化学蚁群算法与多组分导数荧光光谱解析

在分析化学计量学中 ,每一种新算法的诞生都会带动新一轮的研究热潮的掀起 ,并极大的推动着化学计量学的发展 .因此 ,积极开展分析化学计量学新算法的研究具有重大的理论和实际意义 .蚁群算法 ( Ant Colony Algorithm,缩写 ACA)也称蚁群系统 ( ACS) ,是意大利学者 Dorigo等[1] 新近提出的一种模拟进化算法 .该算法具有正反馈、分布式计算、鲁棒性强及易与其它算法相结合等突出优点 ,是求解组合优化问题的一种尚佳方法 .目前已被成功地应于通讯、交通和人工智能等领域[2～ 5] .尤其是最近用蚁群算法编程的微型机器人的问世 [6 ] ,更引起…     

Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

Ant colony optimization (ACO) is a meta-heuristic algorithm, which is derived from the observation of real ants. In this paper, ACO algorithm is proposed to feature selection in quantitative structure property relationship (QSPR) modeling and to predict λ_max of 1,4-naphthoquinone derivatives. Feature selection is the most important step in classification and regression systems. The performance of the proposed algorithm (ACO) is compared with that of a stepwise regression, genetic algorithm and simulated annealing methods. The average absolute relative deviation in this QSPR study using ACO, stepwise regression, genetic algorithm and simulated annealing using multiple linear regression method for calibration and prediction sets were 5.0%, 3.4% and 6.8%, 6.1% and 5.1%, 8.6% and 6.0%, 5.7%, respectively. It has been demonstrated that the ACO is a useful tool for feature selection with nice performance.     

基于智能优化算法的车间调度问题研究

车间的生产调度是一个非常复杂的问题,本文主要介绍车间调度问题模型以及蚁群算法、遗传算法、模拟退火算法等智能优化算法的研究情况,有效的生产调度方法和智能优化算法的应用,在很大程度上可以提高企业的效益.     

Optimal domain decomposition via -median methodology using ACO and hybrid ACGA

In this paper an efficient method is developed for decomposing large-scale finite element meshes. A weighted incidence graph is used to transform the connectivity properties of finite element models into those of graphs. A graph G_c of manageable size is obtained from the main graph model by a coarsening algorithm. The p-medians of this graph are selected using two approaches. The first algorithm uses an ant colony optimization and the second algorithm employs a hybrid ant colony together with genetic algorithm. Here, p is the number of subdomains which the finite element meshes is intended to be decomposed. Once the medians are obtained, the nodes in G_c associated with each median are selected. In an expansion process, the nodes of the subdomains in G are obtained. The capabilities of both ant colony optimization, and hybrid ant colony and genetic algorithm are evaluated using many examples of different topology.     

Antcolony combined with adaptive threshold denoising and reconstruct for THz image

In this paper, an ant colony combined with adaptive threshold denoising algorithm for THz image is proposed. The ant colony is introduced to tackle the image edge detection problem; it is able to establish a pheromone matrix that represents the edge information presented at each pixel position of the image, according to the movements of a number of ants which are dispatched to move on the image. The adaptive threshold is derived in a Bayesian framework; it is adaptive to each subband because it depends on data-driven estimates of the parameters, and it is used to handle edge image and non-edge image which are obtained from ant colony. Experimental results are provided to show that the proposed approach outperforms several THz image denoising approaches developed in the traditional and literature.     

Simultaneous Determination of Guaifenesin and Theophylline by Chemometrics Methods

In this study, simultaneous spectrophotometry determination of guaifenesin and theophylline in pharmaceuticals by chemometric approaches has been reported. Spectra of mixtures of these drugs were recorded and corresponding first derivatives were calculated. Partial least squares regression (PLS) alone and ant colony optimization (ACO) coupled with PLS were used in analysis of the data. Ant colony system (ACS) as an efficient ACO algorithm was used. In addition, ACS was combined to genetic algorithm (GA) to produce better results. The analytical performances of these chemometric methods were characterized by relative prediction errors. These methods were successfully applied to pharmaceutical formulation.     

Stochastic dynamic lot-sizing problem using bi-level programming base on artificial intelligence techniques

Simulation is generally used to study non-deterministic problems in industry. When a simulation process finds the solution to an NP-hard problem, its efficiency is lowered, and computational costs increase. This paper proposes a stochastic dynamic lot-sizing problem with asymmetric deteriorating commodity, in which the optimal unit cost of material and unit holding cost would be determined. This problem covers a sub-problem of replenishment planning, which is NP-hard in the computational complexity theory. Therefore, this paper applies a decision system, based on an artificial neural network (ANN) and modified ant colony optimization (ACO) to solve this stochastic dynamic lot-sizing problem. In the methodology, ANN is used to learn the simulation results, followed by the application of a real-valued modified ACO algorithm to find the optimal decision variables. The test results show that the intelligent system is applicable to the proposed problem, and its performance is better than response surface methodology.     

Multi-ant colony system (MACS) for a vehicle routing problem with backhauls

The vehicle routing problem with backhaul (VRPB) is an extension of the capacitated vehicle routing problem (CVRP). In VRPB, there are linehaul as well as backhaul customers. The number of vehicles is considered to be fixed and deliveries for linehaul customers must be made before any pickups from backhaul customers. The objective is to design routes for the vehicles so that the total distance traveled is minimized. We use multi-ant colony system (MACS) to solve VRPB which is a combinatorial optimization problem. Ant colony system (ACS) is an algorithmic approach inspired by foraging behavior of real ants. Artificial ants are used to construct a solution by using pheromone information from previously generated solutions. The proposed MACS algorithm uses a new construction rule as well as two multi-route local search schemes. An extensive numerical experiment is performed on benchmark problems available in the literature.     

Distributed supply chain management using ant colony optimization

Successful supply chain management requires a cooperative integration between all the partners in the network. At the operational level, the partners individual behavior should be optimal and therefore their activities have to be planned using sophisticated optimization tools. However, these tools should take into account the planning of the remaining partners, through the exchange of information, in order to allow some kind of cooperation between the elements of the chain. This paper introduces a new supply chain management technique, based on modeling a generic supply chain with suppliers, logistics and distributers, as a distributed optimization problem. The different operational activities are solved by the optimization meta-heuristic called ant colony optimization, which allows the exchange of information between different optimization problems by means of a pheromone matrix. The simulation results show that the new methodology is more efficient than a simple decentralized methodology for different instances of a supply chain.     

多层防御模式下武器目标分配决策的群体智能优化算法

武器目标分配(WTA)是军事运筹学中经典的NP完全问题,迄今为止未找到求精确解的多项式时间算法.针对武器数量、布防空间、运行维护成本以及人力资源等多约束下的多层防御WTA问题,采用粒子群优化(PSO)和蚁群优化(ACO)两种群体智能算法求解.给出了PSO和ACO算法实现方案,通过一个算例评估两个算法的性能.结果表明,两种算法都能给出高质量的近似最优解,对求解WTA问题是有效的.PSO在解的质量、算法鲁棒性和计算效率方面均优于ACO.