Two-stage updating pheromone for invariant ant colony optimization algorithm

Ant colony optimization (ACO) is a metaheuristic approach for combinatorial optimization problems. With the introduction of hypercube framework, invariance property of ACO algorithms draws more attention. In this paper, we propose a novel two-stage updating pheromone for invariant ant colony optimization (TSIACO) algorithm. Compared with standard ACO algorithms, TSIACO algorithm uses solution order other than solution itself as independent variable for quality function. In addition, the pheromone trail is updated with two stages: in one stage, the first r iterative optimal solutions are employed to enhance search capability, and in another stage, only optimal solution is used to accelerate the speed of convergence. And besides, the pheromone value is limited to an interval. We prove that TSIACO not only has the property of linear transformational invariance but also has translational invariance. We also prove that the pheromone trail can limit to the interval (0, 1]. Computational results on the traveling salesman problem show the effectiveness of TSIACO algorithm.     

Bi-criterion optimisation for configuring an assembly supply chain using Pareto ant colony meta-heuristic

An assembly supply chain (SC) is composed of stages that provide the components, assemble both sub-assemblies and final products, and deliver products to the customer. The activities carried out in each stage could be performed by one or more options, thus the decision-maker must select the set of options that minimises the cost of goods sold (CoGS) and the lead time (LT), simultaneously. In this paper, an ant colony-based algorithm is proposed to generate a set of SC configurations using the concept of Pareto optimality. The pheromones are updated using an equation that is a function of the CoGS and LT. The algorithm is tested using a notebook SC problem, widely used in literature. The results show that the ratio between the size of the Pareto Front computed by the proposed algorithm and the size of the one computed by exhaustive enumeration is 90%. Other metrics regarding error ratio and generational distance are provided as well as the CPU time to measure the performance of the proposed algorithm.     

Flexible job-shop scheduling with routing flexibility and separable setup times using ant colony optimisation method

This paper proposes an ant colony optimisation-based software system for solving FMS scheduling in a job-shop environment with routing flexibility, sequence-dependent setup and transportation time. In particular, the optimisation problem for a real environment, including parallel machines and operation lag times, has been approached by means of an effective pheromone trail coding and tailored ant colony operators for improving solution quality. The method used to tune the system parameters is also described. The algorithm has been tested by using standard benchmarks and problems, properly designed for a typical FMS layout. The effectiveness of the proposed system has been verified in comparison with alternative approaches.     

Diversity control in ant colony optimization

Optimization inspired by cooperative food retrieval in ants has been unexpectedly successful and has been known as ant colony optimization (ACO) in recent years. One of the most important factors to improve the performance of the ACO algorithms is the complex trade-off between intensification and diversification. This article investigates the effects of controlling the diversity by adopting a simple mechanism for random selection in ACO. The results of computer experiments have shown that it can generate better solutions stably for the traveling salesmen problem than AS_rank which is known as one of the newest and best ACO algorithms by utilizing two types of diversity.     

Hybrid real-code ant colony optimisation for constrained mechanical design

This paper proposes a hybrid meta-heuristic based on integrating a local search simplex downhill (SDH) method into the search procedure of real-code ant colony optimisation (ACOR). This hybridisation leads to five hybrid algorithms where a Monte Carlo technique, a Latin hypercube sampling technique (LHS) and a translational propagation Latin hypercube design (TPLHD) algorithm are used to generate an initial population. Also, two numerical schemes for selecting an initial simplex are investigated. The original ACOR and its hybrid versions along with a variety of established meta-heuristics are implemented to solve 17 constrained test problems where a fuzzy set theory penalty function technique is used to handle design constraints. The comparative results show that the hybrid algorithms are the top performers. Using the TPLHD technique gives better results than the other sampling techniques. The hybrid optimisers are a powerful design tool for constrained mechanical design problems.     

Countering the negative search bias of ant colony optimization in subset selection problems

In their quest to find a good solution to a given optimization problem, metaheuristic search algorithms intend to explore the search space in a useful and efficient manner. Starting from an initial state or solution(s), they are supposed to evolve towards high-quality solutions. For some types of genetic algorithms (GAs), it has been shown that the population of chromosomes can converge to very bad solutions, even for trivial problems. These so-called deceptive effects have been studied intensively in the field of GAs and several solutions to these problems have been proposed. Recently, similar problems have been noticed for ant colony optimization (ACO) as well. As for GAs, ACO's search can get biased towards low-quality regions in the search space, probably resulting in bad solutions. Some methods have been proposed to investigate the presence and strength of this negative bias in ACO. We present a framework that is capable of eliminating the negative bias in subset selection problems. The basic Ant System algorithm is modified to make it more robust to the presence of negative bias. A profound simulation study indicates that the modified Ant System outperforms the original version in problems that are susceptible to bias. Additionally, the proposed methodology is incorporated in the Max–Min AS and applied to a real-life subset selection problem.     

Modeling and implementation of classification rule discovery by ant colony optimisation for spatial land-use suitability assessment

This paper presents an integrated modeling method for multi-criteria land-use suitability assessment (LSA) using classification rule discovery (CRD) by ant colony optimisation (ACO) in ArcGIS. This new attempt applies artificial intelligent algorithms to intelligentise LSA by discovering suitability classification rules. The methodology is implemented as a tool called ACO-LSA. The tool can generate rules which are straightforward and comprehensible for users with high classification accuracy and simple rule list in solving CRD problems. A case study in the Macintyre Brook Catchment of southern Queensland in Australia is proposed to demonstrate the feasibility of this new modeling technique. The results have addressed the major advantages of this novel approach.     

Accelerating ant colony optimisation for the travelling salesman problem on the GPU

Recent graphics processing units (GPUs) can be used for general purpose parallel computation. Ant colony optimisation (ACO) approaches have been introduced as nature-inspired heuristics to find good solutions of the travelling salesman problem (TSP). In ACO approaches, a number of ants traverse the cities of the TSP to find better solutions of the TSP. The ants randomly select next visiting cities based on the probabilities determined by total amounts of their pheromone spread on routes. The main contribution of this paper is to present sophisticated and efficient implementation of one of the ACO approaches on the GPU. In our implementation, we have considered many programming issues of the GPU architecture including coalesced access of global memory and shared memory bank conflicts. In particular, we present a very efficient method for random selection of next cities by a number of ants. Our new method uses iterative random trial which can find next cities in few computational costs with high probability. This idea can be applied in not only GPU implementation but also CPU implementation. The experimental results on NVIDIA GeForce GTX 580 show that our implementation for 1002 cities runs in 8.71 s, while the CPU implementation runs in 190.05 s. Thus, our GPU implementation attains a speed-up factor of 22.11.     

Ant colony optimisation to identify genetic variant association with type 2 diabetes

Around 1.8 million people in the UK have type 2 diabetes, representing about 90% of all diabetes cases in the UK. Genome wide association studies have recently implicated several new genes that are likely to be associated with this disease. However, common genetic variants so far identified only explain a small proportion of the heritability of type 2 diabetes. The interaction of two or more gene variants, may explain a further element of this heritability but full interaction analyses are currently highly computationally burdensome or infeasible. For this reason this study investigates an ant colony optimisation (ACO) approach for its ability to identify common gene variants associated with type 2 diabetes, including putative epistatic interactions. This study uses a dataset comprising 15,309 common (>5% minor allele frequency) SNPs from chromosome 16, genotyped in 1924 type 2 diabetes cases and 2938 controls. This chromosome contains two previously determined associations, one of which is replicated in additional samples. Although no epistatic interactions have been previously reported on this dataset, we demonstrate that ACO can be used to discover single SNP and plausible epistatic associations from this dataset and is shown to be both accurate and computationally tractable on large, real datasets of SNPs with no expert knowledge included in the algorithm.     

On solving permutation scheduling problems with ant colony optimization

A new approach for solving permutation scheduling problems with ant colony optimization (ACO) is proposed in this paper. The approach assumes that no precedence constraints between the jobs have to be fulfilled. It is tested with an ACO algorithm for the single-machine total weighted deviation problem. In the new approach the ants allocate the places in the schedule not sequentially, as in the standard approach, but in random order. This leads to a better utilization of the pheromone information. It is shown by experiments that adequate combinations between the standard approach which can profit from list scheduling heuristics and the new approach perform particularly well.     

一种改进的蚁群算法在TSP问题中的应用研究

蚁群算法是近几年发展起来的一种新型的拟生态启发式算法,它已经被成功地应用在旅行商(TSP)问题上.由于基本蚁群算法存在过早陷入局部最优解和收敛性较差等缺点,文中对基本蚁群算法在基于蚁群系统的基础上进行了改进,在信息素的更新和解的搜索过程中更多地关注了局部最优解的信息,以使算法尽可能地跳出局部最优,并且改进后的算法对一些关键参数更容易控制.多次实验表明改进的蚁群算法在解决TSP问题上与基本蚁群算法相比有较好的寻优能力和收敛能力.这种算法可以应用在其它组合优化问题上,有一定的工程应用价值.     

An ant colony optimization algorithm to improve software quality prediction models: Case of class stability

Context

Assessing software quality at the early stages of the design and development process is very difficult since most of the software quality characteristics are not directly measurable. Nonetheless, they can be derived from other measurable attributes. For this purpose, software quality prediction models have been extensively used. However, building accurate prediction models is hard due to the lack of data in the domain of software engineering. As a result, the prediction models built on one data set show a significant deterioration of their accuracy when they are used to classify new, unseen data.

Objective

The objective of this paper is to present an approach that optimizes the accuracy of software quality predictive models when used to classify new data.

Method

This paper presents an adaptive approach that takes already built predictive models and adapts them (one at a time) to new data. We use an ant colony optimization algorithm in the adaptation process. The approach is validated on stability of classes in object-oriented software systems and can easily be used for any other software quality characteristic. It can also be easily extended to work with software quality predictive problems involving more than two classification labels.

Results

Results show that our approach out-performs the machine learning algorithm C4.5 as well as random guessing. It also preserves the expressiveness of the models which provide not only the classification label but also guidelines to attain it.

Conclusion

Our approach is an adaptive one that can be seen as taking predictive models that have already been built from common domain data and adapting them to context-specific data. This is suitable for the domain of software quality since the data is very scarce and hence predictive models built from one data set is hard to generalize and reuse on new data.     

An adaptive ant colony optimization algorithm for constructing cognitive diagnosis tests

A critical issue in the applications of cognitive diagnosis models (CDMs) is how to construct a feasible test that achieves the optimal statistical performance for a given purpose. As it is hard to mathematically formulate the statistical performance of a CDM test based on the items used, exact algorithms are inapplicable to the problem. Existing test construction heuristics, however, suffer from either limited applicability or slow convergence. In order to efficiently approximate the optimal CDM test for different construction purposes, this paper proposes a novel test construction method based on ant colony optimization (ACO-TC). This method guides the test construction procedure with pheromone that represents previous construction experience and heuristic information that combines different item discrimination indices. Each test constructed is evaluated through simulation to ensure convergence towards the actual optimum. To further improve the search efficiency, an adaptation strategy is developed, which adjusts the design of heuristic information automatically according to the problem instance and the search stage. The effectiveness and efficiency of the proposed method is validated through a series of experiments with different conditions. Results show that compared with traditional test construction methods of CDMs, the proposed ACO-TC method can find a test with better statistical performance at a faster speed.     

Framework for minimising the impact of regional shocks on global food security using multi-objective ant colony optimisation

A general framework for the identification of optimal strategies for mitigating the impact of regional shocks to the global food production network is introduced. The framework utilises multi-objective ant colony optimisation (ACO) as the optimisation engine and is applicable to production-, demand-, storage- and distribution-focussed mitigation options. A detailed formulation for using trade as the mitigation option is presented and applied to a shock to wheat production in North America for illustrative purposes. Different strategies for improving the performance of the ACO algorithm are also presented and tested. Results indicate that the proposed framework has the potential to identify a range of practical trade mitigation strategies for consideration by decision makers, including trade-offs between the extent to which regional shocks can be mitigated and the degree to which existing trade arrangements have to be modified, as well as the relative importance of various trade agreements and different exporting countries.     

Multi-satellite control resource scheduling based on ant colony optimization

The multi-satellite control resource scheduling problem (MSCRSP) is a kind of large-scale combinatorial optimization problem. As the solution space of the problem is sparse, the optimization process is very complicated. Ant colony optimization as one of heuristic method is wildly used by other researchers to solve many practical problems. An algorithm of multi-satellite control resource scheduling problem based on ant colony optimization (MSCRSP–ACO) is presented in this paper. The main idea of MSCRSP–ACO is that pheromone trail update by two stages to avoid algorithm trapping into local optima. The main procedures of this algorithm contain three processes. Firstly, the data get by satellite control center should be preprocessed according to visible arcs. Secondly, aiming to minimize the working burden as optimization objective, the optimization model of MSCRSP, called complex independent set model (CISM), is developed based on visible arcs and working periods. Ant colony algorithm can be used directly to solve CISM. Lastly, a novel ant colony algorithm, called MSCRSP–ACO, is applied to CISM. From the definition of pheromone and heuristic information to the updating strategy of pheromone is described detailed. The effect of parameters on the algorithm performance is also studied by experimental method. The experiment results demonstrate that the global exploration ability and solution quality of the MSCRSP–ACO is superior to existed algorithms such as genetic algorithm, iterative repair algorithm and max–min ant system.     

Replenishment policy based on modified ant colony optimisation and statistical analysis under the pre-order penetration point

In the supply chain, most businesses in the pre-order penetration point (pre-OPP) operate under the forecast-driven mode, so that the decisions regarding inventory are made in accordance with the forecast and replenishment planning. This paper considers the stochastic dynamic lot-sizing problem of the two-phased transportation cost, service level constraint, and cash flow under a non-deterministic demand. This problem includes a nonlinear integer programming sub-problem. Therefore, this paper proposes an optimisation replenishment policy method based on modified ant colony optimisation (ACO) and response surface methodology. The main differences between the modified ACO and the traditional ACO lie in the modified update of pheromone intensity and the dynamic mutation operator. The experimental result shows that when the demand is normal distribution, the proposed approach, successfully finds the stationary point of minimum response. Besides, in the test of the algorithm solution quality, the modified ACO is better than the traditional ACO in all scenarios.     

An ant colony optimization algorithm for DNA sequencing by hybridization

The reconstruction of DNA sequences from DNA fragments is one of the most challenging problems in computational biology. In recent years the specific problem of DNA sequencing by hybridization has attracted quite a lot of interest in the optimization community. Several metaheuristics such as tabu search and evolutionary algorithms have been applied to this problem. However, the performance of existing metaheuristics is often inferior to the performance of recently proposed constructive heuristics. On the basis of these new heuristics we develop an ant colony optimization algorithm for DNA sequencing by hybridization. An important feature of this algorithm is the implementation in a so-called multi-level framework. The computational results show that our algorithm is currently a state-of-the-art method for the tackled problem.     

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.     

Congestion-aware ant colony based routing algorithms for efficient application execution on Network-on-Chip platform

Networks-on-Chip (NoC) is an interesting option in design of communication infrastructures for embedded systems. It provides a scalable structure and balanced communication between the cores. Parallel applications that take advantage of the NoC architectures, are usually are communication-intensive. Thus, a big deal of data packets is transmitted simultaneously through the network. In order to avoid congestion delays that deteriorate the execution time of the implemented applications, an efficient routing strategy must be thought of carefully. In this paper, the ant colony optimization paradigm is explored to find and optimize routes in a mesh-based NoC. The proposed routing algorithms are simple yet efficient. The routing optimization is driven by the minimization of total latency during packets transmission between the tasks that compose the application. The presented performance evaluation is threefold: first, the impact of well-known synthetic traffic patterns is assessed; second, randomly generated applications are mapped into the NoC infrastructure and some synthetic communication traffics, that follow known patterns, are used to simulate real situations; third, sixteen real-world applications of the E3S and one specific application for digital image processing are mapped and their execution time evaluated. In both cases, the obtained results are compared to those obtained with known general purpose algorithms for deadlock free routing. The comparison avers the effectiveness and superiority of the ant colony inspired routing.     

Savings based ant colony optimization for the capacitated minimum spanning tree problem

The problem of connecting a set of client nodes with known demands to a root node through a minimum cost tree network, subject to capacity constraints on all links is known as the capacitated minimum spanning tree (CMST) problem. As the problem is NP-hard, we propose a hybrid ant colony optimization (ACO) algorithm to tackle it heuristically. The algorithm exploits two important problem characteristics: (i) the CMST problem is closely related to the capacitated vehicle routing problem (CVRP), and (ii) given a clustering of client nodes that satisfies capacity constraints, the solution is to find a MST for each cluster, which can be done exactly in polynomial time. Our ACO exploits these two characteristics of the CMST by a solution construction originally developed for the CVRP. Given the CVRP solution, we then apply an implementation of Prim's algorithm to each cluster to obtain a feasible CMST solution. Results from a comprehensive computational study indicate the efficiency and effectiveness of the proposed approach.