Multi-objective Ant Colony System Research Articles

Cooperative optimal operation of hybrid energy integrated system considering multi‐objective dragonfly algorithm

AbstractTo meet the recent energy demand, hybrid energy systems (HESs) play an important role in providing more stable power as well as backup power to the grid. It is always preferable for the power system network to be operated in a secure, dependable, stable, and cost‐effective manner. The optimal power flow (OPF) problem is used to determine the ideal power system network control parameter settings. The majority of the OPF problem, fuel cost reduction is treated as an objective function, but the environmental impact of the generating is ignored. Both fuel expense and emission are treated as objective functions of the work in this case. This research describes a solution for multi‐objective optimal power flow (MOOPF) with a HES integrated conventional power system. HES in this work is made up of a wind park and a compressed air energy storage system (CAES). In this case, locational marginal pricing (LMP) is employed to determine the best location of HES in the power system. To achieve the goals of this work, the multi‐objective dragonfly algorithm (MODA) is used. The IEEE 30 bus system is used to evaluate the MODA. The MODA method findings are validated against other well‐known optimization algorithms such as the multi‐objective ant colony system (MOACS) algorithm, multi‐objective enhanced self‐adaptive differential evolution (MOESDE) algorithm, modified multi‐objective evolutionary algorithm‐based decomposition (MOEA/D), multi‐objective particle swarm algorithm (MOPOSO), and non‐dominated sorting genetic algorithm (NSGA‐II).

A Multipopulation Multiobjective Ant Colony System Considering Travel and Prevention Costs for Vehicle Routing in COVID-19-Like Epidemics

As transportation system plays a vastly important role in combatting newly-emerging and severe epidemics like the coronavirus disease 2019 (COVID-19), the vehicle routing problem (VRP) in epidemics has become an emerging topic that has attracted increasing attention worldwide. However, most existing VRP models are not suitable for epidemic situations, because they do not consider the prevention cost caused by issues such as viral tests and quarantine during the traveling. Therefore, this paper proposes a multi-objective VRP model for epidemic situations, named VRP4E, which considers not only the traditional travel cost but also the prevention cost of the VRP in epidemic situations. To efficiently solve the VRP4E, this paper further proposes a novel algorithm named multi-objective ant colony system algorithm for epidemic situations, termed MOACS4E, together with three novel designs. First, by extending the efficient “multiple populations for multiple objectives” framework, the MOACS4E adopts two ant colonies to optimize the travel and prevention costs respectively, so as to improve the search efficiency. Second, a pheromone fusion-based solution generation method is proposed to fuse the pheromones from different colonies to increase solution diversity effectively. Third, a solution quality improvement method is further proposed to improve the solutions for the prevention cost objective. The effectiveness of the MOACS4E is verified in experiments on 25 generated benchmarks by comparison with six state-of-the-art and modern algorithms. Moreover, the VRP4E in different epidemic situations and a real-world case in the Beijing-Tianjin-Hebei region, China, are further studied to provide helpful insights for combatting COVID-19-like epidemics.

Real Environment-Aware Multisource Data-Associated Cold Chain Logistics Scheduling: A Multiple Population-Based Multiobjective Ant Colony System Approach

Cold chain logistics (CCL) scheduling is important for smart cities as it directly affects the service quality and operating profits of logistics companies. However, traditional CCL models seldom reflect the real transportation environment, making the solutions hardly applicable to the real CCL scenes. Hence, this paper attempts to establish a multisource data-associated CCL model oriented to the real transportation environment. This environment is considered by employing the real-captured driving duration and distance between any two places. Three scheduling objectives (namely, quality losses, personnel and vehicle costs, and transportation costs) are taken into account. To efficiently solve the proposed multisource data-associated multiobjective CCL model, a multiple population-based multiobjective ant colony system (MPMOACS) approach is proposed. Based on the multiple populations for multiple objectives framework, the MPMOACS approach can optimize multiple objectives sufficiently, and thus obtain promising solutions distributed along the entire Pareto front. To further enhance the performance of the MPMOACS, a ranking-based local search strategy is also designed. Experiments are conducted on not only the existing benchmark instances but also a real environment-aware multisource dataset that is built based on real-captured transportation data of Guangzhou and Shenzhen, China. Compared with six state-of-the-art and very recent well-performing multiobjective optimization approaches, the proposed MPMOACS approach exhibits the overall best performance.

Cooperative Task Allocation for Multi-Robot Systems Based on Multi-Objective Ant Colony System

This paper proposes a novel multi-objective ant colony system (MOACS) approach to solve the cooperative task allocation problem of multi-robot systems. The task allocation problem is formulated as a multi-objective multiple traveling salesman problem (MTSP). The objectives are to minimize the total and maximum cost of the robotic vehicles so that the workload of each vehicle could be balanced. The time cost matrices of the salesmen are different and asymmetric due to the different flight speeds of vehicles and executing time of tasks. Based on the single-objective ant colony system (ACS), a novel solution construction method and a novel pheromone update rule are proposed. At each step in the solution construction phase, the ant with minimum cost has the biggest chance to add an unassigned task to balance the workload of each vehicle, while the ant with maximum cost also has a bigger chance than any other ants to add an unassigned task to find better Pareto front. The minimum value of the pheromone is limited in the pheromone update phase, which is helpful in avoiding fast convergence and local optima. Extensive simulation results suggest that the proposed MOACS has better performance and effectiveness than the existing non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Hardware-in-the-loop experiments on multiple unmanned aerial vehicles (UAVs) also show that compared with NSGA-II and MOPSO, the maximum and total flight distance of the UAVs with the proposed MOACS are decreased by up to 28.46% and 26.34%, respectively, while the maximum and total time used to finish all tasks are decreased by up to 23.86% and 17.94%.

Research on hot rolling scheduling problem based on Two-phase Pareto algorithm.

Under the background of excess capacity and energy saving in iron and steel enterprises, the hot rolling batch scheduling problem based on energy saving is a multi-objective and multi constraint optimization problem. In this paper, a hybrid multi-objective prize-collecting vehicle routing problem (Hybrid Price Collect Vehicle Routing Problem, HPCVRP) model is established to ensure minimum energy consumption, meet process rules, and maximize resource utilization. A two-phase Pareto search algorithm (2PPLS) is designed to solve this model. The improved MOEA/D with a penalty based boundary intersection distance (PBI) algorithm (MOEA/D-PBI) is introduced to decompose the HPCVRP in the first phase. In the second phase, the multi-objective ant colony system (MOACS) and Pareto local search (PLS) algorithm is used to generate approximate Pareto-optimal solutions. The final solution is then selected according to the actual demand and preference. In the simulation experiment, the 2PPLS is compared with five other algorithms, which shows the superiority of 2PPLS. Finally, the experiment was carried out on actual slab data from a steel plant in Shanghai. The results show that the model and algorithm can effectively reduce the energy consumption in the process of hot rolling batch scheduling.

A Multi-Objective Ant Colony System Algorithm for Airline Crew Rostering Problem With Fairness and Satisfaction

The airline crew rostering problem (CRP) is significant for balancing the workload of crew and for improving the satisfaction rate of crew’s preferences, which is related to the fairness and satisfaction of crew. However, most existing work considers only one objective on fairness or satisfaction. In this study, we propose a new practical model for CRP that takes both fairness and satisfaction into account simultaneously. To solve the multi-objective CRP efficiently, we develop an ant colony system (ACS) algorithm based on the multiple populations for multiple objectives (MPMO) framework, termed multi-objective ACS (MOACS). The main contributions of MOACS lie in three aspects. Firstly, two ant colonies are utilized to optimize fairness and satisfaction objectives, respectively. Secondly, a new hybrid complementary heuristic strategy with three kinds of heuristic information schemes is proposed to avoid ant colonies focusing only on their own objectives. Ant colonies randomly choose one of the three schemes to help explore the Pareto front (PF) sufficiently. Thirdly, a local search strategy with two types of local search respectively for fairness and satisfaction is designed to further approach the global PF. The MOACS is applied to seven real-world monthly CRPs with different sizes from a major North-American airline. Experimental results show that MOACS generally outperforms the greedy algorithm and some other popular multi-objective optimization algorithms, especially on large-scale instances.

GRP-HEFT: A Budget-Constrained Resource Provisioning Scheme for Workflow Scheduling in IaaS Clouds

In Infrastructure as a Service (IaaS) Clouds, users are charged to utilize cloud services according to a pay-per-use model. If users intend to run their workflow applications on cloud resources within a specific budget, they have to adjust their demands for cloud resources with respect to this budget. Although several scheduling approaches have introduced solutions to optimize the makespan of workflows on a set of heterogeneous IaaS cloud resources within a certain budget, the hourly-based cost model of some well-known cloud providers (e.g., Amazon EC2 Cloud) can easily lead to a higher makespan and some schedulers may not find any feasible solution. In this article, we propose a novel resource provisioning mechanism and a workflow scheduling algorithm, named Greedy Resource Provisioning and modified HEFT (GRP-HEFT), for minimizing the makespan of a given workflow subject to a budget constraint for the hourly-based cost model of modern IaaS clouds. As a resource provisioning mechanism, we propose a greedy algorithm which lists the instance types according to their efficiency rate. For our scheduler, we modified the HEFT algorithm to consider a budget limit. GRP-HEFT is compared against state-of-the-art workflow scheduling techniques, including MOACS (Multi-Objective Ant Colony System), PSO (Particle Swarm Optimization), and GA (Genetic Algorithm). The experimental results demonstrate that GRP-HEFT outperforms GA, PSO, and MOACS for several well-known scientific workflow applications for different problem sizes on average by 13.64, 19.77, and 11.69 percent, respectively. Also in terms of time complexity, GRP-HEFT outperforms GA, PSO and MOACS.

Multi-objective heat transfer search algorithm for truss optimization

In the real world, we often come across conditions like optimization of more than one objective functions concurrently which are of conflicting nature and that makes the prospect of the problem more intricate. To overpower this contrasting state, an efficient meta-heuristic (MH) is required, which provides a balanced trade-off between diverging objective functions and gives an optimum set of solutions. In this article, a recently proposed MH called Heat Transfer Search (HTS) algorithm is enforced to elucidate the structural optimization problems with Multi-objective functions (described as MOHTS). MOHTS is an efficient MH which works on the principle of heat transfer and thermodynamics, where search agents are molecules which interact with other molecules and with surrounding through conduction, convection, and radiation modes of heat transfer. Five challenging benchmark problems of truss optimization have been taken into consideration here to examine the effectiveness of MOHTS. Procure results through the proposed method show the predominance over considered MHs. These benchmark problems are considered for discrete design variables for the structural optimization problem with two objectives, namely minimization of truss weight and maximization of nodal displacement. Here, the Pareto-optimal front achieved through computational experiments, in the process of optimization, is evaluated by three distinct performance quality indicators namely the Hypervolume, the Front spacing metric, and Inverted Generational Distance. Also, the obtained results after a number of runs are compared with other existing optimizers in the literature like multi-objective ant system, multi-objective ant colony system, and multi-objective symbiotic organism search, which manifest the superiority in the performance of the proposed algorithm over others. The statistical analysis of the experimental work has been carried out by conducting Friedman’s rank test and Post-Hoc Holm–Sidak test.

Multiobjective Cloud Workflow Scheduling: A Multiple Populations Ant Colony System Approach.

Cloud workflow scheduling is significantly challenging due to not only the large scale of workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing model of clouds makes the execution time and execution cost two critical issues in the scheduling. This paper models the cloud workflow scheduling as a multiobjective optimization problem that optimizes both execution time and execution cost. A novel multiobjective ant colony system based on a co-evolutionary multiple populations for multiple objectives framework is proposed, which adopts two colonies to deal with these two objectives, respectively. Moreover, the proposed approach incorporates with the following three novel designs to efficiently deal with the multiobjective challenges: 1) a new pheromone update rule based on a set of nondominated solutions from a global archive to guide each colony to search its optimization objective sufficiently; 2) a complementary heuristic strategy to avoid a colony only focusing on its corresponding single optimization objective, cooperating with the pheromone update rule to balance the search of both objectives; and 3) an elite study strategy to improve the solution quality of the global archive to help further approach the global Pareto front. Experimental simulations are conducted on five types of real-world scientific workflows and consider the properties of Amazon EC2 cloud platform. The experimental results show that the proposed algorithm performs better than both some state-of-the-art multiobjective optimization approaches and the constrained optimization approaches.

A Multi-Objective Ant Colony System Algorithm for Virtual Machine Placement in Traffic Intense Data Centers

Virtual machine (VM) placement can meet different kinds of performance targets in data centers. As a result, it has become one of the most critical operations in data centers. In this paper, we investigate the VM placement problem for cloud applications, which have intense bandwidth requirements. In this kind of applications, all VMs communicate with a single designated point. The work of predecessors focuses on the revenue of communications in the network, and tries to find a good solution composed of the best fitted VMs. However, in their work, where to place the selected VMs is not important and has no effect on their objective, and this may cause high power consumption. We formulate the problem as a bin packing problem, which is strictly NP-hard. Then, we propose a multi-objective Ant Colony System (ACS) algorithm which is called ACS-BVMP. The goal is to obtain Pareto optimal solution set, which can simultaneously maximize the revenue of communications and minimize the power consumption of PMs. The proposed algorithm is tested with some instances from the literature. Its solution set is compared with two existing multi-objective algorithms and three single-objective algorithms. The results show that our proposed algorithm outperforms the above algorithms.

Optimal routing of complex transportation system of biomedical waste with multiple depot and disposal options

Determination of safe and optimal routes for biomedical waste (BMW) collection and transportation using a fleet of vehicles is a complex issue. The issue gets significant, when the vehicles that outset from multiple depots, collects the BMW from hospitals scattered around a region and carry the waste to multiple disposal sites. The present paper deals with the development of a modified ACS-based approach to determine the optimal and safest route for BMW collection and transportation for such situation. The major objectives considered for route selection of vehicles, are the risk associated with the collection and transportation of BMW; total scheduling time of vehicles and number of vehicles. In this approach, clusters of the hospital nodes are constituted based on their distance from the nearest depot and late time window associated with the hospital node. Thereafter, the routes are scheduled and optimised using modified multi-objective ant colony system (MOACS). The computed results are validated abreast with benchmark solutions, demonstrating effectiveness of the proposed approach. Its applicability is elucidated using an illustrative example based on realistic data.

Optimal routing of complex transportation system of biomedical waste with multiple depot and disposal options

Determination of safe and optimal routes for biomedical waste (BMW) collection and transportation using a fleet of vehicles is a complex issue. The issue gets significant, when the vehicles that outset from multiple depots, collects the BMW from hospitals scattered around a region and carry the waste to multiple disposal sites. The present paper deals with the development of a modified ACS-based approach to determine the optimal and safest route for BMW collection and transportation for such situation. The major objectives considered for route selection of vehicles, are the risk associated with the collection and transportation of BMW; total scheduling time of vehicles and number of vehicles. In this approach, clusters of the hospital nodes are constituted based on their distance from the nearest depot and late time window associated with the hospital node. Thereafter, the routes are scheduled and optimised using modified multi-objective ant colony system (MOACS). The computed results are validated abreast with benchmark solutions, demonstrating effectiveness of the proposed approach. Its applicability is elucidated using an illustrative example based on realistic data.

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

In this paper, we present a novel multi-objective ant colony system algorithm for virtual machine (VM) consolidation in cloud data centres. The proposed algorithm builds VM migration plans, which are then used to minimise over-provisioning of physical machines (PMs) by consolidating VMs on under-utilised PMs. It optimises two objectives that are ordered by their importance. The first and foremost objective in the proposed algorithm is to maximise the number of released PMs. Moreover, since VM migration is a resource-intensive operation, it also tries to minimise 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 centres. Moreover, it outperforms two existing ant colony optimization-based VM consolidation algorithms in terms of number of released PMs and number of VM migrations.

Efficient meta-heuristics for the Multi-Objective Time-Dependent Orienteering Problem

In this paper, the Multi-Objective Time-Dependent Orienteering Problem (MOTDOP) is investigated. Time-dependent travel time and multiple preferences are two of the most important factors in practice, and have been handled separately in previous work. However, no attempts have been made so far to consider these two factors together. Handling both multiple preferences and time-dependent travel time simultaneously poses a challenging optimization task in this NP-hard problem. In this study, two meta-heuristic methods are proposed for solving MOTDOP: a Multi-Objective Memetic Algorithm (MOMA) and a Multi-objective Ant Colony System (MACS). Two sets of benchmark instances were generated to evaluate the proposed algorithms. The experimental studies show that both MOMA and MACS managed to find better solutions than an existing multi-objective evolutionary algorithm (FMOEA). Additionally, MOMA achieved better performance than MACS in a shorter time, and is less sensitive to the parameter setting. Given that MACS inherits promising features of P-ACO, which is a state-of-the-art algorithm for multi-objective orienteering problem, the advantage of MOMA over MACS and FMOEA demonstrates the efficacy of adopting the memetic algorithm framework to solve MOTDOP.

Generalization of the MOACS algorithm for Many Objectives. An application to motorcycle distribution

To solve many-objective routing problems, this paper generalizes the Multi-Objective Ant Colony System (MOACS) algorithm, a well-known Multi-Objective Ant Colony Optimization (MOACO) metaheuristic proposed in 2003. This Generalized MOACS algorithm is used to solve a Split-Delivery/Mixed-Fleet Vehicle Routing Problem (SD/MF-VRP) under different constraints, resulting from the mathematical modeling of a logistic problem: the distribution of motorcycles by a Paraguayan factory, considering several objective functions as: (1) total distribution cost, (2) total traveled distance, (3) total traveled time, and (4) nsatisfied demand. Experimental results using the proposed algorithm in weekly operations of the motorcycle factory prove the advantages of using the proposed algorithm, facilitating the work of the logistic planner, reducing the distribution cost and minimizing the time needed to satisfy customers.

A MULTI-OBJECTIVE HYBRID ACO-PSO OPTIMIZATION ALGORITHM FOR VIRTUAL MACHINE PLACEMENT IN CLOUD COMPUTING

Cloud computing is made possible through virtualization technology which makes virtual machines (VMs) to be placed in physical servers. The process of assigning VMs to servers is called virtual machine placement. VM placement in cloud is done by focusing on many objectives like VM allocation time, energy consumption, SLA violation, utilization of resources, etc. In this paper, a multiobjective hybrid ACO-PSO optimization algorithm is proposed for minimizing resource wastage, minimizing power consumption and for load balancing in physical servers. Simulation results show that the proposed algorithm reduces resource wastage and power consumption and also provides load balancing in servers when compared to the existing multi-objective ant colony system algorithm.

A multi-objective ant colony system algorithm for virtual machine placement in cloud computing

Virtual machine placement is a process of mapping virtual machines to physical machines. The optimal placement is important for improving power efficiency and resource utilization in a cloud computing environment. In this paper, we propose a multi-objective ant colony system algorithm for the virtual machine placement problem. The goal is to efficiently obtain a set of non-dominated solutions (the Pareto set) that simultaneously minimize total resource wastage and power consumption. The proposed algorithm is tested with some instances from the literature. Its solution performance is compared to that of an existing multi-objective genetic algorithm and two single-objective algorithms, a well-known bin-packing algorithm and a max–min ant system (MMAS) algorithm. The results show that the proposed algorithm is more efficient and effective than the methods we compared it to.

A multi-objective ant colony system algorithm for flow shop scheduling problem

In this paper, we consider the flow shop scheduling problem with respect to the both objectives of makespan and total flowtime. This problem is known to be NP-hard type in literature. Several algorithms have been proposed to solve this problem. We present a multi-objective ant colony system algorithm (MOACSA), which combines ant colony optimization approach and a local search strategy in order to solve this scheduling problem. The proposed algorithm is tested with well-known problems in literature. Its solution performance was compared with the existing multi-objective heuristics. The computational results show that proposed algorithm is more efficient and better than other methods compared.

Reliability optimization using multiobjective ant colony system approaches

The multiobjective ant colony system (ACS) meta-heuristic has been developed to provide solutions for the reliability optimization problem of series-parallel systems. This type of problems involves selection of components with multiple choices and redundancy levels that produce maximum benefits, and is subject to the cost and weight constraints at the system level. These are very common and realistic problems encountered in conceptual design of many engineering systems. It is becoming increasingly important to develop efficient solutions to these problems because many mechanical and electrical systems are becoming more complex, even as development schedules get shorter and reliability requirements become very stringent. The multiobjective ACS algorithm offers distinct advantages to these problems compared with alternative optimization methods, and can be applied to a more diverse problem domain with respect to the type or size of the problems. Through the combination of probabilistic search, multiobjective formulation of local moves and the dynamic penalty method, the multiobjective ACSRAP, allows us to obtain an optimal design solution very frequently and more quickly than with some other heuristic approaches. The proposed algorithm was successfully applied to an engineering design problem of gearbox with multiple stages.

Reliability optimization using multiobjective ant colony system approaches

Reliability optimization using multiobjective ant colony system approaches