A bi-objective mathematical model toward dynamic cell formation considering labor utilization

This paper addresses dynamic cell formation problem (DCFP) via a new bi-objective mathematical formulation. Although literature body of DCFP includes a vast number of research instances, human-related issues are mostly neglected as an important aspect of DCFP in the corresponding literature. In this paper, the first objective function seeks to minimize relevant costs of the problem including machine procurement and relocation costs, machine variable cost, inter-cell movement and intra-cell movement costs, overtime cost and labor shifting cost between cells, while labor utilization of the modeled DCFP is maximized through the second objective function. Due to NP-hardness of DCFP, an ant colony optimization (ACO) meta-heuristic is developed for the first time in the literature to tackle the problem. Also, authors enhance diversification of the developed algorithm is enhanced by hybridization of the proposed ACO algorithm with a genetic one. Finally, some numerical samples are generated randomly to validate the proposed model by which strengths of the developed algorithm is shown.     

An Ant Colony Optimisation algorithm for solving the asymmetric traffic assignment problem

In this paper we propose an Ant Colony Optimisation (ACO) algorithm for defining the signal settings on urban networks following a local approach. This consists in optimising the signal settings of each intersection of an urban network as a function only of traffic flows at the accesses to the same intersection, taking account of the effects of signal settings on costs and on user route choices. This problem, also known as Local Optimisation of Signal Settings (LOSS), has been widely studied in the literature and can be formulated as an asymmetric assignment problem. The proposed ACO algorithm is based on two kinds of behaviour of artificial ants which allow the LOSS problem to be solved: traditional behaviour based on the response to pheromones for simulating user route choice, and innovative behaviour based on the pressure of an ant stream for solving the signal setting definition problem. Our results on real-scale networks show that the proposed approach allows the solution to be obtained in less time but with the same accuracy as in traditional MSA (Method of Successive Averages) approaches.     

Ant colony optimization: Introduction and recent trends

Ant colony optimization is a technique for optimization that was introduced in the early 1990's. The inspiring source of ant colony optimization is the foraging behavior of real ant colonies. This behavior is exploited in artificial ant colonies for the search of approximate solutions to discrete optimization problems, to continuous optimization problems, and to important problems in telecommunications, such as routing and load balancing. First, we deal with the biological inspiration of ant colony optimization algorithms. We show how this biological inspiration can be transfered into an algorithm for discrete optimization. Then, we outline ant colony optimization in more general terms in the context of discrete optimization, and present some of the nowadays best-performing ant colony optimization variants. After summarizing some important theoretical results, we demonstrate how ant colony optimization can be applied to continuous optimization problems. Finally, we provide examples of an interesting recent research direction: The hybridization with more classical techniques from artificial intelligence and operations research.     

基于蚁群算法的二维最大熵分割算法

由于二维最大熵分割法不仅考虑了像素的灰度信息,而且还充分利用了像素的空间邻域信息,因此能够取得较好的分割效果.但是,该方法的计算量巨大,不利于红外图像的快速处理.蚁群算法于20世纪90年代初提出,是受到蚁群集体行为的启发而提出的一种基于种群的模拟进化算法,属于随机搜索算法.该算法已经成功应用于旅行商等离散问题.将蚁群算法应用于二维最大熵法,提出了基于蚁群算法的二维最大熵分割算法.与传统的穷尽搜索法相比,求解速度提高了60倍左右.仿真实验表明,该方法快速、简单、有效.     

An effective ant colony optimization algorithm (ACO) for multi-objective resource allocation problem (MORAP)

The multi-objective resource allocation problem (MORAP) addresses the important issue which seeks to find the expected objectives by allocating the limited amount of resource to various activates. Resources may be manpower, assets, raw material or anything else in limited supply which can be used to accomplish the goals. The goals may be objectives (i.e., minimizing costs, or maximizing efficiency) usually driven by specific future needs. In this paper, in order to obtain a set of Pareto solution efficiently, we proposed a modified version of ant colony optimization (ACO), in this algorithm we try to increase the efficiency of algorithm by increasing the learning of ants. Effectiveness and efficiency of proposed algorithm was validated by comparing the result of ACO with hybrid genetic algorithm (hGA) which was applied to MORAP later.     

An improved ant colony optimisation heuristic for graph colouring

The focus of this paper is an ant colony optimisation heuristic for the graph colouring problem. We start by showing how a series of improvements enhance the performance of an existing ant colony approach to the problem and then go on to demonstrate that a further strengthening of the construction phase, combined with a tabu search improvement phase, raise the performance to the point where it is able to compete with some of the best-known approaches on a series of benchmark problems.     

Ant colony optimization for power plant maintenance scheduling optimization—a five-station hydropower system

A number of algorithms have been developed for the optimization of power plant maintenance schedules. However, the true test of such algorithms occurs when they are applied to real systems. In this paper, the application of an Ant Colony Optimization formulation to a hydropower system is presented. The formulation is found to be effective in handling various constraints commonly encountered in practice. Overall, the results obtained using the ACO formulation are better than those given by traditional methods using engineering judgment, which indicates the potential of ACO in solving realistic power plant maintenance scheduling problems.     

Desarrollo de un algoritmo de optimización global en colonias de hormigas con selección de región factible para espacios continuos

This study introduces a new algorithm for the ant colony optimization (ACO) method, which has been proposed to solve global optimization problems with continuous decision variables. This algorithm, namely ACO-FRS, involves a strategy for the selection of feasible regions during optimization search and it performs the exploration of the search space using a similar approach to that used by the ants during the search of food. Four variants of this algorithm have been tested in several benchmark problems and the results of this study have been compared with those reported in literature for other ACO-type methods for continuous spaces. Overall, the results show that the incorporation of the selection of feasible regions allows the performing of a global search to explore those regions with low level of pheromone, thus increasing the feasibility of ACO for finding the global optimal solution.     

A multi-objective-ACO-based data association method for bearings-only multi-target tracking

The study is concerned with data association of bearings-only multi-target tracking using two stationary observers in a 2-D scenario. In view of each target moving with a constant speed, two objective functions, i.e., distance and slope differences, are proposed and a multi-objective-ant-colony-optimization-based algorithm is then introduced to execute data association by minimizing the two objective functions. Numerical simulations are conducted to evaluate the effectiveness of the proposed algorithm in comparison with the data association results of the joint maximum likelihood (ML) method under different noise levels and track figurations.     

On the Finite-Time Dynamics of Ant Colony Optimization

An analytical framework for investigating the finite-time dynamics of ant colony optimization (ACO) under a fitness-proportional pheromone update rule on arbitrary construction graphs is developed. A limit theorem on the approximation of the stochastic ACO process by a deterministic process is demonstrated, and a system of ordinary differential equations governing the process dynamics is identified. As an example for the application of the presented theory, the behavior of ACO on three different construction graphs for subset selection problems is analyzed and compared for some basic test functions. The theory enables first rough theoretical predictions of the convergence speed of ACO. AMS 2000 Subject classification 68W20, 68W40