求解方案。首先,分析了原始麻雀搜索算法的缺陷,为了提升全局勘探能力,引入瞬态搜索策略指导加入者的寻优过程;其次,采用随迭代而变化的惯性权重调节具体的搜索方式,增强了算法的动态适应能力,并通过九组基准函数的数值测试确认了改进策略的有效性。最后,采用时域等分的方式,在控制变量参数化（CVP）的框架下利用TASSA对三组典型的动态优化问题进行求解,对比不同文献中的方法,所提算法取得了更精确的结果。     

一种引入Nelder-Mead算子的改进狼群搜索算法*

为了克服狼群搜索算法(WSA)存在的不足,提出一种新的混合优化算法,称之为引入Nelder-Mead算子的改进狼群搜索算法。该算法使每只狼在搜索中可利用群体信息和个体记忆来指导其搜索猎物,以提高算法的全局搜索能力;让每只狼在搜索中可使用Nelder-Mead方法,以弥补WSA算法在局部搜索能力上的不足。针对12个基准测试实例的实验结果表明, 该算法能够寻得更优的最优解,且鲁棒性更强。     

Cultural algorithms: a Tabu search approach for the optimization of engineering design problems

Cultural Algorithms and Tabu search algorithms are both powerful tools to solve intricate constrained engineering and large-scale multi-modal optimization problems. In this paper, we introduce a hybrid approach that combines Cultural Algorithms and Tabu search (CA–TS). Here, Tabu Search is used to transform History Knowledge in the Belief Space from a passive knowledge source to an active one. In each generation of the Cultural Algorithm, we calculate the best individual solution and then seek the best new neighbor of that solution in the social network for that population using Tabu search. In order to speed up the convergence process through knowledge dissemination, simple forms of social network topologies were used to describe the connectivity of individual solutions. This can reduce the number of needed generations while maintaining accuracy and increasing the search radius when needed. The integration of the Tabu search algorithm as a local enhancement process enables CA–TS to leap over false peaks and local optima. The proposed hybrid algorithm is applied to a set of complex non-linear constrained engineering optimization design problems. Furthermore, computational results are discussed to show that the algorithm can produce results that are comparable or superior to those of other well-known optimization algorithms from the literature, and can improve the performance and the speed of convergence with a reduced communication cost.     

DS-DPSO: A dual surrogate approach for intelligent watermarking of bi-tonal document image streams

Intelligent watermarking (IW) techniques employ population-based evolutionary computing in order to optimize embedding parameters that trade-off between watermark robustness and image quality for digital watermarking systems. Recent advances indicate that it is possible to decrease the computational burden of IW techniques in scenarios involving long heterogeneous streams of bi-tonal document images by recalling embedding parameters (solutions) from a memory based on Gaussian Mixture Model (GMM) representation of optimization problems. This representation can provide ready-to-use solutions for similar optimization problem instances, avoiding the need for a costly re-optimization process. In this paper, a dual surrogate dynamic Particle Swarm Optimization (DS-DPSO) approach is proposed which employs a memory of GMMs in regression mode in order to decrease the cost of re-optimization for heterogeneous bi-tonal image streams. This approach is applied within a four level search for near-optimal solutions, with increasing computational burden and precision. Following previous research, the first two levels use GMM re-sampling to recall solutions for recurring problems, allowing to manage streams of heterogeneous images. Then, if embedding parameters of an image require a significant adaptation, the third level is activated. This optimization level relies on an off-line surrogate, using Gaussian Mixture Regression (GMR), in order to replace costly fitness evaluations during optimization. The final level also performs optimization, but GMR is employed as a costlier on-line surrogate in a worst-case scenario and provides a safeguard to the IW system. Experimental validation were performed on the OULU image data set, featuring heterogeneous image streams with a varying levels of attacks. In this scenario, the DS-DPSO approach has been shown to provide comparable level of watermarking performance with a 93% decline in computational cost compared to full re-optimization. Indeed, when significant parameter adaptation is required, fitness evaluations may be replaced with GMR.     

Evolutionary algorithms,simulated annealing and tabu search: a comparative study

Evolutionary algorithms, simulated annealing (SA), and tabu search (TS) are general iterative algorithms for combinatorial optimization. The term evolutionary algorithm is used to refer to any probabilistic algorithm whose design is inspired by evolutionary mechanisms found in biological species. Most widely known algorithms of this category are genetic algorithms (GA). GA, SA, and TS have been found to be very effective and robust in solving numerous problems from a wide range of application domains. Furthermore, they are even suitable for ill-posed problems where some of the parameters are not known before hand. These properties are lacking in all traditional optimization techniques. In this paper we perform a comparative study among GA, SA, and TS. These algorithms have many similarities, but they also possess distinctive features, mainly in their strategies for searching the solution state space. The three heuristics are applied on the same optimization problem and compared with respect to (1) quality of the best solution identified by each heuristic, (2) progress of the search from initial solution(s) until stopping criteria are met, (3) the progress of the cost of the best solution as a function of time (iteration count), and (4) the number of solutions found at successive intervals of the cost function. The benchmark problem used is the floorplanning of very large scale integrated (VLSI) circuits. This is a hard multi-criteria optimization problem. Fuzzy logic is used to combine all objective criteria into a single fuzzy evaluation (cost) function, which is then used to rate competing solutions.     

A machine-learning based memetic algorithm for the multi-objective permutation flowshop scheduling problem

In recent years, the historical data during the search process of evolutionary algorithms has received increasing attention from many researchers, and some hybrid evolutionary algorithms with machine-learning have been proposed. However, the majority of the literature is centered on continuous problems with a single optimization objective. There are still a lot of problems to be handled for multi-objective combinatorial optimization problems. Therefore, this paper proposes a machine-learning based multi-objective memetic algorithm (ML-MOMA) for the discrete permutation flowshop scheduling problem. There are two main features in the proposed ML-MOMA. First, each solution is assigned with an individual archive to store the non-dominated solutions found by it and based on these individual archives a new population update method is presented. Second, an adaptive multi-objective local search is developed, in which the analysis of historical data accumulated during the search process is used to adaptively determine which non-dominated solutions should be selected for local search and how the local search should be applied. Computational results based on benchmark problems show that the cooperation of the above two features can help to achieve a balance between evolutionary global search and local search. In addition, many of the best known Pareto fronts for these benchmark problems in the literature can be improved by the proposed ML-MOMA.