Objective Ant Colony Optimization Research Articles

Power and thermal-aware virtual machine scheduling optimization in cloud data center

Virtual machine (VM) consolidation technology is a commonly used energy-saving method in data centers. Most of the existing methods are committed to consolidating VMs to a small number of servers to improve the utilization of server resources and reduce the total energy consumption while preventing hotspots. However, energy and thermal-aware VM scheduling is a multi-objective optimization problem. Most of the existing related work cannot provide adequate means to adjust the impact of energy consumption and temperature on VM scheduling. Hence, this paper proposes a power and thermal-aware VM dynamic scheduling scheme (PTDS) for cloud data centers. The proposed PTDS dynamically adjusts the VM consolidation scheme by real-time detecting the server temperature and resource utilization rate under the premise of considering the thermal cycle effect of the data center computer room. Power and Thermal Objective Ant Colony Optimization (PTOACO) is proposed in the VM placement. PTOACO improves the defect that the ant colony algorithm easily falls into local optimization and adds control parameters to adjust the bias between sub-objectives. We performed extensive experiments by using real PlanetLab and random workloads. The performance results were compared with several advanced schemes regarding total energy consumption, hotspots, SLA violation rate, etc. The experimental results demonstrate that PTDS reduces energy consumption by 26.69% on average compared with other advanced schemes and ensures a meager SLA violation rate while avoiding hotspots.

A decomposition-based many-objective ant colony optimization algorithm with adaptive reference points

The discrete many-objective problem (MaOP) is challenging in practice. Improving the convergence speed and making the nondominated solutions close to the Pareto front (PF) are vital issues in the optimization of the discrete MaOP. This pper proposed a modified decomposition-based many objective ant colony optimization (ACO) algorithm and employs an adaptive reference point mechanism that chooses the ideal point or nadir point as the reference point according to the distribution of the candidate solutions. This mechanism is utilized to improve the selection operator, accelerate the convergence speed and enhance the optimization ability. A comparative experiment is conducted on traveling salesman problems (TSPs) constrained by two, five, and ten objectives, which are built using test cases from the TSPLIB. The experimental results indicate that the inverted generational distance (IGD) indicator of the nondominated solution of the proposed algorithm has a rapid convergence speed and that the proposed algorithm achieves competitive performance regarding its optimization quality.

A green multi-objective integrated scheduling of production and distribution with heterogeneous fleet vehicle routing and time windows

Scheduling the supply chain in an integrated manner is a quite fundamental subject in supply chain management. To consider due-dates with production and distribution times at the same time could lead to reduced costs and, thereby, increased profitability. Integrating the problems dealing with due-date assignment, production/distribution times, and routing, while incorporating environmental considerations, could not only increase the cohesion among different decision-making levels and reduce costs in the long run, it would also contribute to the improvement of environmental conditions and benefit the world population. In this study, the integrated supply chain scheduling problem features assignment of due dates, batch delivery, assignment to multiple heterogeneous vehicles based on their capacity, and delivery of customer orders in time-windows. The objective is to minimize distribution cost, fixed and variable fuel costs, the carbon emitted by the vehicles, total delivery tardiness, and customer dissatisfaction. In this model, the customers are categorized into five clusters based on the length of their association, how recently they began interacting, how often they interact, and the monetary value of their interaction with the organization. The members of these clusters are called core customers, potential customers, new customers, lost customers, and resource-consumption customers. A mixed integer non-linear programming model is introduced for this problem which is solved using three multi-objective metaheuristic algorithms: Multi-Objective Particle Swarm Optimization, Non-dominated Sorting Genetic Algorithm II, and Multi Objective Ant Colony Optimization. A number of performance criteria and statistical tests are used to evaluate the algorithms.

Enhanced resource allocation in mobile edge computing using reinforcement learning based MOACO algorithm for IIOT

The Mobile networks deploy and offers a multiaspective approach for various resource allocation paradigms and the service based options in the computing segments with its implication in the Industrial Internet of Things (IIOT) and the virtual reality. The Mobile edge computing (MEC) paradigm runs the virtual source with the edge communication between data terminals and the execution in the core network with a high pressure load. The demand to meet all the customer requirements is a better way for planning the execution with the support of cognitive agent. The user data with its behavioral approach is clubbed together to fulfill the service type for IIOT. The swarm intelligence based and reinforcement learning techniques provide a neural caching for the memory within the task execution, the prediction provides the caching strategy and cache business that delay the execution. The factors affecting this delay are predicted with mobile edge computing resources and to assess the performance in the neighboring user equipment. The effectiveness builds a cognitive agent model to assess the resource allocation and the communication network is established to enhance the quality of service. The Reinforcement Learning techniques Multi Objective Ant Colony Optimization (MOACO) algorithms has been applied to deal with the accurate resource allocation between the end users in the way of creating the cost mapping tables creations and optimal allocation in MEC.

Pressure Zoning Approach for Leak Detection in Water Distribution Systems Based on a Multi Objective Ant Colony Optimization

Leakages result in considerable loss of water in water pipe networks. Therefore it is an important issue to detect leakage amount and its approximate location. Leakages in water distribution system are directly related to the operating pressure. In the current study, a new model is proposed for leakage amount and location detection and it is applied into two benchmark water distribution networks. In the proposed method, the water distribution networks are divided into three pressure zones in order to consider the leakage differences in different operating pressures. Then, nodal pressures and demands are calibrated using a new multi objective ant colony based optimization model. In this method, leaks are simulated as extra nodal demands. For determining the nodes where leakage happens, a probability based scheme is used. The leakage occurrence probability varies depending on the pressure zone that each node is located. The results illustrate the applicability of the proposed model for detecting the leakages in water distribution systems.

Multi-objective Virtual Machine Placement for Load Balancing

The virtual machine placement is closely related to the efficient and balanced utilization of physical resources. In this paper, the influence of two scenarios about resource utilization on load balancing is analyzed. A multi-objective ant colony optimization algorithm is proposed to solve the virtual machine placement problem, which balances the load among physical machines and the internal load of physical machine simultaneously. The proposed algorithm is compared with two single objective ant colony optimization algorithms, first fit algorithm and greedy algorithm under some instances. The results show that the proposed algorithm can search and find solutions that exhibit good balance among objectives while others cannot. This demonstrates the proposed algorithm can balance the load in the process of mapping virtual machines to physical machines.

Three metaheuristics improved by a mapping method

A mapping method (MaM) is used to guide the metaheuristic for a better exploration. The MaM is adapted to the tree well known metaheuristics: Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multi Objective Ant Colony Optimization (MOACO) and Multi Objective Particle Swarm Optimization (MOPSO). The new hybridized Metaheuristics (MaM-SPEA2, MaM-MOACO, MaM-MOPSO) are applied to solve the Flexible Job Shop Problem (FJSP) with the objectives of minimizing the makespan (Cmax) and the production just in time. Then, the multi-objective metric C-Metric’ show that MaM hybridization improves the performances of the three algorithms.

A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP

The difficulty to solve multiple objective combinatorial optimization problems with traditional techniques has urged researchers to look for alternative, better performing approaches for them. Recently, several algorithms have been proposed which are based on the ant colony optimization metaheuristic. In this contribution, the existing algorithms of this kind are reviewed and a proposal of a taxonomy for them is presented. In addition, an empirical analysis is developed by analyzing their performance on several instances of the bi-criteria traveling salesman problem in comparison with two well-known multi-objective genetic algorithms.

Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic

This paper presents an ant colony optimization metaheuristic for the solution of an industrial scheduling problem in an aluminum casting center. We present an efficient representation of a continuous horizontal casting process which takes account of a number of objectives that are important to the scheduler. We have incorporated the methods proposed in software that has been implemented in the plant.