Proposed Ant Colony Optimization Algorithm Research Articles

Demand side management using ant colony optimization algorithm in renewable energy integrated smart grid

The increasing demand for electrical energy is a result of advancing technologies and changing lifestyles worldwide. Meeting this escalating energy need poses a substantial challenge, especially the difficulty in constructing new conventional power plants due to limited fossil fuel resources. To address this, demand-side management (DSM) in smart grid (SG), integrated with solar photovoltaic energy (SPE) have emerged as a crucial tool for effectively managing electricity demand, ensuring flexibility and reliability. DSM achieves optimal electricity utilization by rescheduling the operation schedules of consumer appliances and carefully adjusting their demand profiles. Integrating DSM into a smart grid framework is highly advantageous for the power industry’s pursuit of sustainable energy goals. While various heuristic-based optimization techniques have been employed for DSM, the focus on SPE has been limited to small-scale residential loads. This study utilizes the Ant Colony Optimization (ACO) algorithm to tackle a day ahead DSM minimization problem, considering SPE in areas with large number of appliances. The DSM minimization problem falls into the category of discrete combinatorial problems, making it well-suited for ACO optimization. The self-healing, self-protection, and self-organizing attributes of ACO make it particularly effective for DSM solutions. Residential, commercial, and industrial loads, with and without SPE integration, are considered to demonstrate the efficacy of the proposed ACO algorithm. Simulation results are compared with other studies in the literature, including Evolutionary Algorithm (EA), Moth Flame Optimization (MFO), and Bacterial Foraging Optimization (BFO), in terms of reducing consumer’s cost of energy (CCE) and utility peak load (UPL). The findings indicate that the proposed ACO algorithm outperforms the other algorithms considered in the current context.

Modification of the Ant Colony Optimization Algorithm for Solving Multi-Agent Task Allocation Problem in Agricultural Application

This paper considers the problem of task allocation where the goal is to find a coalition of UAVs (agents) to complete on-farm agricultural tasks. In this study, Ant Colony Optimization (ACO) algorithm is employed to find the best coalition of agents. The performance of the basic ACO algorithm for solving task allocation is improved by modifying the efficiency factor. In the proposed algorithm, the efficiency factor is defined as a function that relates not only to the capability of the agents and the distance between the agents, but also to the distance between the agents and the target. To solve the task allocation problem, the capability list of the agents was also adjusted using common UAV capabilities in agricultural application. Simulation results showed that the proposed ACO algorithm with the modified efficiency factor improved the performance of basic ACO algorithm for solving task allocation problem in terms of the average total travel cost for each agent. The optimum number of ants and agents in the proposed algorithm was also analysed for robust performance. Simulation results revealed that the addition of the numbers of agents and ants increases the average efficiency of the algorithm. In this study, we have also added a function to calculate the system capability utilization. By employing such a function, simulation results show that the total resource used by the agents and total communication cost can be optimized. In addition, a simple experiment using five ground robots with a centralized control was also carried out as a proof of concept for the proposed algorithm.

A robust ant colony metaheuristic for urban freight transport scheduling using passenger rail network

This paper considers a promising alternative for freight transport using urban passenger rail networks. It addresses a freight rail transport scheduling problem (FRTSP), which is one of the key operational issues to integrate freight into passenger transport in urban areas. In this paper, we develop a robust ant colony optimization (ACO) metaheuristic to tackle large-size instances. Taguchi method is used to set control parameters to make the algorithm robust. Computational results clearly highlight the proposed ACO algorithm’s capability of yielding near-optimal solutions in short computation times, even in large-size instances. They also show that the proposed algorithm dominates classic ACO variants, even when the parameters of these variants are also set using Taguchi method.

Effective train routing selection for real-time traffic management: Improved model and ACO parallel computing

The real-time Railway Traffic Management Problem (rtRTMP) is the problem of detecting and solving time-overlapping conflicting requests made by multiple trains on the same track resources. This problem consists in retiming, reordering and rerouting trains in such a way that the propagation of disturbances in the railway network is minimized. The rtRTMP is an NP-complete problem and finding good strategies to simplify its solution process is paramount to obtain good quality results in a short computation time. Solving the Train Routing Selection Problem (TRSP) aims to reduce the size of rtRTMP instances by limiting the number of routing variables: during the pre-processing, the most promising routing alternatives among the available ones are selected for each train. Then, the selected alternatives are the only ones used for the rtRTMP. A first version of the TRSP has been recently proposed in the literature. This paper presents an improved TRSP model, where rolling stock re-utilization timing constraints and estimation of train delay propagation are taken into account. Additionally, a parallel Ant Colony Optimization (ACO) algorithm is proposed. We analyze the impact of the TRSP model and algorithm on the rtRTMP through a thorough computational campaign performed on a French case study with timetable disturbances and infrastructure disruptions. The presented model leads to a better correlation between TRSP and rtRTMP solutions, and the proposed ACO algorithm outperforms the state-of-the-art algorithm.

Performance Measurement of Energy Optimal Path Finding for Waste Collection Robot Using ACO Algorithm

Indoor waste collection that utilizes mobile robots can solve the labor cost and manpower shortage but has the problem of limited energy resources, making it difficult to operate for long periods of time. Therefore, it is important to reduce the energy consumption for efficient waste collection. The waste collection robot can be modeled as a Capacitated Vehicle Routing Problem (CVRP), where heuristics algorithms can be deployed to search for the most energy-efficient path. This paper proposes the Ant Colony Optimization (ACO) algorithm for finding the optimal path of the waste collection robot. Energy consumption of the robot depends not only on the travel path but also on the weight of the waste it carries. Therefore, the proposed ACO algorithm utilizes the path distance and waste weight as the visibility. The travel distance and energy consumption are also used to determine the updated pheromone. Whereas the conventional and adapted ACO algorithms use only either the path distance or the waste weight as the visibility, respectively. The simulation experiments are conducted to compare the travel distance and the energy consumption that the waste collection robot takes by using the conventional, adapted, and proposed ACO algorithms. In the simulation experiments, the number of nodes, the waste weight, and the carrying capacity are used as parameters to verify the performance under the determined environment. The simulation results express that the proposed ACO algorithm provides a better energy optimal path in terms of travel distance and energy consumption than the conventional and adapted ACO algorithms.

Solving the vehicle routing problem with drone for delivery services using an ant colony optimization algorithm

E-commerce and logistics companies are facing important challenges to satisfy the rapid growth of customer demands. Unmanned aerial vehicles such as drones are an emerging technology that are very useful to cope with rising customer expectations of fast, flexible, and reliable delivery services. Drones work in tandem with trucks to perform parcel delivery, which have proven to reduce costs, CO2 emissions, and delivery times. This research proposes a mixed integer programming formulation to address the Vehicle Routing Problem with Drone (VRPD) by assigning customers to drone-truck pairs, determining the number of dispatching drone-truck units, and obtaining optimal service routes while the fixed and travel costs of both vehicles are minimized. Given the NP-hard nature of the VRPD, an ant colony optimization (ACO) algorithm is elaborated to solve this problem. Two novel methods are proposed to investigate the efficiency of the drone-truck combination by allowing the drones to perform additional delivery services to only one feasible customer and also multiple feasible customers while the truck waits at a customer location. Experimental results show that the proposed ACO algorithm can effectively solve the VRDP for different size instances and different customer location distributions, and is successful in providing timely solutions for small test instances within 1% of the optimal solutions. Finally, experimentation also reveals that the ACO algorithm outperforms the classical VRP by obtaining cost-savings of over 30% for large instances.

Ant Colony Approach for Optimizing a Multi-stage Closed-Loop Supply Chain with a Fixed Transportation Charge

Reverse logistics (RL) network can be adequately planned and implemented to gain additional benefits such as maximizing customer satisfaction and a positive image of the business organization, even though most distribution networks are not equipped with reverse channels to deal with the return of merchandise. Therefore, the main objective of this paper is to develop a new mixed-integer nonlinear programming (MINLP) mathematical model with a single-objective, single-product, multi-stage closed-loop supply chain network design (CLSC ND), considering the fixed transportation charge in the distribution network that has been neglected in the recently published papers in the field of CLSC ND. Since such network design challenges belong to the class of NP-hard problems, an algorithm based on ant colony optimization (ACO) is proposed to design a multi-stage RL network with fixed transportation cost and variable cost for the routes. Four network characteristics of different sizes were designed, and 30 instances were randomly generated for each network characteristic to evaluate the effectiveness of the proposed algorithm. The computational analysis of the results shows the high-quality effectiveness of the proposed ACO algorithm compared with the exact results.

Role Assignment for Spatially-Correlated Data Aggregation Using Multi-Sink Internet of Underwater Things

In this paper, we consider a multi-sink underwater data aggregation network, in which a set of Internet-of-Underwater-Things devices survey an underwater area of interest and upload their data to a set of data gathering stations. A device-role assignment framework is provided, which captures the network topology and allows multi-hop data aggregation. In this framework, an optimization problem is formulated with the objective of maximizing the uncorrelated data at the gathering stations with minimal energy consumption. The optimization problem is constrained over binary coupled role assignment, inter-device, and device-station association decision variables. An ant colony optimization (ACO) algorithm is developed to tackle the complexity of the optimization problem and find optimized solutions. Simulation results illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search. Results also show that the proposed framework aggregates more uncorrelated data and preserves more energy compared to a baseline approach, where the devices transmit raw data to the stations directly.

Implementation of the ACO algorithm in an electrical vehicle system powered by five-level NPC inverter

The ant colony optimization (ACO) algorithm is generally used to create an optimal PI regulator added to the pulse width modulation (PWM) strategy for a five-level neutral-point-clamped (NPC) inverter implemented in electric vehicle (EV). As heavy transportation is our main focus, the proposed system was formed by two induction motors (IMs); each of which was fed by one NPC inverter. The two IMs were fixed to the front axle and the rear axle, respectively. The PDC-PWM (Phases Disposition Carrier-PWM) strategies for the three-phase five-level NPC inverter was employed in the rotor-flux-oriented control (FOC) of the induction motor (IM). Special interest was given to the analysis of the total harmonic distortion (THD%). Considering the dynamic loading conditions of an EV driven by an IM, the proposed ACO algorithm effectively calculated the required PI parameter to find the best PI regulator gains values. These optimizations were applied to the PDC-PWM strategy in order to obtain the minimum THD% in both current and voltage outputs. The effectiveness of the introduced system for a wide range of torque/speed variations was proven through simulation by developing a model of an IM traction inverter drive with the FOC strategy for closed-loop control. As the experimental test realization of the designed system was not possible because it is very expensive, the ACO optimization performance was confirmed by means of MATLAB/SimPower Systems simulations.

Energy-Efficient Data Dissemination Using a UAV: An Ant Colony Approach

In this letter, we address the problem of minimizing the energy consumption of disseminating a library of files to a set of Internet-of-Things (IoT) devices using an unmanned aerial vehicle (UAV). A framework is provided, in which a subset of devices are selected to receive data from a UAV and then forward the required data to other devices. Furthermore, optimal energy-efficient path selection is considered in order to realize efficient data dissemination. Specifically, an optimization problem is formulated to minimize the energy expenditure of the IoT devices and UAV while the latter tours to disseminate the required files to the former. An ant colony optimization (ACO) algorithm is developed to solve the optimization problem. Simulation results show that the proposed framework is more energy-efficient compared to a baseline approach, where the UAV hovers above each device to deliver the data. Results also illustrate that the proposed ACO algorithm provides performance close to the optimal solution, which is obtained through exhaustive search.

Integrated scheduling on parallel batch processing machines with non-identical capacities

ABSTRACTIn this article, the problem of scheduling a set of jobs on parallel batch machines with arbitrary capacities is considered. The jobs have identical processing time, non-identical sizes and unequal weights. After being processed, the jobs are delivered to the customers by the vehicles. The objective is to minimize the total weighted delivery time of the jobs. Two heuristic algorithms and an algorithm based on ant colony optimization (ACO) are presented to address the problem. Considering the relationship between the jobs and the batches, a three-level candidate list is designed for building the solutions effectively. Furthermore, based on the change of the normalized weight of the batch, the heuristic information is designed to control the search direction of the ants and improve the solution quality. The experimental results show that the performance of the proposed ACO algorithm is superior to the other algorithms compared.

Solving the Feeder Vehicle Routing Problem using ant colony optimization

Abstract This paper studies the Feeder Vehicle Routing Problem (FVRP), a new variant of the vehicle routing problem (VRP), in which each customer is served by either a large (truck) or a small vehicle (motorcycle). In this particular type of delivery, the trucks and the motorcycles must depart from the depot, visit the customers, and eventually return to the depot. During the delivery process, the motorcycles travel to the truck locations for reloading. The ant colony optimization (ACO) algorithm is employed for solving the problem with the objective of determining the number of dispatching sub-fleets and optimal routes to minimize the total cost (fixed route and travel costs). Three benchmark datasets are generated to examine the performance of the FVPR. For comparison purposes, all instances are executed by dispatching only trucks as in the traditional VRP and a four-stage hierarchical heuristic. Additionally, ACO is compared to optimal solutions for small instances. The results indicate that the proposed ACO algorithm yields promising solutions particularly for large instances within a reasonable time frame in an efficient manner.

An Intelligent Packing Programming for Space Station Extravehicular Missions

Packing programming for extravehicular missions to the space station is the process of arranging a set of missions into multiple extravehicular activities. It is an interesting combinatorial optimization problem developed from the traditional bin-packing problem. This paper first formulates a practical mathematical model that considers both the constraints of the time window for each extravehicular mission and the spacewalk time per astronaut. An Ant Colony Optimization (ACO) algorithm with a self-adaptation strategy and a new pheromone matrix characterizing the relationship between any two extravehicular missions is then proposed. The simulation results on various independent experiments show that the proposed ACO algorithm is capable of producing optimal packing programming schemes with a success rate of over 90%, which is acceptable for application to real-world problems.

An ant colonial optimisation approach for no-wait permutation flow shop scheduling

This research aims to address the applications of variants of ant colony optimisation (ACO) approach to solve no-wait flow shop scheduling problem (NW-FSSP). The most suitable ACO algorithm out of basic algorithms has been selected and modified to achieve more purified results. The algorithm was coded in visual basic. The varied algorithm has been applied to the bench mark problems and results were compared with the results achieved previously by other researchers using different meta-heuristics. The research covers detailed steps carried out for application of basic ACO algorithms on bench mark problems, comparison of results achieved by application of basic ACO algorithms, selection of best out of basic algorithms, modification of selected basic algorithm and generation of varied ACO algorithm. The varied ACO algorithm gave reasonably good results for almost all the problems under consideration and was able to handle fairly large sized problems with far less computational time. Comparative analysis depicted that the proposed ACO algorithm performed better than genetic algorithm on large sized problems and better than Rajendran heuristic in almost all problems under considerations.

An ant colonial optimisation approach for no-wait permutation flow shop scheduling

This research aims to address the applications of variants of ant colony optimisation (ACO) approach to solve no-wait flow shop scheduling problem (NW-FSSP). The most suitable ACO algorithm out of basic algorithms has been selected and modified to achieve more purified results. The algorithm was coded in visual basic. The varied algorithm has been applied to the bench mark problems and results were compared with the results achieved previously by other researchers using different meta-heuristics. The research covers detailed steps carried out for application of basic ACO algorithms on bench mark problems, comparison of results achieved by application of basic ACO algorithms, selection of best out of basic algorithms, modification of selected basic algorithm and generation of varied ACO algorithm. The varied ACO algorithm gave reasonably good results for almost all the problems under consideration and was able to handle fairly large sized problems with far less computational time. Comparative analysis depicted that the proposed ACO algorithm performed better than genetic algorithm on large sized problems and better than Rajendran heuristic in almost all problems under considerations.

A short-term operating room surgery scheduling problem integrating multiple nurses roster constraints

Operating room (OR) surgery scheduling determines the individual surgery's operation start time and assigns the required resources to each surgery over a schedule period, considering several constraints related to a complete surgery flow and the multiple resources involved. This task plays a decisive role in providing timely treatments for the patients while balancing hospital resource utilization. The originality of the present study is to integrate the surgery scheduling problem with real-life nurse roster constraints such as their role, specialty, qualification and availability. This article proposes a mathematical model and an ant colony optimization (ACO) approach to efficiently solve such surgery scheduling problems. A modified ACO algorithm with a two-level ant graph model is developed to solve such combinatorial optimization problems because of its computational complexity. The outer ant graph represents surgeries, while the inner graph is a dynamic resource graph. Three types of pheromones, i.e. sequence-related, surgery-related, and resource-related pheromone, fitting for a two-level model are defined. The iteration-best and feasible update strategy and local pheromone update rules are adopted to emphasize the information related to the good solution in makespan, and the balanced utilization of resources as well. The performance of the proposed ACO algorithm is then evaluated using the test cases from (1) the published literature data with complete nurse roster constraints, and 2) the real data collected from a hospital in China. The scheduling results using the proposed ACO approach are compared with the test case from both the literature and the real life hospital scheduling. Comparison results with the literature shows that the proposed ACO approach has (1) an 1.5-h reduction in end time; (2) a reduction in variation of resources' working time, i.e. 25% for ORs, 50% for nurses in shift 1 and 86% for nurses in shift 2; (3) an 0.25h reduction in individual maximum overtime (OT); and (4) an 42% reduction in the total OT of nurses. Comparison results with the real 10-workday hospital scheduling further show the advantage of the ACO in several measurements. Instead of assigning all surgeries by a surgeon to only one OR and the same nurses by traditional manual approach in hospital, ACO realizes a more balanced surgery arrangement by assigning the surgeries to different ORs and nurses. It eventually leads to shortening the end time within the confidential interval of [7.4%, 24.6%] with 95% confidence level. The ACO approach proposed in this paper efficiently solves the surgery scheduling problem with daily nurse roster while providing a shortened end time and relatively balanced resource allocations. It also supports the advantage of integrating the surgery scheduling with the nurse scheduling and the efficiency of systematic optimization considering a complete three-stage surgery flow and resources involved.

Ant colony optimizer for phasor measurement units placement

This paper proposes an approach for optimal placement of phasor measurement units (PMUs) for complete observability at normal and emergency conditions such as any single line outage or any single PMU loss using ant colony optimization (ACO) algorithm. The objective function is to find the optimal locations of PMUs and the minimum number of PMU channels by optimizing the logic decision of control variables, under the observability constraint. The ACO algorithm is applied to the standard IEEE 14-bus, 30-bus and New England 39-bus systems. In addition, an application of the proposed algorithm to a real power system of the west Delta network (WDN) as a part of the Unified Egyptian Network (UEN) is presented. The results obtained are compared with those obtained using other techniques. Simulation results show that the proposed ACO algorithm is more accurate and efficient for obtaining the optimal placement of PMUs with minimum number of PMU channels, especially with increasing the system size.

OPTIMAL PLACEMENT OF PHASOR MEASUREMENT UNITS WITH LIMITED CHANNELS USING ANT COLONY OPTIMIZATION ALGORITHM

This paper proposes an approach for optimal placement of phasor measurement units (PMUs) for complete observability at normal and emergency conditions such as any single line outage or any single PMU loss using ant colony optimization (ACO) algorithm. The objective function is to find theoptimallocations of PMUs and the minimum number of PMU channels by optimizing the logic decision of control variables, under the observability constraint. The ACO algorithm is applied to the standard IEEE 14-bus, 30-bus and New England 39-bus systems. Add to that, an application of the proposed algorithm to a real power system of the west Delta network (WDN) as a part of the Unified Egyptian Network (UEN) is presented. The results obtained are compared with those obtained using other techniques. Simulation results show that the proposed ACO algorithm is more accurate and efficient for obtaining the optimal placement of PMUs with minimum number of PMU channels, especially with increasing the system size.

Discussion of “Ant Colony Optimization for Multimode Resource-Constrained Project Scheduling” by Hong Zhang

The paper used the ant colony optimization (ACO) metaheuristic method to solve multimode resource-constrained project scheduling problems with the objective of minimizing project duration. The author used two small examples to compare the ACO method with two other metaheuristic methods: genetic algorithm (GA) and particle swarm optimization (PSO). For each case study, several runs of each metaheuristic method were conducted and the results were used to report the success rates of the three methods, which were 73, 74, and 80%, on average, for GA, PSO, and ACO, respectively (based on Table 4 in the paper). The author concluded that the proposed ACO algorithm is an effective methodology that can help practitioners plan construction projects. In addition, the author mentioned that further studies will address deep insight into the parameters of the ACO algorithm in addition to other considerations, including multiple objective optimization. In the opinion of the discussers, the results of the three methods on the two small examples are very comparable and, because of the random nature of these processes, their comparative quality is highly problem dependent. All of these methods, however, are highly inefficient to handle large-scale problems of any practical size. As problem size increases (e.g., hundreds of activities) and multiple objectives are considered, all metaheuristic methods exhibit an exponential increase in the solution space (number of possible solutions), which makes the search for an optimum solution a difficult and time consuming task. Kandil and El-Rayes (2005), for example, reported a GA processing time of 55 h for a case study of only 360 activities to reach optimum solution, which was reduced to 9.3 h by using a system of parallel computing with 50 processors. Thus, the focus of future work in this area should be introducing efficient methods to handle practical sized problems. Introducing new metaheuristics or tweaking existing methods to achieve minor performance improvements on small textbook-type problems has no practical value.

Scheduling in Grid Systems using Ant Colony Algorithm

Task scheduling is an important factor that directly influences the performance and efficiency of the system. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and data grid. Job scheduling in computing grid is a very important problem. To utilize grids efficiently, we need a good job scheduling algorithm to assign jobs to resources in grids. This paper presents a new algorithm based on ant colony optimization (ACO) metaheuristic for solving this problem. In this study, a proposed ACO algorithm for scheduling in Grid systems will be presented. Simulation results indicate our ACO algorithm optimizes total response time and also it increase utilization.