Max-Min Ant System Research Articles

Multi- Objective Optimal Design of on- Demand Pressurized Irrigation Networks

In the context of water as an economic good, from the use of water, one can derive a value, which can be affected by the reliability of supply. On-demand irrigation systems provide valuable water to skilled farmers who have the capacity to maximize economic value of water. In this study, simultaneous optimization of on-demand irrigation network layout and pipe sizes is considered taking into account both investment and annual energy costs. The optimization problem is formulated as a problem of searching for the upstream head value, which minimizes the total cost (investment and energy costs) of the system. The investment and annual energy costs are obtained in two separate phases. Max–Min ant system (MMAS) algorithm is used to obtain the minimum cost design considering layout and pipe diameters of the network simultaneously. Clement methodology is used to determine flow rates of pipelines at the peak period of irrigation requirements. The applicability of the proposed method is showed by re-designing a real world example from literature.

Energy Optimization for Train Operation Based on an Improved Ant Colony Optimization Methodology

More and more lines are using the Communication Based Train Control (CBTC) systems in urban rail transit. Trains are operated by tracking a pre-determined target speed curve in the CBTC system, so one of the most effective ways of reducing energy consumption is to fully understand the optimum curves that should prevail under varying operating conditions. Additionally, target speed curves need to be calculated with optimum real-time performance in order to cope with changed interstation planning running time. Therefore, this paper proposes a fast and effective algorithm for optimization, based on a two-stage method to find the optimal curve using a max-min ant colony optimization system, using approximate calculations of a discrete combination optimization model. The first stage unequally discretizes the line based on static gradient and speed limit in low-density and it could conduct a comprehensive search for viable energy saving target speed curves. The second stage unequally discretizes the line based on first stage discretion results, it makes full use of first-stage optimization information as pheromone, quickly optimizing the results to satisfy real-time demands. The algorithm is improved through consideration of the experience of train drivers. Finally, the paper presents some examples based on the operation data of Beijing Changping Subway Line, which is using CBTC system. The simulation results show that the proposed approach presents good energy-efficient and real-time performance.

Ant Colony Optimization for Solving the Container Stacking Problem

In this paper, the authors study the Container Stacking Problem (CSP) which is one of the most important problems in marine terminal. An optimization model is developed in order to determine the optimal storage strategy for various container-handling schedules. The objective of the model is to minimize the distance between vessel berthing location and the storage positions of containers. The CSP is solved by an efficient ant colony algorithm based on MAX-MIN ant system variant. The performance of the algorithm proposed is verified by a comparison with ILOG CPLEX for small-sized instances. In addition, numerical results for real-sized instances proved the efficiency of the algorithm.

Experimental Investigation and Performance Analysis Comparison of Max-min ANT System and ANT System using TSPLIB Data Sets

Background/Objectives: To analyse the performance of ant-colony optimization by comparing max-min ant system and ant system using TSPLIB datasets to reduce the time and provide the optimal result. Methods/Statistical Analysis: Ant Colony Optimization was being applied in TSP for optimizing the path to visit all the nodes in TSP. Working of ACO algorithm is to find out an optimal solution in a search space where finding the optimal solution is almost not a possible case. Findings: In this research, it has been given the number of cities that are in need to be travelled by salesman, and the distance between the cities will also be given. The sales persons are supposed to visit all the cities exactly once and they have to return to the source point or origin. Now the ants are allowed to travel between the nodes by applying the constraints and for choosing the next visit node using some heuristics and the pheromone quantity level as usual which is the ants indirect communication and this has been done iteratively and finally the maximum contained arc will be chosen as the best path to solve the Vehicle Routing Problem. Applications/Improvements: They have proposed a new algorithm called Ant Colony System-Software Project Scheduling Problem (ACS-SPSP) algorithm. The software project should be scheduled in terms of duration so that it should be finished in time and it should utilize the employee's skills and the cost associated with it. And also the employees can have more than one skill and their skills also should be utilized properly by considering the working time of the employees and the employees have been appointed to do a project which consists of several tasks.

A hybrid of max–min ant system and linear programming for the k-covering problem

This paper presents a hybrid algorithm of linear programming (LP), max–min ant system, and local search for solving large instances of the k-covering problem (SCkP). This algorithm exploits the LP-relaxation solution by classifying the columns, based on their reduced costs, into three sets, such that two of these sets have the columns that need to be included or excluded from any solution while ants search the third set, the selection set, to construct their feasible solutions. Moreover, to choose high-quality columns from the selection set, ants rely on heuristic information derived from the rows’ dual costs, which we obtain from the LP-relaxation solution as well. To benchmark our algorithm, we solve a set of 135 instances and compare the results with those of the state-of-the-art algorithm, in addition to the best-known solutions obtained using a branch and bound algorithm. Our algorithm shows superior results in terms of solution quality and computation time. Moreover, it can identify two new best-known solutions.

Fine-tuning heuristic methods for combinatorial optimization in forest planning

Heuristic methods are commonly used in complicated spatial forest planning problems to find the best combination of management alternatives for stands. The performance of heuristic methods depends on the parameters that guide their search processes. This study used numerical optimization to find the optimal parameter values for simulated annealing (SA), threshold accepting (TA), great deluge (GD), tabu search (TS), genetic algorithm (GA) and ant colony optimization (AC) when they are used for combinatorial optimization in forest planning. Ant colony optimization was implemented using the Max–Min Ant System, which was applied for the first time to forest planning problem. Solutions found by different heuristic methods for a non-spatial and a spatial forest planning problem were compared in a situation where the search time was restricted. The comparisons revealed that SA and TA were the best methods for fast search in both non-spatial and spatial problems. GA and AC were the least satisfactory methods, and GD and TS were between the best and the worst heuristics. The main reason for the poor performance of GA and AC was their slow search process. Differences between heuristic methods decreased when the allowed search time increased.

Minimizing total flow time on a batch processing machine using a hybrid max–min ant system

A single batch processing machine scheduling problem with non-identical job sizes to minimize the total flow time is investigated. The problem is formulated as a binary mixed integer programming model. Since the research problem is shown to be NP-hard, a hybrid metaheuristic algorithm based on the max–min ant system (MMAS) is proposed. MMAS is an ant colony optimization algorithm derived from ant system. In the proposed algorithm, first, a sequence of jobs is constructed based on the MMAS algorithm. Then, a dynamic programming algorithm is applied to obtain the optimal batching for the given job sequence. At last, an effective local search procedure is embedded in the algorithm for finding higher quality solutions. The performance of the proposed algorithm is compared with CPLEX and available heuristics in the literature. Computational results demonstrate the efficacy of the proposed algorithm in terms of the solution quality.

The GPU-based parallel Ant Colony System

The Ant Colony System (ACS) is, next to Ant Colony Optimization (ACO) and the MAX–MIN Ant System (MMAS), one of the most efficient metaheuristic algorithms inspired by the behavior of ants. In this article we present three novel parallel versions of the ACS for the graphics processing units (GPUs). To the best of our knowledge, this is the first such work on the ACS which shares many key elements of the ACO and the MMAS, but differences in the process of building solutions and updating the pheromone trails make obtaining an efficient parallel version for the GPUs a difficult task. The proposed parallel versions of the ACS differ mainly in their implementations of the pheromone memory. The first two use the standard pheromone matrix, and the third uses a novel selective pheromone memory. Computational experiments conducted on several Traveling Salesman Problem (TSP) instances of sizes ranging from 198 to 2392 cities showed that the parallel ACS on Nvidia Kepler GK104 GPU (1536 CUDA cores) is able to obtain a speedup up to 24.29x vs the sequential ACS running on a single core of Intel Xeon E5-2670 CPU. The parallel ACS with the selective pheromone memory achieved speedups up to 16.85x, but in most cases the obtained solutions were of significantly better quality than for the sequential ACS.

Reactive max-min ant system with recursive local search and its application to TSP and QAP

AbstractAnt colony optimization is a successful metaheuristic for solving combinatorial optimization problems. However, the drawback of premature exploitation arises in ant colony optimization when coupled with local searches, in which the neighborhood’s structures of the search space are not completely traversed. This paper proposes two algorithmic components for solving the premature exploitation, i.e. the reactive heuristics and recursive local search technique. The resulting algorithm is tested on two well-known combinatorial optimization problems arising in the artificial intelligence problems field and compared experimentally to six (6) variants of ACO with local search. Results showed that the enhanced algorithm outperforms the six ACO variants.

An integrated ant colony optimization algorithm to solve job allocating and tool scheduling problem

In this article, max–min ant colony optimization algorithm is proposed to determine how to allocate jobs and schedule tools with the objective of minimizing the makespan of processing plans in flexible manufacturing system. To expand the application range of max–min ant colony optimization algorithm, tool movement policy is selected as the running mode of flexible manufacturing system, which assumes that tools are shared among work centers and each operation is allowed to be machined by different kinds of tools. In the process of converting this scheduling problem into traveling salesman problem, disjunctive graph is modified to possess more than one path between each neighbor node. Besides providing practical methods of initializing pheromone, selecting node and calculating pheromone increment, max–min ant colony optimization algorithm employs the pheromone updating rule in max–min ant system to limit pheromone amount in a range, of which the upper and lower boundaries are updated after each iteration by formulations involving the current optimal makespan, the average number of optional tools and parameters. Finally, different sizes of processing plans are randomly generated, through which max–min ant colony optimization algorithm is proved effectively to tackle early stagnation and local convergence and thus obtains better solution than ant colony optimization algorithm and bidirectional convergence ant colony optimization algorithm.

Branch pipe routing based on 3D connection graph and concurrent ant colony optimization algorithm

Pipe routing, in particular branch pipes with multiple terminals, has an important influence on product performance and reliability. This paper develops a new rectilinear branch pipe routing approach for automatic generation of the optimal rectilinear branch pipe routes in constrained spaces. Firstly, this paper presents a new 3D connection graph, which is constructed by extending a new 2D connection graph. The new 2D connection graph is constructed according to five criteria in discrete Manhattan spaces. The 3D connection graph can model the 3D constrained layout space efficiently. The length of pipelines and the number of bends are modeled as the optimal design goal considering the number of branch points and three types of engineering constraints. Three types of engineering constraints are modeled by this 3D graph and potential value. Secondly, a new concurrent Max–Min Ant System optimization algorithm, which adopts concurrent search strategy and dynamic update mechanism, is used to solve Rectilinear Branch Pipe Routing optimization problem. This algorithm can improve the search efficiency in 3D constrained layout space. Numerical comparisons with other current approaches in literatures demonstrate the efficiency and effectiveness of the proposed approach. Finally, a case study of pipe routing for aero-engines is conducted to validate this approach.

Max-Min Ant System based Approach for Intelligent VM Migration and Consolidation for Green Cloud Computing

Cloud computing has bring a revolution in the field of computing. Many algorithms are proposed to make it even more efficient. In cloud computing Virtualization plays an important role and whole performance of cloud depends on VM allocation and Migration. As lots of energy is consumed in this technology so algorithms to save energy and improve efficiency are proposed called Green algorithms. In this paper a green algorithm for VM Migration is proposed using metaheuristic algorithm called ACO. The variant of ACO used in this paper is Max-Min Ant System. Results show that MaxMin Ant System gives best result as compared to other approaches in terms of VM Migrations, VM consolidation and energy consumptions.

Optimisation of total water coverage in a network with priority using max-min ant system algorithm

A general mathematical programming algorithm is presented to optimise the total cost for the whole water network achieving total water coverage to all demand points in a water network with priority considering penalties associated with failure to meet the demand of two classes of consumers. The algorithm accommodates water leakages and pressure-driven demand and their effects on allocating water. Max-min ant system (MMAS) is used to solve the model using MIDACO 3.0 after interfacing MATLAB 7.0.4 and GAMS 23.9.4. The model is first solved using a particle swarm optimisation (PSO) algorithm. MMAS proved to be an effective water network optimisation tool with priority evidenced in an analysis of the Bulawayo water network by a lower total cost, inclusive of penalties for failure to meet demand for senior priority water users, which is reduced by 25.4%.

Optimisation of recommended speed profile for train operation based on ant colony algorithm

An automatic train operation (ATO) system generally consists of the generation of recommended speed profile and the speed tracking strategy. It determines the tracked trajectory and the energy consumption of trains during the trip. Therefore, the optimisation of recommended speed profile and the ATO tracking strategy are regarded as two important means to achieve energy-efficient train operation between the successive stations. By considering the ATO tracking strategy, an optimisation method of the recommended speed profile is proposed in this paper. Based on the approximate calculation, a discrete combination optimisation model is formulated and a modified max-min ant system (MMAS) is taken as the core algorithm. With the integration speed tracking strategy, this method achieves the recommended speed profile with optimised energy consumption and a perfect running punctuality along the actual tracked trajectory. The computation time of the algorithm is shorter and the switching time of operation during the cruising phase is reduced by integrating the drivers' experience, which also reduces the energy consumption of train running between stations. The simulation results of a case study based on Beijing Subway verify the effectiveness of the proposed method, which has a good performance on energy-efficient train operation.

A Fractional-Order Weighted and Self-Adaptive Max-Min Ant System with 3-Opt Algorithm for Traveling Salesman Problem

In this paper, a Fractional-order weighted and Self-adaptive Max-Min Ant System (FS-MMAS) is proposed to make full use of spatial information of the traveling salesman problem. Furthermore, it is realized so that ants can select next city according to the complex topography. Most advanced algorithms based on Ant Colony Optimization can't take advantage of spatial information during the traverse tour, which easily leads to local minimums. Through the multi-scale and self-adaptive search, ants can take advantage of information which contributes to finding the global optimization. Finally, the 3-Opt algorithm is used to improve local solutions. The performance of proposed method was investigated on eight different benchmark problems taken from a literature and proved to be better than other well-known methods in terms of solution quality and robustness.

A New Ant Algorithm for Optimal Service Selection with End-to-End QoS Constraints

In this paper, we study the dynamic service composition becomes a decision problem on which component services should be selected under the E2E-to- End Quality of Service constraints. This problem is modeled is a nonlinear optimization problem under several constraints. We have two approaches to solve this problem. The first approach is local selection which is an efficient strategy with polynomial time complexity but can not handle global constraints. The second approach is the traditional global selection approach based on integer linear programming can handle global constraints, but its poor performance renders it inappropriate for practical applications with dynamic requirements. To overcome this disadvantage, in this study we proposed a novel Min-Max Ant System algorithm with the new utilities heuristic information to search the global approximate optimal. Our goal focus on its efficiency in terms of execution time and the quality in terms of the obtained best solution fitness value. The experiment results show that our algorithm is efficiency and feasibility more than the recently proposed related algorithms.

An ACO algorithm for makespan minimization in parallel batch machines with non-identical job sizes and incompatible job families

We study the problem of scheduling a set of N jobs with non-identical job sizes from F different families on a set of M parallel batch machines; the objective is to minimize the makespan. The problem is known to be NP-hard. A meta-heuristic based on Max–Min Ant System (MMAS) is presented. The performance of the algorithm is compared with several previously studied algorithms by computational experiments. According to our results, the average distance between the solutions found by our proposed algorithm and the lower bounds is about 4% less than that of the best of all the compared algorithms, demonstrating that our algorithm outperforms the previously studied algorithms.

Effective heuristic for makespan minimization in parallel batch machines with non-identical capacities

We consider the problem of scheduling a set of n jobs with arbitrary job sizes on a set of m parallel batch machines with non-identical capacities; the objective is to minimize the makespan. The problem is known to be NP-hard. A heuristic based on the First-Fit-Decreasing (FFD) rule is presented as well as a meta-heuristic based on Max-Min Ant System (MMAS). The performances of the two heuristics are compared with a previously studied heuristic by computational experiments. The results show that both proposed algorithms outperform the previously studied heuristic. Moreover, the MMAS heuristic obtains better solutions compared with the FFD heuristic.

Analyzing the Effects of Instance Features and Algorithm Parameters for Max–Min Ant System and the Traveling Salesperson Problem

Ant colony optimization (ACO) performs very well on many hard optimization problems, even though no good worst case guarantee can be given. Understanding the effects of different ACO parameters and the structural features of the considered problem on algorithm performance has become an interesting problem. In this paper, we study structural features of easy and hard instances of the Traveling Salesperson problem for a well-known ACO variant called Max Min Ant System MMAS) for several parameter settings. The four considered parameters are the importance of pheromone values, the heuristic information, the pheromone update strength and the number of ants. We further use this knowledge to predict the best parameter setting for a wide range of instances taken from TSPLIB.

Implementation of a Parallel Algorithm Based on a Spark Cloud Computing Platform

Parallel algorithms, such as the ant colony algorithm, take a long time when solving large-scale problems. In this paper, the MAX-MIN Ant System algorithm (MMAS) is parallelized to solve Traveling Salesman Problem (TSP) based on a Spark cloud computing platform. We combine MMAS with Spark MapReduce to execute the path building and the pheromone operation in a distributed computer cluster. To improve the precision of the solution, local optimization strategy 2-opt is adapted in MMAS. The experimental results show that Spark has a very great accelerating effect on the ant colony algorithm when the city scale of TSP or the number of ants is relatively large.