Data Mining based mutation function for engineering problems with mixed continuous-discrete design variables

Genetic algorithms are well established for solving engineering optimization problems having both continuous and discrete design variables. In this paper, a mutation function for discrete design variables based on Data Mining is introduced. The M5P Data Mining algorithm is used to build rules for the prediction of the optimization objectives with respect to the discrete design variables. The most promising combinations of discrete design variables are then selected in the mutation function of the genetic algorithm GAME to create children. This approach results in faster convergence and better results for both single and multi-objective problems when compared with a standard mutation scheme of discrete design variables. The optimization of a vehicle space frame showed that a mutation probability between 40% and 60% for the discrete design variables results in the fastest convergence. A multi-objective aerospace conceptual design example showed a substantial improvement in the number of pareto-optimal solutions found after 100 generations.     

Evolutionary multiobjective optimization using an outranking-based dominance generalization

One aspect that is often disregarded in the current research on evolutionary multiobjective optimization is the fact that the solution of a multiobjective optimization problem involves not only the search itself, but also a decision making process. Most current approaches concentrate on adapting an evolutionary algorithm to generate the Pareto frontier. In this work, we present a new idea to incorporate preferences into a multi-objective evolutionary algorithm (MOEA). We introduce a binary fuzzy preference relation that expresses the degree of truth of the predicate “x is at least as good as y”. On this basis, a strict preference relation with a reasonably high degree of credibility can be established on any population. An alternative x is not strictly outranked if and only if there does not exist an alternative y which is strictly preferred to x. It is easy to prove that the best solution is not strictly outranked. For validating our proposed approach, we used the non-dominated sorting genetic algorithm II (NSGA-II), but replacing Pareto dominance by the above non-outranked concept. So, we search for the non-strictly outranked frontier that is a subset of the Pareto frontier. In several instances of a nine-objective knapsack problem our proposal clearly outperforms the standard NSGA-II, achieving non-outranked solutions which are in an obviously privileged zone of the Pareto frontier.     

An improved particle swarm optimizer based on tabu detecting and local learning strategy in a shrunk search space

To improve the performance of the standard particle swarm optimization (PSO) which suffers from premature convergence and slow convergence speed, many PSO variants introduce lots of stochastic or aimless strategies to overcome the convergence problem. However, the mutual learning between elites particles is omitted, although which might be benefit to the convergence speed and, prevent the premature convergence. In this paper, we introduce DSLPSO, which integrates three novel strategies, specifically, tabu detecting, shrinking and local learning strategies, into PSO to overcome the aforementioned shortcomings. In DSLPSO, search space of each dimension is divided into many equal subregions. Then the tabu detecting strategy, which has good ergodicity for search space, helps the global historical best particle to detect a more suitable subregion, and thus help it jump out of a local optimum. The shrinking strategy enables DSLPSO to take optimization in a smaller search space and obtain a higher convergence speed. In the local learning strategy, a differential between two elites particles is used to increase solution accuracy. The experimental results show that DSLPSO has a superior performance in comparison with several other participant PSOs on most of the tested functions, as well as offering faster convergence speed, higher solution accuracy and stronger reliability.     

区间混合性能指标优化问题的自适应遗传算法

为了解决区间混合性能指标优化问题,在此提出了一种自适应进化优化方法。首先,基于前后代最优个体的距离,计算种群的收敛进度;然后,基于种群的多样性、收敛进度,以及进化代数,计算进化种群的交叉和变异概率;最后,将所提算法应用于室内布局这一典型的区间混合性能指标优化问题,并与其他算法比较,实验结果表明,所提算法在最优解数目、性能,以及分布性等方面均具有优越性。     

求解多目标最小生成树的一种新的遗传算法

在改进的非支配排序遗传算法（NSGA-II）的基础上,提出了一种新的基于生成树边集合编码的繁殖算子求解多目标最小生成树问题的遗传算法。通过快速非支配排序法,降低了算法的计算复杂度,引入保存精英策略,扩大采样空间。实验结果表明：对于多目标最小生成树问题,边集合编码具有较好的遗传性和局部性,而且基于此繁殖算子的遗传算法在求解效率和解的质量方面都优于基于PrimRST的遗传算法。     

改进差分进化算法在可靠性冗余分配问题中的应用

提出一种改进差分进化算法(IDE),以解决系统可靠性冗余分配问题.在罚函数法的基础上,对约束处理方法进行改进. 新约束处理方法在搜索过程中不需要在每一步都计算惩罚函数值,加快了寻优速度.具有良好的通用性,可以引入到其他智能优化算法中.将改进的算法用于求解4类典型的系统可靠性冗余分配问题,实验结果表明了所提出的改进算法具有很好的寻优精度和收敛速度.     

Directed searching optimization algorithm for constrained optimization problems

A directed searching optimization algorithm (DSO) is proposed to solve constrained optimization problems in this paper. The proposed algorithm includes two important operations — position updating and genetic mutation. Position updating enables the non-best solution vectors to mimic the best one, which is beneficial to the convergence of the DSO; genetic mutation can increase the diversity of individuals, which is beneficial to preventing the premature convergence of the DSO. In addition, we adopt the penalty function method to balance objective and constraint violations. We can obtain satisfactory solutions for constrained optimization problems by combining the DSO and the penalty function method. Experimental results indicate that the proposed algorithm can be an efficient alternative on solving constrained optimization problems.     

基于环境经济调度问题的空间自适应划分多目标优化

提出一种基于空间自适应划分的多目标优化算法. 为了增强种群的收敛性和多样性, 多维搜索空间被划分成多个网格, 网格内的粒子通过共享“引导”粒子的经验信息调整自身的速度和位置, 并引入年龄观测器实时记录引导粒子对Pareto 解集所做的贡献, 及时更新引导粒子, 以增强算法的全局搜索能力. 对多目标测试函数以及环境经济调度问题进行了仿真实验, 实验结果表明, 所提出算法能对解空间进行更加全面、充分的探索, 快速找到一组分布具有较好的逼近性、宽广性和均匀性的最优解集合.

     

Vehicle power train optimization using multi-objective bird swarm algorithm

In this paper, a multi-objective bird swarm algorithm (MOBSA) is proposed to cope with multi-objective optimization problems. The algorithm is explored based on BSA which is an evolutionary algorithm suitable for single objective optimization. In this paper, non-dominated sorting approach is used to distinguish optimal solutions and parallel coordinates is applied to evaluate the distribution density of non-dominated solution and further update the external archive when it is full to overflowing, which ensure faster convergence and more widespread of Pareto front. Then, the MOBSA is adopted to optimize benchmark problems. The results demonstrate that MOBSA gets better performance compared with NSGA-II and MOPSO. Since a vehicle power train problem could be treated as a typical multi-objective optimization problem with constraints, with integration of constrained non-dominated solution, MOBSA is adopted to acquire optimal gear ratios and optimize vehicle power train. The results compared with other popular algorithm prove the proposed algorithm is more suitable for constrained multi-objective optimization problem in engineering field.

     

On-line emission and economic load dispatch using adaptive Hopfield neural network

This paper presents an adaptive Hopfield neural network (AHNN) based methodology, where the slope of the activation function is adjusted, for finding approximate Pareto solutions for the multi-objective optimization problem of emission and economic load dispatch (EELD). We have placed emphasis on finding solutions quickly, rather than the global Pareto solutions, so that our algorithm can be employed in large on-line power systems where variations in load are quite frequent. To enable faster convergence, adaptive learning rates have been developed by using energy function and applied to the slope adjustment method. The proposed algorithm has been tested on selected IEEE bus benchmark systems. The convergence of AHNN is found to be nearly 50% faster than the non-adaptive version.     

A hierarchical particle swarm optimizer for noisy and dynamic environments

New Particle Swarm Optimization (PSO) methods for dynamic and noisy function optimization are studied in this paper. The new methods are based on the hierarchical PSO (H-PSO) and a new type of H-PSO algorithm, called Partitioned Hierarchical PSO (PH-PSO). PH-PSO maintains a hierarchy of particles that is partitioned into several sub-swarms for a limited number of generations after a change of the environment occurred. Different methods for determining the best time when to rejoin the sub-swarms and how to handle the topmost sub-swarm are discussed. A standard method for metaheuristics to cope with noise is to use function re-evaluations. To reduce the number of necessary re-evaluations a new method is proposed here which uses the hierarchy to find a subset of particles for which re-evaluations are particularly important. In addition, a new method to detect changes of the optimization function in the presence of noise is presented. It differs from conventional detection methods because it does not require additional function evaluations. Instead it relies on observations of changes that occur within the swarm hierarchy. The new algorithms are compared experimentally on different dynamic and noisy benchmark functions with a variant of standard PSO and H-PSO that are both provided with a change detection and response method.

自适应扩散混合变异机制微粒群算法

为了避免微粒群算法(particle swarm optimization,简称PSO)在全局优化中陷入局部极值,分析了标准PSO算法早熟收敛的原因,提出了自适应扩散混合变异机制微粒群算法(InformPSO).结合生物群体信息扩散的习性,设计了一个考虑微粒分布和迭代次数的函数,自适应调整微粒的"社会认知"能力,提高种群的多样性;模拟了基因自组织和混沌进化规律,引入克隆选择使群体最佳微粒gBest实现遗传微变、局部增值,具有变异确定性;利用Logistic序列指导gBest随机漂移,进一步增强逃离局部极值能力.基于种群的随机状态转移过程,证明了新算法具有全局收敛性.与其他几种PSO变种相比,复杂基准函数仿真优化结果表明,新算法收敛速度快,求解精度高,稳定性好,能够有效抑制早熟收敛.     

改进搜索策略的人工蜂群算法

针对人工蜂群(ABC)算法存在收敛速度慢、收敛精度低的问题,给出一种改进的人工蜂群算法用于数值函数优化问题。在ABC的邻域搜索公式中利用目标函数自适应调整步长,并根据迭代次数非线性减小侦查蜂的搜索范围。改进ABC算法提高了ABC算法的局部搜索能力,能够有效避免早熟收敛。基于6个标准测试函数的仿真实验表明,改进ABC算法的寻优能力有较大提高,对于多个高维多模态函数该算法可取得理论全局最优解。与对比算法相比,该算法具有更高的收敛精度,并且收敛速度更快。     

带两类正态变异的PSO 算法

针对基本粒子群优化算法(PSO)容易陷入局部最优点和收敛速度较慢的缺点,提出在PSO更新过程中加入两类基于正态分布投点的变异操作。一类变异用来增强局部搜索能力,另一类变异用来提高发现全局最优点的能力,避免所有粒子陷入到一个局部最优点的邻域内。数值结果表明,所提出算法的全局搜索能力有显著提高,并且收敛速度更快。     

Pareto frontier for simultaneous structural and manufacturing optimization of a composite part

Optimized design of composite structures requires simultaneous optimization of structural performance and manufacturing process. Such a challenge calls for a multi-objective optimization. Here, a generating multi-objective optimization method called normalized normal constraint method, which attains a set of optimal solutions and allows the designer to explore design alternatives before making the final decision, is coupled with a local-global search called constrained globalized bounded Nelder–Mead method. The proposed approach is applied to the design of a Z-shaped composite bracket for optimization of structural and manufacturing objectives. Comparison of the results with non-dominated sorting genetic algorithm (NSGA-II) shows that when only a small number of function evaluations are possible and a few Pareto optima are desired, the proposed method outperforms NSGA-II in terms of convergence to the true Pareto frontier. The results are validated by an enumeration search and by exploring the neighbourhood of the final solutions.     

Improving convergence of evolutionary multi-objective optimization with local search: a concurrent-hybrid algorithm

A local search method is often introduced in an evolutionary optimization algorithm, to enhance its speed and accuracy of convergence to optimal solutions. In multi-objective optimization problems, the implementation of local search is a non-trivial task, as determining a goal for local search in presence of multiple conflicting objectives becomes a difficult task. In this paper, we borrow a multiple criteria decision making concept of employing a reference point based approach of minimizing an achievement scalarizing function and integrate it as a search operator with a concurrent approach in an evolutionary multi-objective algorithm. Simulation results of the new concurrent-hybrid algorithm on several two to four-objective problems compared to a serial approach, clearly show the importance of local search in aiding a computationally faster and accurate convergence to the Pareto optimal front.     

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.     

Particle swarm optimization with selective particle regeneration for data clustering

This paper presents selective regeneration particle swarm optimization (SRPSO), a novel algorithm developed based on particle swarm optimization (PSO). It contains two new features, unbalanced parameter setting and particle regeneration operation. The unbalanced parameter setting enables fast convergence of the algorithm and the particle regeneration operation allows the search to escape from local optima and explore for better solutions. This algorithm is applied to data clustering problems for performance evaluation and a hybrid algorithm (KSRPSO) of K-means clustering method and SRPSO is developed. In the conducted numerical experiments, SRPSO and KSRPSO are compared to the original PSO algorithm, K-means, as well as, other methods proposed by other studies. The results demonstrate that SRPSO and KSRPSO are efficient, accurate, and robust methods for data clustering problems.     

A Human-Robot Collaborative Reinforcement Learning Algorithm

This paper presents a new reinforcement learning algorithm that enables collaborative learning between a robot and a human. The algorithm which is based on the Q(λ) approach expedites the learning process by taking advantage of human intelligence and expertise. The algorithm denoted as CQ(λ) provides the robot with self awareness to adaptively switch its collaboration level from autonomous (self performing, the robot decides which actions to take, according to its learning function) to semi-autonomous (a human advisor guides the robot and the robot combines this knowledge into its learning function). This awareness is represented by a self test of its learning performance. The approach of variable autonomy is demonstrated and evaluated using a fixed-arm robot for finding the optimal shaking policy to empty the contents of a plastic bag. A comparison between the CQ(λ) and the traditional Q(λ)-reinforcement learning algorithm, resulted in faster convergence for the CQ(λ) collaborative reinforcement learning algorithm.     

基于混合遗传算法的重入式流水线调度研究

针对可重入式生产流水线的大规模调度优化问题,提出了一种改进的混合遗传算法。在计算适应值的过程中利用信息素算法的正反馈机制,解决工件在重入环节的竞争问题。并将禁忌搜索算法中独有的记忆功能引入到遗传算法的交叉与变异的进化过程中,可以有效的避免算法早熟和迂回搜索。通过实例仿真,表明改进后的算法相比于普通的遗传算法不仅有较强的收敛性,并且具有更快的寻优功能,是解决复杂调度问题的有效算法。