基于风驱动优化算法WDO的PID参数优化

鉴于PID控制器的优越性,其在工业控制领域中的引用越来越广泛。PID控制器的性能主要在于其参数优化设计,PID参数优化问题一直是研究热点。为了解决PID参数优化问题,提出了一种基于自然启发的风驱动优化算法（WDO）的PID优化控制方法,该算法以PID三个参量为控制对象,以误差绝对值和控制输入平方项的时间积分作为优化目标,经过迭代寻优计算得到系统最优控制量。通过计算机仿真,并与遗传算法和粒子群算法PID参数优化相比,结果表明：该算法提高了系统的控制精度、响应速度和鲁棒性,为控制系统PID参数整定提供了参考。     

Haplotype inference using a novel binary particle swarm optimization algorithm

The knowledge of haplotypes allows researchers to identify the genetic variation affecting phenotypic such as health, disease and response to drugs. However, getting haplotype data by experimental methods is both time-consuming and expensive. Haplotype inference (HI) from the genotypes is a challenging problem in the genetics domain. There are several models for inferring haplotypes from genotypes, and one of the models is known as haplotype inference by pure parsimony (HIPP) which aims to minimize the number of distinct haplotypes used. The HIPP was proved to be an NP-hard problem. In this paper, a novel binary particle swarm optimization (BPSO) is proposed to solve the HIPP problem. The algorithm was tested on variety of simulated and real data sets, and compared with some current methods. The results showed that the method proposed in this paper can obtain the optimal solutions in most of the cases, i.e., it is a potentially powerful method for HIPP.     

The continuous artificial bee colony algorithm for binary optimization

Artificial bee colony (ABC) algorithm, one of the swarm intelligence algorithms, has been proposed for continuous optimization, inspired intelligent behaviors of real honey bee colony. For the optimization problems having binary structured solution space, the basic ABC algorithm should be modified because its basic version is proposed for solving continuous optimization problems. In this study, an adapted version of ABC, ABC_bin for short, is proposed for binary optimization. In the proposed model for solving binary optimization problems, despite the fact that artificial agents in the algorithm works on the continuous solution space, the food source position obtained by the artificial agents is converted to binary values, before the objective function specific for the problem is evaluated. The accuracy and performance of the proposed approach have been examined on well-known 15 benchmark instances of uncapacitated facility location problem, and the results obtained by ABC_bin are compared with the results of continuous particle swarm optimization (CPSO), binary particle swarm optimization (BPSO), improved binary particle swarm optimization (IBPSO), binary artificial bee colony algorithm (binABC) and discrete artificial bee colony algorithm (DisABC). The performance of ABC_bin is also analyzed under the change of control parameter values. The experimental results and comparisons show that proposed ABC_bin is an alternative and simple binary optimization tool in terms of solution quality and robustness.     

Model driven test case generation and optimization using adaptive cuckoo search algorithm

Innovations in Systems and Software Engineering - Software testing is leading toward automation that reduces the effort to find errors or bugs. The identification of test cases and its critical...     

折半查找算法在最优化问题中的应用

折半查找是一种常见的静态查找方法,在特定的、有序的查找区间内,通过折半方式不断地缩小查找区间,将区间中间位置的元素与给定元素加以比较,最终确定查找结果.在此传统折半查找基础上,总结了一种抽象化的改进方法,并将此改进后的折半查找算法应用于最优化问题的求解.     

求解双层规划优化问题的层次风驱动优化算法

针对双层规划的求解问题, 提出一种层次风驱动优化算法. 初始化上层优化变量后, 首先对下层规划进行求解, 满足约束条件的同时, 更新下层规划中的空气质点速度和位置; 然后, 利用风驱动优化算法对上层规划问题进行求解; 最后, 在优化解集合中, 选择上下层规划目标值次序之和最小的解作为最终优化解. 实验结果表明, 所提出的层次风驱动算法是一种有效的求解双层规划问题的方法.

     

一种求解二元约束满足问题自适应粒子群算法

提出了一种求解二元约束满足问题的自适应粒子群算法（SAPSO）,其中每个粒子具有两种状态,定义了一个反应粒子活跃程度的变量以决定粒子所属的状态。为了平衡粒子不同进化阶段的开发和探测能力,在SAPSO中引入了随着每个粒子的进化状态和粒子群的进化状态动态改变的惯性权重。利用自适应的选取方式代替随机选择的盲目搜索方式,使群体在解空间搜索时,能够自适应地去探索新的区域,选择有希望找到更优解的地方搜索。使用随机约束满足问题的实验表明,改进后的算法比原算法（PS-CSP）能以更快的速度收敛到全局解。算法的效率大约提高两倍,平均迭代次数大约为原来的一半。     

Multi-objective optimization using teaching-learning-based optimization algorithm

Two major goals in multi-objective optimization are to obtain a set of nondominated solutions as closely as possible to the true Pareto front (PF) and maintain a well-distributed solution set along the Pareto front. In this paper, we propose a teaching-learning-based optimization (TLBO) algorithm for multi-objective optimization problems (MOPs). In our algorithm, we adopt the nondominated sorting concept and the mechanism of crowding distance computation. The teacher of the learners is selected from among current nondominated solutions with the highest crowding distance values and the centroid of the nondominated solutions from current archive is selected as the Mean of the learners. The performance of proposed algorithm is investigated on a set of some benchmark problems and real life application problems and the results show that the proposed algorithm is a challenging method for multi-objective algorithms.     

N状态二进制一致性算法设计及其优化

针对二进制一致性算法扩展性差、经验依赖性强的缺点,提出了一种N状态分布式二进制一致性算法。首先,基于Gossip算法的平均一致性思想和轮盘赌思想,更新无线传感器网络状态均值和当前状态均值的偏差程度,计算所有可能更新状态的初始概率分布;然后,利用遗传算法优化初始概率分布,得到准确率较高的最优概率分布。仿真结果表明,在相同状态个数条件下,本文所设计的算法具有更好的准确率和收敛时间。     

Particle swarm optimization algorithm driven by multichaotic number generator

In this paper, the utilization of different chaotic systems as pseudo-random number generators (PRNGs) for velocity calculation in the PSO algorithm are proposed. Two chaos-based PRNGs are used alternately within one run of the PSO algorithm and dynamically switched over when a certain criterion is met. By using this unique technique, it is possible to improve the performance of PSO algorithm as it is demonstrated on different benchmark functions.     

改进的带经验因子的二进制粒子群优化算法

针对传统二进制粒子群优化(BPSO)算法未充分利用粒子位置的历史信息辅助迭代寻优,从而影响算法寻优效率的进一步提高的问题,提出一种改进的带经验因子的BPSO算法。该算法通过引入反映粒子位置历史信息的经验因子来影响粒子速度的更新,从而引导粒子寻优。为避免粒子对历史信息的过度依赖,算法通过赏罚机制和历史遗忘系数对其进行调节,最后通过经验权重决定经验因子对速度更新的影响。仿真实验结果表明,与经典BPSO算法以及相关改进算法相比,新算法无论在收敛速度还是全局搜索能力上,都能达到更好的效果。     

A hybrid optimization approach for hyperspectral band selection based on wind driven optimization and modified cuckoo search optimization

Multimedia Tools and Applications - Selection of useful bands plays a very important role in hyperspectral image classification. In the past decade, metaheuristic algorithms have been used as...     

二进制粒子群算法在路径规划中的应用

全局路径规划是智能机器人的一个重要研究领域,将二进制粒子群算法应用于路径规划是一种新的尝试.提出一种机器人路径全局路径规划方法,介绍了利用改进的二进制粒子群算法进行路径规划的详细实现过程.机器人工作空间中的障碍物表示为多边形,对多边形顶点进行编号.利用二进制粒子群算法进行路径规划,粒子的长度定义为工作环境中障碍物顶点的个数,每一位为0或1表示路径是否经过该顶点.为了克服传统的二进制粒子群算法的早熟收敛问题,在改进的算法中采用了双重编码结构,并引入变异操作.最后给出仿真结果证明该方法的正确性和有效性.     

Fast synthesis of cross‐coupled resonator filters using hybrid optimization algorithm

This article describes a hybrid optimization algorithm for synthesizing prototype filters with arbitrary topology. Two cost functions are constructed from the eigenvalues of the coupling matrix and its principal submatrices or at specially selected frequencies involving the values S₁₁ and S₂₁. The values of non‐zero elements of the coupling matrix are found using solvopt and fmincon to minimize the cost functions. With the initial coupling matrix synthesized in a tridiagonal form by solving the Jacobi inverse eigenvalue problem, the proposed method provides fast convergence and good accuracy to find the final solution. In addition, as the hybrid method may be run from random initial variables whose values lie within specified limits, it has the potential to be used for series of coupling matrix synthesis problems. For demonstrating the proposed hybrid optimization algorithm, some extraordinary prototype topologies have been synthesized to validate the proposed synthesis procedure. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:445–452, 2015.     

精英交叉二进制蝙蝠算法求解0-1背包问题*

针对基本蝙蝠算法收敛速度慢,易早熟的问题,提出了一种精英交叉二进制蝙蝠算法。该算法借鉴精英策略和遗传算法中的交叉机制,按照一定比例选择蝙蝠群中的精英个体进行交叉,将得到子蝙蝠群和父蝙蝠群进行混合择优,保证蝙蝠群的多样性和优秀性,提高了全局搜索能力;为提高局部搜索能力,算法在对每个个体计算适应度值时加入贪心策略;另外,通过对蝙蝠群最优解进行动态监测,适时对种群进行柯西变异,使算法具有跳出局部极值的能力。通过对5个实例的仿真计算比较表明,该算法与改进贪心遗传算法,贪心二进制蝙蝠算法和病毒协同蝙蝠算法相比,无论是收敛速度还是寻优能力都表现优异,为求解0-1背包问题提供了一个实用的算法。     

Global optimization in clustering using hyperbolic cross points

Erich Novak and Klaus Ritter developed in 1996 a global optimization algorithm that uses hyperbolic cross points (HCPs). In this paper we develop a hybrid algorithm for clustering called CMHCP that uses a modified version of this HCP algorithm for global search and the alternating optimization for local search. The program has been tested extensively with very promising results and high efficiency. This provides a nice addition to the arsenal of global optimization in clustering. In the process, we also analyze the smoothness of some reformulated objective functions.     

Association rule mining using binary particle swarm optimization

In this paper, we developed a binary particle swarm optimization (BPSO) based association rule miner. Our BPSO based association rule miner generates the association rules from the transactional database by formulating a combinatorial global optimization problem, without specifying the minimum support and minimum confidence unlike the a priori algorithm. Our algorithm generates the best M rules from the given database, where M is a given number. The quality of the rule is measured by a fitness function defined as the product of support and confidence. The effectiveness of our algorithm is tested on a real life bank dataset from commercial bank in India and three transactional datasets viz. books database, food items dataset and dataset of the general store taken from literature. Based on the results, we infer that our algorithm can be used as an alternative to the a priori algorithm and the FP-growth algorithm.     

JayaX: Jaya algorithm with xor operator for binary optimization

Jaya is a population-based heuristic optimization algorithm proposed for solving constrained and unconstrained optimization problems. The peculiar distinct feature of Jaya from the other population-based algorithms is that it updates the positions of artificial agent in the population by considering the best and worst individuals. This is an important property for the algorithm to balance exploration and exploitation on the solution space. However, the basic Jaya cannot be applied to binary optimization problems because the solution space is discretely structured for this type of optimization problems and the decision variables of the binary optimization problems can be element of set [0,1]. In this study, we first focus on discretization of Jaya by using a logic operator, exclusive or – xor. The proposed idea is simple but effective because the solution update rule of Jaya is replaced with the xor operator, and when the obtained results are compared with the state-of-art algorithms, it is seen that the Jaya-based binary optimization algorithm, JayaX for short, produces better quality results for the binary optimization problems dealt with the study. The benchmark problems in this study are uncapacitated facility location problems and CEC2015 numeric functions, and the performance of the algorithms is compared on these problems. In order to improve the performance of the proposed algorithm, a local search module is also integrated with the JayaX. The obtained results show that the proposed algorithm is better than the compared algorithms in terms of solution quality and robustness.     

Structural topology optimization using ant colony optimization algorithm

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.     

Modification of binary optimization algorithm and use small digit capacity numbers

The article deals with the neural minimization of the quadratic functional in a space of binary variables. A modified minimization algorithm based on the discretization method is offered to tackle the problem. The use of the algorithm is shown to allow a smaller amount of computations.