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Current planning approaches for home care services do not generally support the social and human dimension of planning. They focus on optimization criteria that are easily quantifiable, such as the cost. Whereas other criteria such as the quality of the relationship between caregivers and beneficiaries or the satisfaction of the latter are important too as they can highly influence the planning. To address this issue, we investigate in this work the problem of planning optimization for home care service. We propose an extension of the classical ant colony optimization algorithms. Optimization is carried out by several classic criteria such as the cost along with social-based criteria such as the relationship between caregivers and beneficiaries. We also propose a flexible and expressive language to represent the constraints in the form of predicates that can include variables, constants and functions of the problem. This allows each organisation to add its own constraints. [ABSTRACT FROM AUTHOR]

Electric Power Assist Steering (EPAS) system offers a significant potential in enhancing the driving performance of a vehicle where the energy conserving issue is important. In this paper, Ant Colony Optimization (ACO) algorithm is implemented as tuning mechanism for PID controller. The aim of this hybrid controller is to minimize energy consumption of the EPAS system in Electric Vehicle (EV) by minimizing the assist current supplied to the assist motor. The ACO algorithm searching technique is applied to search for the best gain parameters of the PID controller. The fast tuning feature of ACO algorithm is the factor that distinguish this hybrid method as compared to conventional trial and error method PID controller tuning. Simulation results shows the performance and effectiveness of using ACO algorithm for PID tuning. [ABSTRACT FROM PUBLISHER]

Traveling salesman problem (TSP) is a popular routing problem, which is a sub-problem of many application domains such as transportation, network communication, vehicle routing and integrated circuits designs. Among many approaches which have been proposed for TSP so far, evolutionary algorithms effectively applied to solve it, and attempt to avoid trapping in local minima. In this paper, a hybrid approach (named PMACO) is proposed for TSP, which is based on ant colony optimization (ACO) and the mutation operators in genetic algorithm (GA). In this manner, in the each iteration a hybrid mutation scheme is applied to search the neighborhood area of the solution corresponding with the two best ants (the best current ant, and the total best ant found so far). The population is divided to two parts: the first is regenerated according to the pheromone rule in ACO, and the last is generated by applying the proposed hybrid mutation scheme. Also in this paper, the initial population was not considered randomly; it was generated according to nearest neighborhood rule, which force the ants toward better solutions, and leads to reduce the running time. Finally, we compared the performance of PMACO, with ACO and GA separately. For each algorithm, the various parameters and operators were tested, and the bests were chosen to tune that. Experimental results were carried out on benchmarks from the TSPLIB for validation. [ABSTRACT FROM PUBLISHER]

This paper attempts to undertake the study of maximizing the lifetime of Heterogeneous wireless sensor networks (WSNs). In wireless sensor networks, sensor nodes are typically power-constrained with limited lifetime, and thus it is necessary to know how long the network sustains its networking operations. Heterogeneous WSNs consists of different sensor devices with different capabilities. We can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. One of major issue in WSNs is finding maximum number of connected coverage. This paper proposed a Swarm Intelligence, Ant Colony Optimization (ACO) based approach. Ant colony optimization algorithm provides a natural and intrinsic way of exploration of search space of coverage area. Ants communicate with their nest-mates using chemical scents known as pheromones, Based on Pheromone trail between sensor devices the shortest path is found. The methodology is based on finding the maximum number of connected covers that satisfy both sensing coverage and network connectivity. By finding the coverage area and sensing range, the network lifetime maximized and reduces the energy consumption. This approach can be used in both cases of discrete point coverage and area coverage. Local search algorithm used for further enhancement. Extensive Java Agent Framework(JADE) multi agent simulator result clearly show that the proposed approach provides more approximate, effective and efficient way for maximizing the lifetime of heterogeneous WSNs. [ABSTRACT FROM PUBLISHER]

The average travel speed and waiting time of cars are factors which determine the time taken to get to a destination. These factors are influenced by the traffic situation at various sections of the road. In this paper a novel Distributed Intelligent Traffic System (DITS) which is based on Ant Colony Optimization has been proposed. In the proposed model the average speed of cars at various sections of the road is used to determine the traffic situation in those sections of the road. Applying Ant Colony Optimization, cars arriving at a junction avoid the congested path and use the path with least traffic to get to their destination. Simulation results of the model by NetLogo have shown that the DITS model demonstrate better performance when compared with a scenario where the cars select their travel path randomly. [ABSTRACT FROM PUBLISHER]

With the advent of cloud computing and the need for increasing amount of computing power, cloud infrastructure providers are now facilitating the deployment of large-scale data centers. In order to efficiently manage such environments three important properties have to be fulfilled by their resource management frameworks: (1) scalability, (2) autonomy (i.e. self-organization and healing), (3) energy-awareness. However, existing open-source cloud management stacks (e.g. Eucalyptus, Nimbus, Open Nebula, Open Stack) have a high degree of centralization and limited power management support. In this context, this PhD thesis focuses on more scalable, autonomic, and energy-aware resource management frameworks for large-scale cloud infrastructures. Particularly, a novel virtual machine (VM) management system based on a self-organizing hierarchical architecture called Snooze is proposed. In order to conserve energy, Snooze automatically transitions idle servers into a low-power mode (e.g. suspend). To favor idle times the system integrates a nature-inspired VM consolidation algorithm based on the Ant Colony Optimization (ACO). [ABSTRACT FROM PUBLISHER]

A modified ant colony optimization algorithm implementing a new definition of pheromone and a new cooperation mechanism between ants is presented in this paper. The study aims at improving the suitability and computational efficiency of the ant colony optimization algorithm in dynamic topology optimization problems. The natural frequencies of the structure must be maximized yet satisfying a constraint on the final volume. Optimization results obtained in three test cases indicate that modified ACO is more efficient and robust than ACO in solving dynamic topology optimization problems. [ABSTRACT FROM PUBLISHER]

Due to the limitation of DTN resource and intermittent connection, it's challenging to design a mechanism which achieves high message delivery successful ratio and control congestion to satisfy the demand. In this paper, a novel DTN congestion control routing mechanism, Ant Colony Optimization(ACO) for DTN congestion control algorithm(ADC) is proposed. In the message delivery, this mechanism can actively leverage pheromone and heuristic in ACO to evaluate the selection of transfer node. Pheromone can guarantee the message successful delivery ratio and heuristic prevent network congestion in real time. The evaluation of this congestion control routing algorithm shows the evident improvement of some parameters in network circumstance. [ABSTRACT FROM PUBLISHER]

To tackle problems emerging with rapid growth of information networks in scale and complexity, bio-inspired self organization is considered one of promising design principles of a new generation network which is scalable, robust, adaptive, and sustainable. However, self-organizing systems would fall into a local optimum or never converge under some environmental conditions. Controlled or guided self-organization is a novel concept attracting many researchers in these years, where loose and moderate control is imposed on a self-organizing system to push it toward a desired state. In this paper, we take Ant Net, an ant-based routing protocol, as an example and consider a mechanism to accelerate convergence by limiting the search space. The proposed mechanism is compared with Ant Net and HOPNET from viewpoints of the convergence time, path length, and control overhead. Simulation results show that our proposal can accelerate convergence of ant-based routing to a shorter path than Ant Net and with lower control overhead than HOPNET. [ABSTRACT FROM PUBLISHER]

Classification in imbalanced domains is a challenging task, since most of its real domain applications present skewed distributions of data. However, there are still some open issues in this kind of problem. This paper presents a multi-objective grammar-based ant programming algorithm for imbalanced classification, capable of addressing this task from both the binary and multiclass sides, unlike most of the solutions presented so far. We carry out two experimental studies comparing our algorithm against binary and multiclass solutions, demonstrating that it achieves an excellent performance for both binary and multiclass imbalanced data sets. [ABSTRACT FROM PUBLISHER]

The existing mobile service discovery approaches do not completely address the issues of service selection and the robustness faced to mobility. The infrastructure of mobile service must be QoS-aware plus context-aware (i.e.) aware of the user's required-QoS and the QoS offered by the other networks in user's context. In this paper, we propose a cluster based QoS-aware service discovery architecture using swarm intelligence. Swarm intelligence is used to establish the intra and inter cluster shortest path routing. By simulation results, we show that the proposed architecture can attain a good success rate with reduced delay and energy consumption, since it satisfies the QoS constraints. [ABSTRACT FROM PUBLISHER]

In this paper, a new path planning approach combining framed-quadtree representation with hybrid-simulated annealing (SA) and ant colony optimization (ACO) algorithm called SAACO is presented to improve the efficiency of path planning. The utilization of framed-quadtree representation is for improving the decomposed efficiency of the environment and maintaining the representation capability of maps. Simulated annealing and ant colony optimization were applied for robot path planning problem respectively and there have been plenty of accomplishments in recent year. Lots forms of SA depend on random starting points and how to efficiently offer better initial estimates of solution sets automatically is still a research hot point. We use ACO to supply a good initial solution for SA runs. According to the theoretical analysis and results obtained from simulation experiment, the presented SAACO algorithm can solve successfully the mobile robot path planning problem, which leads robot to seek the specific destination in the free-collision path and increases the speed of the robot navigation. Some excellent properties of this method have also been proved that is robustness, self-adaptation. [ABSTRACT FROM PUBLISHER]

A mobile ad-hoc network (MANET) is a collection of mobile nodes which communicate over radio. These kind of networks are very flexible, thus they do not require any existing infrastructure or central administration. Therefore, mobile ad-hoc networks are suitable for temporary communication links. As the nodes are mobile, it is very difficult to find the path between two end points. This paper presents a survey of routing algorithms for finding path between nodes in mobile ad hoc network. . In recent years,several routing algorithms have been proposed for mobile ad hoc networks and prominent among them are ant-colony, bee-colony, termite, distance vector routing and novel. The functions and features involved in implementation of different routing algorithms for MANET are discussed in detail giving comparative study of various routing algorithms. [ABSTRACT FROM PUBLISHER]

Published studies have focused on the application of one bio-inspired or evolutionary computational method to the functions of a single protocol layer in a wireless ad hoc sensor network (WSN). In a novel departure from previous work, the cross-layer approach to WSN protocol design suggests applying a bio-inspired evolutionary computational method to the functions of each protocol layer to improve the intrusion detection identification (IDID) performance of a WSN cross-layer design beyond that of a single method assigned to only one layer's functions. A cross-layer design, embedding genetic algorithms, anti-phase synchronization, ant colony optimization, and a trust model based on quantized data reputation at the physical (PHY), medium access control (MAC), network, and application layers, respectively, is constructed. Simulation results demonstrate synergies among the bio-inspired methods of the proposed baseline design improve overall IDID performance of networks over that of a single computational method. [ABSTRACT FROM PUBLISHER]

In this paper, the multi-mode resource-constrained project scheduling problem (MRCPSP) is considered. The MRCPSP is extremely complex and is known to be NP-hard problem. In order to obtain satisfactory sub optimal solutions within a reasonable amount of computational time, various met heuristic techniques have been developed to solve MRCPSP. For a metaheuristic algorithm, too much emphasis on the exploitation of the search experience gathered so far can frequently converge to local optima. On the contrary, too much emphasis on the exploration of unvisited or relatively unexplored search space regions can cause an unstable state. On the basis for balancing between exploitation and exploration, we proposed ACODT-MRCPSP approach which introduces the dynamic tournament strategy to solve MRCPSP. Experimental results indicate that ACODT-MRCPSP approach is a significant improvement compared with the previous attempts at solving the MRCPSP. [ABSTRACT FROM PUBLISHER]

Electric utilities are the companies responsible for ensuring energy supply meets their customers' requirement. While ensuring the energy is generated in the right amount, they have to guarantee that the energy is generated within feasible cost. Economic Load Dispatch (ELD) problem involves the scheduling of generating unit outputs that can satisfy load demand at minimum operating cost. Several approaches have been applied to yield the best solution for the problem, such as Genetic Algorithm, Bees Algorithm and Neural Network Algorithm. This paper presents Differential Evolution Ant Colony Optimization (DEACO) to optimize Economic Load Dispatch in power system. Implementation of the IEEE Reliability Test System (RTS) demonstrated that this technique is feasible to crack the economic problem. Comparative studies with respect to ACO and the traditional ELD techniques designate that the proposed DEACO outperformed these two techniques. [ABSTRACT FROM PUBLISHER]

A distinctive optimization technique known as Ant Colony Optimization (ACO) has gained huge popularity in these recent years. This algorithm has turned into a candidate approach to many optimization problems. Unfortunately, this attractive algorithm suffers several drawbacks, including stagnation and slow convergence toward optimal solution. Thus, a new algorithm, termed as Differential Evolution Ant Colony Optimization (DEACO) has been modeled to compensate the drawbacks. The algorithm was utilized to solve the economic load dispatch problem in order to verify its performance. In this study, several DEACO parameters, including the number of ants and nodes were manipulated to investigate the behavior of the brand-new algorithm. Comparative studies between DEACO and conventional ACO suggested that the new algorithm had successfully overcome the weaknesses of classical ACO. [ABSTRACT FROM PUBLISHER]

Multi-objective evacuation routing optimization problem is defined to find out optimal evacuation routes for a group of evacuees according to multiple evacuation objectives. For improving the evacuation efficiency, we abstracted the evacuation zone as a positive-point-charge-potential-field-like model (PPCPF-like model), and we proposed PPCPF-ACO algorithm to solve this problem based on the proposed model. In PPCPF-ACO algorithm, we use non-dominated sorting based roulette wheel routing method (NSRWR) to further improve evacuation efficiency. In Wuhan Sports Center case, we compared PPCPF-ACO with HMERP-ACO (hierarchical multi-objective evacuation routing problem - ant colony optimization) and traditional ACO according to three evacuation objectives, namely, total evacuation time, total evacuation route length and cumulative congestion degree. The experimental results show that PPCPF-ACO has a better performance than HMERP-ACO algorithm and traditional ACO algorithm while solving multi-objective evacuation routing optimization problem. [ABSTRACT FROM PUBLISHER]

Filters are important building blocks in signal processing circuits. Some researcher had successfully utilized genetic algorithm (GA) in design of filters. Nevertheless, filters obtained by GA are always complex and require lengthy computations. Ant colony optimization (ACO) is a novel searching technique used in optimization problems. In this paper, an ACO approach for optimization of analog filters is presented. In a design example, the order of a lowpass filter and the parameters of its components have been optimized in a discrete search space. AC analysis of the optimized filter has been conducted, and the results have been compared with a filter obtained by GA. The results show that filters obtained by ACO have simpler structures and better performance. [ABSTRACT FROM PUBLISHER]

The advent of Grid environments made feasible the solution of computational intensive problems in a reliable and cost-effective way. As scheduling carry out more complex and mission-critical applications, Quality of Service (QoS) analysis serves to ensure that each application meets user requirements. In that frame, author presents a novel Ant colony algorithm with a quick convergence of ant to move from source to destination which allows the mapping of job processes to Grid provided services assuring at the same time end-to-end provision of QoS (cost, makespan and reliability) based on user-defined parameters and preferences. Paper also demonstrate the operation of the implemented algorithm using three different heuristics and evaluate its effectiveness using a Grid scenario. [ABSTRACT FROM PUBLISHER]

There are a mass of researches on the issue of scheduling in cloud computing, most of them, however, are bout workflow and job scheduling. There are fewer researches on service flow scheduling. Here we propose a model of service flow scheduling with various quality of service (QoS) requirements in cloud computing firstly, then we adopt the use of an ant colony optimization (ACO) algorithm to optimize service flow scheduling. In our model, we use default rate to describe the radio of cloud service provider breaking service level agreement (SLA), and also introduce an SLA monitoring module to monitor the running state of cloud services. [ABSTRACT FROM PUBLISHER]

To solve the well known traveling salesman problem(TSP) a combined approach of Ant Colony Optimization (ACO) and Genetic Algorithm (GA) are considered over here primarily. A number of solutions are given by the researchers in the past few decades even using a hybrid approach of ACO and GA to achieve the optimization. But our work gives the optimal solution for the said problem by using the modular hybrid approach of ACO and GA in addition to some heuristic approach. We have incorporated GA in ACO module, hence each iteration calls the GA within ACO module results in a higher amount of pheromone deposited in the optimal path in the global pheromone update. Hence the convergence is quicker and solution is optimal. [ABSTRACT FROM AUTHOR]

To deal with people assign to job optimization problem in getting into local minima, people assign to job problem (PATJP) model and ant colony optimization (ACO) are developed for people assign to job optimization problem. In PATJP model, one people can assign to more than one job, and one job can be done by more than one people. People assign to job optimization is searched by ACO, optimization processes tend to get the global solution. Illustration results show that PATJP model and ACO algorithm are effective and offer a way to PATJP. [ABSTRACT FROM PUBLISHER]