Showing papers on "Ant colony published in 2017"

A novel collaborative optimization algorithm in solving complex optimization problems

Abstract: To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid)

Abstract: Ant Colony Optimization (ACO) and Bee Colony Optimization (BCO) are famous meta-heuristic search algorithms used in solving numerous combinatorial optimization problems. Feature Selection (FS) helps to speed up the process of classification by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem because selecting the appropriate feature subset is very important. This paper proposes a novel Swarm based hybrid algorithm AC-ABC Hybrid, which combines the characteristics of Ant Colony and Artificial Bee Colony (ABC) algorithms to optimize feature selection. By hybridizing, we try to eliminate stagnation behavior of the ants and time consuming global search for initial solutions by the employed bees. In the proposed algorithm, Ants use exploitation by the Bees to determine the best Ant and best feature subset; Bees adapt the feature subsets generated by the Ants as their food sources. Thirteen UCI (University of California, Irvine) benchmark datasets have been used for the evaluation of the proposed algorithm. Experimental results show the promising behavior of the proposed method in increasing the classification accuracies and optimal selection of features.

Cooperation and profit allocation in two-echelon logistics joint distribution network optimization

Abstract: A two-echelon logistics joint distribution network optimization model is developed.This model is to minimize the total cost of TELJDN.A hybrid algorithm combining ACO and GA operations is proposed.A cooperative mechanism strategy for sequential coalitions is studied in TELJDN.An empirical study demonstrates the applicability of the proposed approach. Collaborative two-echelon logistics joint distribution network can be organized through a negotiation process via logistics service providers or participants existing in the logistics system, which can effectively reduce the crisscross transportation phenomenon and improve the efficiency of the urban freight transportation system. This study establishes a linear optimization model to minimize the total cost of two-echelon logistics joint distribution network. An improved ant colony optimization algorithm integrated with genetic algorithm is presented to serve customer clustering units and resolve the model formulation by assigning logistics facilities. A two-dimensional colony encoding method is adopted to generate the initial ant colonies. Improved ant colony optimization combines the merits of ant colony optimization algorithm and genetic algorithm with both global and local search capabilities. Finally, an improved Shapley value model based on cooperative game theory and a cooperative mechanism strategy are presented to obtain the optimal profit allocation scheme and sequential coalitions respectively in two-echelon logistics joint distribution network. An empirical study in Guiyang City, China, reveals that the improved ant colony optimization algorithm is superior to the other three methods in terms of the total cost. The improved Shapley value model and monotonic path selection strategy are applied to calculate the best sequential coalition selection strategy. The proposed cooperation and profit allocation approaches provide an effective paradigm for logistics companies to share benefit, achieve winwin situations through the horizontal cooperation, and improve the negotiation power for logistics network optimization.

Design and development of a hybrid ant colony-variable neighbourhood search algorithm for a multi-depot green vehicle routing problem

Abstract: The traditional distribution planning problem in a supply chain has often been studied mainly with a focus on economic benefits. The growing concern about the effects of anthropogenic pollutions has forced researchers and supply chain practitioners to address the socio-environmental concerns. This research study focuses on incorporating the environmental impact on route design problem. In this work, the aim is to integrate both the objectives, namely economic cost and emission cost reduction for a capacitated multi-depot green vehicle routing problem. The proposed models are a significant contribution to the field of research in green vehicle routing problem at the operational level. The formulated integer linear programming model is solved for a set of small scale instances using LINGO solver. A computationally efficient Ant Colony Optimization (ACO) based meta-heuristic is developed for solving both small scale and large scale problem instances in reasonable amount of time. For solving large scale instances, the performance of the proposed ACO based meta-heuristic is improved by integrating it with a variable neighbourhood search.

The effects of ant nests on soil fertility and plant performance: a meta-analysis

Abstract: Ants are recognized as one of the major sources of soil disturbance world-wide. However, this view is largely based on isolated studies and qualitative reviews. Here, for the first time, we quantitatively determined whether ant nests affect soil fertility and plant performance, and identified the possible sources of variation of these effects. Using Bayesian mixed-models meta-analysis, we tested the hypotheses that ant effects on soil fertility and plant performance depend on the substrate sampled, ant feeding type, latitude, habitat and the plant response variable measured. Ant nests showed higher nutrient and cation content than adjacent non-nest soil samples, but similar pH. Nutrient content was higher in ant refuse materials than in nest soils. The fertilizer effect of ant nests was also higher in dry habitats than in grasslands or savannas. Cation content was higher in nests of plant-feeding ants than in nests of omnivorous species, and lower in nests from agro-ecosystems than in nests from any other habitat. Plants showed higher green/root biomass and fitness on ant nests soils than in adjacent, non-nest sites; but plant density and diversity were unaffected by the presence of ant nests. Root growth was particularly higher in refuse materials than in ant nest soils, in leaf-cutting ant nests and in deserts habitats. Our results confirm the major role of ant nests in influencing soil fertility and vegetation patterns and provide information about the factors that mediate these effects. First, ant nests improve soil fertility mainly through the accumulation of refuse materials. Thus, different refuse dump locations (external or in underground nest chambers) could benefit different vegetation life-forms. Second, ant nests could increase plant diversity at larger spatial scales only if the identity of favoured plants changes along environmental gradients (i.e. enhancing β-diversity). Third, ant species that feed on plants play a relevant role fertilizing soils, which may balance their known influence as primary consumers. Fourth, the effects of ant nests as fertility islands are larger in arid lands, possibly because fertility is intrinsically lower in these habitats. Overall, this study provide novel and quantitative evidence confirming that ant nests are key soil modifiers, emphasizing their role as ecological engineers.

Solving NP-Hard Problems with Physarum-Based Ant Colony System

Abstract: NP-hard problems exist in many real world applications. Ant colony optimization ACO algorithms can provide approximate solutions for those NP-hard problems, but the performance of ACO algorithms is significantly reduced due to premature convergence and weak robustness, etc. With these observations in mind, this paper proposes a Physarum-based pheromone matrix optimization strategy in ant colony system ACS for solving NP-hard problems such as traveling salesman problem TSP and 0/1 knapsack problem 0/1 KP. In the Physarum-inspired mathematical model, one of the unique characteristics is that critical tubes can be reserved in the process of network evolution. The optimized updating strategy employs the unique feature and accelerates the positive feedback process in ACS, which contributes to the quick convergence of the optimal solution. Some experiments were conducted using both benchmark and real datasets. The experimental results show that the optimized ACS outperforms other meta-heuristic algorithms in accuracy and robustness for solving TSPs. Meanwhile, the convergence rate and robustness for solving 0/1 KPs are better than those of classical ACS.

Multi-objective dynamic virtual machine consolidation in the cloud using ant colony system

Abstract: In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centers. The proposed algorithm builds VM migration plans, which are then used to minimize over-provisioning of physical machines (PMs) by consolidating VMs on under-utilized PMs. It optimizes two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximize the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimize the number of VM migrations. The proposed algorithm is empirically evaluated in a series of experiments. The experimental results show that the proposed algorithm provides an efficient solution for VM consolidation in cloud data centers. Moreover, it outperforms two existing ant colony optimization based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

Ant Colony System Sanitization Approach to Hiding Sensitive Itemsets

Abstract: In recent years, privacy-preserving data mining (PPDM) has received a lot of attention in the field of data mining research. While some sensitive information in databases cannot be revealed, PPDM can discover additional important knowledge and still hide critical information. There are different ways to approach this exhibited in previous research, which applied addition and deletion operations to adjust an original database in order to hide sensitive information. However, it is an NP-hard problem to find an appropriate set of transactions/itemsets for hiding sensitive information. In the past, evolutionary algorithms were developed to hide sensitive itemsets by building an appropriate database. Genetic-based algorithms and a particle swarm optimization-based algorithm, proposed in previous works, not only hide sensitive itemsets but also minimize the side effects of sanitization processes. In this paper, an ant colony system (ACS)-based algorithm called ACS2DT is proposed to decrease side effects and enhance the performance of the sanitization process. Each ant in the population will build a tour for each iteration and each tour indicates the deleted transactions in the original database. The proposed algorithm introduces a useful heuristic function to conduct each ant to select a suitable edge (transaction) for the current situation and also designs several termination conditions to stop the sanitization processes. The proposed heuristic function applies the pre-large concept to monitor side effects and calculates the degree of hiding information to adjust the selecting policy for deleted transactions. The experimental results show that the proposed ACS2DT algorithm performs better than the Greedy algorithm and other two evolutionary algorithms in terms of runtime, fail to be hidden, not to be hidden, not to be generated and database similarity on both real-world and synthetic data sets.

Analysis, design and simulation of Internet of Things routing algorithm based on ant colony optimization

Abstract: Summary In this paper, a routing algorithm to optimize the selection of the best path for the transmitted data within the Internet of Things (IoT) system is proposed. The algorithm controls the use of ant colony ideas in the IoT system to obtain the best routing benefit. It divides the IoT environment into categorized areas depending on network types. Then, it applies the most suitable ant colony algorithm to the concerned network within each area. Furthermore, the algorithm considers routing problem in intersected areas that may arise in case of IoT system. Finally, Network Simulator 2 is used to evaluate the proposed algorithm performance. Simulation results demonstrate the effectiveness of the proposed routing algorithm in terms of end-to-end delay, packet loss ratio, bandwidth consumption, throughput, overhead of control bits, and energy consumption ratio. Copyright © 2016 John Wiley & Sons, Ltd.

Ants regulate colony spatial organization using multiple chemical road-signs.

Abstract: Communication provides the basis for social life. In ant colonies, the prevalence of local, often chemically mediated, interactions introduces strong links between communication networks and the spatial distribution of ants. It is, however, unknown how ants identify and maintain nest chambers with distinct functions. Here, we combine individual tracking, chemical analysis and machine learning to decipher the chemical signatures present on multiple nest surfaces. We present evidence for several distinct chemical ‘road-signs’ that guide the ants’ movements within the dark nest. These chemical signatures can be used to classify nest chambers with different functional roles. Using behavioural manipulations, we demonstrate that at least three of these chemical signatures are functionally meaningful and allow ants from different task groups to identify their specific nest destinations, thus facilitating colony coordination and stabilization. The use of multiple chemicals that assist spatiotemporal guidance, segregation and pattern formation is abundant in multi-cellular organisms. Here, we provide a rare example for the use of these principles in the ant colony. While the organization of ants within their nest is key for colony function, it remains unknown how ants navigate this dark subterranean environment. Here, Heymanet al. use a series of behavioral tests, chemical analyses, and machine learning to identify chemical landmarks that ants use to distinguish between nest areas.

An ant colony approach to operation sequencing optimization in process planning

Abstract: Computer-aided process planning is an important component for linking design and manufacturing in computer-aided design/computer-aided process planning/computer-aided manufacturing integrated manuf...

Survey of swarm intelligence optimization algorithms

Abstract: Unmanned aerial vehicles (UAVs) swarm technology has been a hot topic. The concept of swarm comes from nature, such as the cooperation of bee colony or ant colony, so the research of swarm intelligence (SI) optimization algorithm inspired by swarm activities in nature is beneficial to the development of swarm UAVs technology. The characteristics and principles of eleven SI algorithms are described and analyzed in the paper. The article also analyzed how to combine SI and multi-UAV task assignments. Furthermore, the prospects for development of the SI are discussed.

Greedy-Ant: Ant Colony System-Inspired Workflow Scheduling for Heterogeneous Computing

Abstract: The last decades have seen a considerable progress on workflow scheduling in heterogeneous computing environments. However, existing methods still need to be improved on the performance in the makespan-based metrics. This paper proposes a novel workflow scheduling algorithm named Greedy-Ant to minimize total execution time of an application in heterogeneous environments. First, the ant colony system is applied to scheduling from a new standpoint by guiding ants to explore task priorities and simultaneously assign tasks to machines. Second, forward/backward dependence is defined to indicate the global significance of each node, based on which, a new heuristic factor is proposed to help ants search for task sequences. Finally, a greedy machine allocating strategy is presented. Experimental results demonstrate that Greedy-Ant outperforms the state of the art up to 18% in the metric of speedup.

The Time-Dependent Orienteering Problem with Time Windows: A Fast Ant Colony System

Abstract: This paper proposes a fast ant colony system based solution method to solve realistic instances of the time-dependent orienteering problem with time windows within a few seconds of computation time. Orienteering problems occur in logistic situations where an optimal combination of locations needs to be selected and the routing between these selected locations needs to be optimized. For the time-dependent problem, the travel time between two locations depends on the departure time at the first location. The main contribution of this paper is the design of a fast and effective algorithm for this problem. Numerous experiments on realistic benchmark instances with varying size confirm the state-of-the-art performance and practical relevance of the algorithm.

Fire disturbance disrupts an acacia ant-plant mutualism in favor of a subordinate ant species.

Abstract: Although disturbance theory has been recognized as a useful framework in examining the stability of ant-plant mutualisms, very few studies have examined the effects of fire disturbance on these mutualisms. In myrmecophyte-dominated savannas, fire and herbivory are key drivers that could influence ant-plant mutualisms by causing complete colony mortality and/or decreasing colony size, which potentially could alter dominance hierarchies if subordinate species are more fire resilient. We used a large-scale, replicated fire experiment to examine long-term effects of fire on acacia-ant community composition. To determine if fire shifted ant occupancy from a competitive dominant to a subordinate ant species, we surveyed the acacia-ant community in 6-7 yr old burn sites and examined how the spatial scale of these burns influenced ant community responses. We then used two short-term fire experiments to explore possible mechanisms for the shifts in community patterns observed. Because survival of ant colonies is largely dependent on their ability to detect and escape an approaching fire, we first tested the evacuation response of all four ant species when exposed to smoke (fire signal). Then to better understand how fire and its interaction with large mammal herbivory affect the density of ants per tree, we quantified ant worker density in small prescribed burns within herbivore exclusion plots. We found clear evidence suggesting that fire disturbance favored the subordinate ant Crematogaster nigriceps more than the dominant and strong mutualist ant C. mimosae, whereby C. nigriceps (1) was the only species to occupy a greater proportion of trees in 6-7 yr old burn sites compared to unburned sites, (2) had higher burn/unburn tree ratios with increasing burn size, and (3) evacuated significantly faster than C. mimosae in the presence of smoke. Fire and herbivory had opposite effects on ant density per meter of branch for both C. nigriceps and C. mimosae, with fire decreasing ant densities per meter of branch and the presence of large herbivores increasing ant density. Taken together, these experiments suggest that major ecosystem disturbances like fire can disrupt mutualistic associations and maintain diversity in partner quality and identity despite a clear dominance hierarchy.

On the efficiency of artificial neural networks for plastic analysis of planar frames in comparison with genetic algorithms and ant colony systems

Abstract: The investigation of plastic behavior and determining the collapse load factors are the important ingredients of every kinematical method that is employed for plastic analysis and design of frames. The determination of collapse load factors depends on many effective parameters such as the length of bays, height of stories, types of loads and plastic moments of individual members. As the number of bays and stories increases, the parameters that have to be considered make the analysis a complex and tedious task. In such a situation, the role of algorithms that can help to compute an approximate collapse load factor in a reasonable time span becomes more and more crucial. Due to their interesting properties, heuristic algorithms are good candidates for this purpose. They have found many applications in computing the collapse load factors of low-rise frames. In this work, artificial neural networks, genetic algorithms and ant colony systems are used to obtain the collapse load factors of two-dimensional frames. The latter two algorithms have already been employed in the analysis of frames, and hence, they provide a good basis for comparing the results of a newly developed algorithm. The structure of genetic algorithm, in the form presented here, is the same as previous works; however, some minor amendments have been applied to ant colony systems. The performance of each algorithm is studied through numerical examples. The focus is mainly on the behavior of artificial neural networks in the determination of collapse load factors of two-dimensional frames compared with other two algorithms. The investigation of results shows that a careful selection of the structure of artificial neural networks can lead to an efficient algorithm that predicts the load factors with higher accuracy. The structure should be selected with the aim to reduce the error of the network for a given frame. Such an algorithm is especially useful in designing and analyzing frames whose geometry is known a priori.

A Novel Heuristic Communication Heterogeneous Dual Population Ant Colony Optimization Algorithm

Abstract: To balance the convergence speed and the solution’s diversity in the large-scale travel salesman problem (TSP), this paper proposes a new heuristic communication heterogeneous dual population ant colony optimization (HHACO). First, the main characteristics of HHACO are the heuristic communication and the two heterogeneous ant colonies. Heuristic communication, an indirect communication strategy, helps improve the deviation of solution. Heterogeneous ant colonies are beneficial to balance the convergence speed and the diversity of solution, in which one ant colony is in charge of solution’s diversity and the another one in charge of convergence speed inspiring from nature evolution with self-adaptive ability. Besides, this paper takes advantage of orthogonal test to discuss the parameters in HHACO algorithm and a better parameters’ set is obtained. Then, HHACO algorithm is applied to solve TSP, and meanwhile characteristics of different ant colonies in HHACO are discussed. Finally, HHACO is compared with the other dual colonies algorithms and several classic ant colony optimization algorithms, and results suggest that the HHACO has a better performance in the large-scale problem.

EACO and FABC to multi-path data transmission in wireless sensor networks

Abstract: Owing to the extensive growth of wireless technology for sending and collecting a variety of information for the different applications, routing is a major challenge to find the optimal path for the data transmission In this study, the authors have developed a new algorithm called, exponential ant colony optimisation (EACO) to route discovery problem in wireless sensor network after finding the cluster heads (CHs) using fractional artificial bee colony (FABC) algorithm In the first step, CHs are found out using the FABC algorithm with fitness function considering the distance, energy and delay In the second phase, ACO algorithm is modified with exponential smoothing model for multi-path route discovery This new algorithm called, EACO found the optimal routes among CHs to transmit a data from any source node to base station with multiple objectives including energy, distance, intra-cluster delay and intercluster delay These objectives are effectively formulated as new fitness function to find the optimal route path From the experimentation, the outcome showed that the cumulative energy kept after 2000 round of experiments is 02039 for the proposed algorithm but the existing approach (threshold + ACO) kept only 00338

The generalized traveling salesman problem solved with ant algorithms

Abstract: A well known $$\mathcal{NP}$$ -hard problem called the generalized traveling salesman problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph are partitioned into clusters. The objective is to find a minimum cost tour passing through exactly one node from each cluster. An exact exponential time algorithm and an effective meta-heuristic algorithm for the problem are presented. The meta-heuristic proposed is a modified Ant Colony System (ACS) algorithm called reinforcing Ant Colony System which introduces new correction rules in the ACS algorithm. Computational results are reported for many standard test problems. The proposed algorithm is competitive with the other already proposed heuristics for the GTSP in both solution quality and computational time.

An ant colony algorithm based on cross-layer design for routing and wavelength assignment in optical satellite networks

Abstract: This paper introduces an ant colony routing and wavelength assignment algorithm based on cross-layer design (CL-ACRWA), which can overcome the adverse effects of Doppler wavelength shift on data transmission in optical satellite networks. Firstly, a cross-layer optimization model is built, which considers the Doppler wavelength shift, the transmission delay as well as wavelength-continuity constraint. Then an ant colony algorithm is utilized to solve the cross-layer optimization model, resulting in finding an optimal light path satisfying the above constraints for every connection request. The performance of CL-ACRWA is measured by the communication success probability, the convergence property and the transmission delay. Simulation results show that CL-ACRWA performs well in communication success probability and has good global search ability as well as fast convergence speed. Meanwhile, the transmission delay can meet the basic requirement of real-time transmission of business.