Max-Min Ant System Research Articles

A Max-Min Ant System for Research Topic Selction in Academia

University professors all over the world are facing an increasing challenges to publish more and more high-quality papers, get funds, and improve their citation indices. In many scientific disciplines, like engineering and computer science, research topics keep changing and professors’ research agendas need to change as well. In this paper, we develop a mixed-integer linear program (MILP) to model a university professor problem of selecting research topics. The problem aims to increase the professor’s number of citations subject to time constraint and publication probability constraint. We also solve the suggested MILP using a max-min ant system. A case study for a professor in an industrial engineering department is also presented.

Integrated Planning Model of Land-Use Layout and Transportation Network Design for Regional Urbanization in China Based on TOD Theory

With the acceleration of urbanization and the development of urban rail transit (URT) in China, an increasing number of metropolises have begun to apply transit-oriented development (TOD) theory in URT planning. However, spatial mismatches between residences and jobs, unbalanced traffic infrastructure utilization, and environmental pollution persist. Moreover, studies of regional-scale TOD theory are lacking in China. In this paper, an integrated model based on TOD theory is presented that optimizes land use and transportation simultaneously for a region covered by URT in China under New-type urbanization, aiming to achieve sustainable development. A max–min ant system (MMAS) algorithm integrated with the Frank–Wolfe and Dial algorithms is proposed. Jianzhou New Town is taken as a case study to validate the effectiveness of the model and algorithm. The superiority of the scheme of this integrated model is shown through comparative analysis. This research provides a new concept for governments and planners about the integrated planning of urbanization and transportation. In addition, this research extends TOD theory to employ an improved multi-objective planning model.

A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Ant colony optimization (ACO) algorithms have originally been designed for static optimization problems, where the input data is known in advance and is not subject to changes over time. Later, the long term memory of ACO proved effective for reoptimization over environment changes when extended to deal with dynamic combinatorial optimization problems (DCOPs). Among the major proposals of this kind, several adaptations of ACO procedures to improve information reuse can be identified, as well as a population-based ACO algorithm (P-ACO) specifically designed for DCOPs. Indeed, P-ACO drew the attention of the research community due to its ability to faster process pheromone information, but the few works assessing the effectiveness of the adapted ACO procedures and also P-ACO are not enough to reach more general conclusions on the current state-of-the-art in ACO for dynamic optimization. In this work, we conduct an extensive experimental campaign to evaluate the most common ACO procedure adaptations identified in the literature, using as underlying algorithms the state-of-the-art in ACO for static optimization (MAX-MIN Ant System, MMAS) and the most relevant ACO algorithm proposed for dynamic optimization (P-ACO). A variant of the traveling salesman problem with dynamic demands (DTSP) is used as test benchmark, similarly to most investigations on ACO for combinatorial optimization. Besides the carefully designed experimental setup we adopt, our work represents a significant contribution for, at least, three reasons. First, our work is the first to acknowledge that DCOPs require custom-configured parameter settings, and also the first to use automatic configuration tools for this task. Concretely, we show how the hypervolume indicator can be used to evaluate and configure the anytime behavior of algorithms for DCOPs. Second, we directly compare MMAS and P-ACO, isolating local search as an experimental factor. While P-ACO proves indeed effective in the absence of local search, MMAS is able to consistently outperform it when local search is adopted. Finally, we conduct an experimental investigation on the DCOP-specific components proposed for ACO, once again isolating local search. Results show that those components contribute very little to performance when algorithms are allowed to use local search, but are remarkably effective in its absence. In fact, coupled with DCOP components, MMAS outperforms P-ACO for a large part of our experimental setup.

An Effective Discrete Artificial Bee Colony Based SPARQL Query Path Optimization by Reordering Triples

Semantic Web has emerged to make web content machine-readable, and with the rapid increase in the number of web pages, its importance has increased. Resource description framework (RDF) is a special data graph format where Semantic Web data are stored and it can be queried by SPARQL query language. The challenge is to find the optimal query order that results in the shortest period of time. In this paper, the discrete Artificial Bee Colony (dABCSPARQL) algorithm is proposed, based on a novel heuristic approach, namely reordering SPARQL queries. The processing time of queries with different shapes and sizes is minimized using the dABCSPARQL algorithm. The performance of the proposed method is evaluated on chain, star, cyclic, and chain-star queries of different sizes from the Lehigh University Benchmark (LUBM) dataset. The results obtained by the proposed method are compared with those of ARQ (a SPARQL processor for Jena) query engine, the Ant System, the Elitist Ant System, and MAX-MIN Ant System algorithms. The experiments demonstrate that the proposed method significantly reduces the processing time, and in most queries, the reduction rate is higher compared with other optimization methods.

Parallel disassembly sequence planning using improved ant colony algorithm

Disassembly is one of the most crucial links in the product life cycle. Disassembly sequence planning (DSP) is a combination of optimization problem and has been studied by many researchers. Asynchronous parallel disassembly planning (APDP) eliminates the requirement that manipulators must start and stop their tasks in the working process. In this paper, a new asynchronous parallel disassembly sequence planning method is proposed, which to deal with the problems of highly time-dependent on asynchronous parallel disassembly mode. The disassembly time model has been divided into execution time and preparation time. The former represents the time consumes by removing a part, and the latter means the time consumes on the disassembly tool preparation while the disassembly position switch. The disassembly matrix and the disassembly work area collision matrix are also utilized to avoid infeasible sequences. Based on the max-min ant system, a waiting strategy is proposed to solve asynchronous parallel disassembly sequence planning problem. To further shorten disassembly process, a time overlap strategy is developed to take advantage of waiting time. Finally, a bevel gear reducer is utilized as an example to discuss and analysis the value of key parameters, as well as the effectiveness of the algorithm and strategies proposed in the paper.

Dynamic Path Finding using Ant Colony Optimization

Ant Colony Optimization (ACO) has been commonly applied in solving discrete optimization problems. This is an attempt to apply ACO in a dynamic environment for finding the optimal route. To create a dynamically changing scenario, in addition to distance, constraints such as air quality, congestion, user feedback, etc are also incorporated for deciding the optimal route. Max-Min Ant System (MMAS), an ACO algorithm is used to find the optimal path in this dynamic scenario. A local search parameter ε is also introduced in addition to ρ to improve the exploration of unused paths. Adaptability was studied by dynamically changing the costs associated with different parameters.

Multiple ant colony optimization using both novel LSTM network and adaptive Tanimoto communication strategy

Ant Colony Optimization (ACO) tends to fall into local optima and has insufficient convergence when solving the Traveling Salesman Problem (TSP). To overcome this problem, this paper proposes a multiple ant colony optimization (LDTACO) based on novel Long Short-Term Memory network and adaptive Tanimoto communication strategy. Firstly, we introduce an Artificial Bee Colony-based Ant Colony System (ABC-ACS), which along with the classic Ant Colony System (ACS) and Max-Min Ant System (MMAS), form the final proposed algorithm. These three types of subpopulations complement each other to improve overall optimization performance. Secondly, the evaluation reward mechanism is proposed to enhance the guiding role of the Recommended paths, which can effectively accelerate convergence speed. Besides, an adaptive Tanimoto communication strategy is put forward for interspecific communication. When the algorithm is stagnant, the homogenized information communication method is activated to help the algorithm jump out of the local optima, thus improving solution accuracy. Finally, the experimental results show that the proposed algorithm can lead to more accurate solution accuracy and faster convergence speed.

Research on Multi-Agent Task Optimization and Scheduling Based on Improved Ant Colony Algorithm

As a key problem of cloud computing, the performance of task scheduling strategy may seriously affect the efficiency and service quality of the system. With the purpose of achieving balanced task scheduling with optimal completion time, ant colony optimization (ACO) algorithm is adopted as task scheduling strategy in this paper. Considering the problem of premature convergence to local optimal solution of ACO, an improved Max-Min Ant System (MMAS) is introduced to find the global optimal solution. Based on the study of pheromone’s updating mechanism of MMAS, MMAS is applied as task scheduling strategy with reasonable mapping from the objective of shortest path to shortest completion time. The task scheduling strategy based on MMAS has been simulated from views of tasks’ number, size and load balance, and the results shows that MMAS task scheduling strategy is with a better performance on completion time and load balance than ACO based strategy.?

A Comparative Performance Analysis of Computational Intelligence Techniques to Solve the Asymmetric Travelling Salesman Problem.

This paper presents a comparative performance analysis of some metaheuristics such as the African Buffalo Optimization algorithm (ABO), Improved Extremal Optimization (IEO), Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO), Max-Min Ant System (MMAS), Cooperative Genetic Ant System (CGAS), and the heuristic, Randomized Insertion Algorithm (RAI) to solve the asymmetric Travelling Salesman Problem (ATSP). Quite unlike the symmetric Travelling Salesman Problem, there is a paucity of research studies on the asymmetric counterpart. This is quite disturbing because most real-life applications are actually asymmetric in nature. These six algorithms were chosen for their performance comparison because they have posted some of the best results in literature and they employ different search schemes in attempting solutions to the ATSP. The comparative algorithms in this study employ different techniques in their search for solutions to ATSP: the African Buffalo Optimization employs the modified Karp–Steele mechanism, Model-Induced Max-Min Ant Colony Optimization (MIMM-ACO) employs the path construction with patching technique, Cooperative Genetic Ant System uses natural selection and ordering; Randomized Insertion Algorithm uses the random insertion approach, and the Improved Extremal Optimization uses the grid search strategy. After a number of experiments on the popular but difficult 15 out of the 19 ATSP instances in TSPLIB, the results show that the African Buffalo Optimization algorithm slightly outperformed the other algorithms in obtaining the optimal results and at a much faster speed.

Laser Ablation Manipulator Coverage Path Planning Method Based on an Improved Ant Colony Algorithm

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.

Research on emotion recognition based on ECG signal

In this paper, we mainly study the emotion recognition algorithm based on ECG signals, extract the correlation feature and time-frequency domain statistical feature of ECG signals, and introduce SVW, CART and KNN three classification algorithms commonly used in emotion recognition. By comparing the accuracy of emotion recognition in the application of three classification algorithms between the correlation features of ECG signals and traditional time-frequency domain features, we found that the use of correlation features of ECG signals can get a higher recognition rate, which is 16.7%∼19.7% higher than that of the traditional feature. In addition, among the three classification algorithms, KNN algorithm can get the highest emotion recognition accuracy. In order to further improve the accuracy of emotion recognition, Max-Min Ant System is combined with KNN classification algorithm in this paper to optimize the feature combination. The overall recognition rate reaches 92%, which is 16.9% higher than the accuracy of emotion recognition directly using KNN classification algorithm.

A Strategy to Alleviate Rush Hour Traffics in Urban Areas Based on School-Trip Commute Information

As one of the main social issues in the 21st century, urban traffic congestion has become a severe challenge, especially during rush hours. Investigation indicates relationships between school-trip commutes, i.e., students' trips to and from school, and traffic congestion. Therefore, optimizing traffic in terms of the school-trip commute is considered as an important practice of smart cities. This article proposes a long-coding adapted genetic algorithm for large-scale school district replanning. Moreover, the fitness function takes into account both the congestion coefficient model and the grayscale difference model, which can reduce the traffic congestion as well as balance the traffic load on school-trip routes. Based on the above results, the school-trip route optimization is formulated as the school bus routing problem (SBRP) and the split demand school bus routing problem (SDSBRP). A max-min ant system with a pheromone smoothing mechanism is proposed for the SBRP and the SDSBRP. Considering the large scale of the problem, a high-compactness K-means algorithm is proposed to determine bus stops before school bus routing. Besides, a public bus allocation strategy is proposed to allocate a fleet of public buses to serve school-trip routes with low allocation costs. The proposed algorithms are tested on a realistic and challenging dataset from real world. Experimental research results show the advantage of our proposed strategies in reducing overall rush-hour traffic and optimizing school-trip routes.

A novel ant colony optimization based on game for traveling salesman problem

Ant Colony Optimization (ACO) algorithms tend to fall into local optimal and have insufficient astringency when applied to solve Traveling Salesman Problem (TSP). To address this issue, a novel game-based ACO (NACO) is proposed in this report. NACO consists of two ACOs: Ant Colony System (ACS) and Max-Min Ant System (MMAS). First, an entropy-weighted learning strategy is proposed. By improving diversity adaptively, the optimal solution precision can be optimized. Then, to improve the astringency, a nucleolus game strategy is set for ACS colonies. ACS colonies under cooperation share pheromone distribution and distribute cooperative profits through nucleolus. Finally, to jump out of the local optimum, mean filtering is introduced to process the pheromone distribution when the algorithm stalls. From the experimental results, it is demonstrated that NACO has well performance in terms of both the solution precision and the astringency.

On the Resilience of Ant Algorithms. Experiment with Adapted MMAS on TSP

This paper focuses on the resilience of a nature-inspired class of algorithms. The issues related to resilience fall under a very wide umbrella. The uncertainties that we face in the world require the need of resilient systems in all domains. Software resilience is certainly of critical importance, due to the presence of software applications which are embedded in numerous operational and strategic systems. For Ant Colony Optimization (ACO), one of the most successful heuristic methods inspired by the communication processes in entomology, performance and convergence issues have been intensively studied by the scientific community. Our approach addresses the resilience of MAX–MIN Ant System (MMAS), one of the most efficient ACO algorithms, when studied in relation with Traveling Salesman Problem (TSP). We introduce a set of parameters that allow the management of real-life situations, such as imprecise or missing data and disturbances in the regular computing process. Several metrics are involved, and a statistical analysis is performed. The resilience of the adapted MMAS is analyzed and discussed. A broad outline on future research directions is given in connection with new trends concerning the design of resilient systems.

Sharp Bounds for Genetic Drift in Estimation of Distribution Algorithms

Estimation of distribution algorithms (EDAs) are a successful branch of evolutionary algorithms (EAs) that evolve a probabilistic model instead of a population. Analogous to genetic drift in EAs, EDAs also encounter the phenomenon that the random sampling in the model update can move the sampling frequencies to boundary values not justified by the fitness. This can result in a considerable performance loss. This article gives the first tight quantification of this effect for three EDAs and one ant colony optimizer, namely, for the univariate marginal distribution algorithm, the compact genetic algorithm, population-based incremental learning, and the max-min ant system with iteration-best update. Our results allow to choose the parameters of these algorithms in such a way that within a desired runtime, no sampling frequency approaches the boundary values without a clear indication from the objective function.

Desynchronization in distributed Ant Colony Optimization in HPC environment

Metaheuristics have significant computing requirements, in particular Ant Colony Optimization (ACO) processes a population of individuals (agents/ants) roaming in a graph, leaving the pheromone trails and getting inspired by its amount perceived on the edges. If the considered problem instance is large or the time is crucial, one can try to leverage parallel, hybrid or distributed infrastructure, but the algorithm itself must be properly prepared to deal with new possibilities. We have already presented a method for efficient implementation of distributed ACO, in this paper we follow up with introducing planned desynchronization in the pheromone matrix updates in order to further increase the scalability of the proposed system. The proposed modifications allowed the algorithm to scale up to 400 computations nodes without a significant impact on results quality. Efficacy of the algorithm outperforms the standard Max–Min Ant System by 10%.

Ant colony optimization algorithm for total weighted completion time minimization on non-identical batch machines

This paper aims at the problem of scheduling a set of jobs with arbitrary sizes on parallel batch processing machines with arbitrary capacities. The optimization objective is to minimize the total weighted completion time of jobs. After describing the studied problem, we analyze its complexity and present a lower bound of the problem. A heuristic is provided to solve the problem firstly. Then, with the proposed first job selection strategy based on the weights of jobs, two algorithms based on the ant system and the max-min ant system, respectively, are designed to address the problem. Through extensive experiments, the performance of the proposed algorithms is compared with several state-of-the-art algorithms. The comparative results verify effectiveness and efficiency of the proposed algorithms.

Implementing a GPU-based parallel MAX–MIN Ant System

The MAX–MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore’s law, and the availability of graphics processing units (GPUs) capable of conducting general-purpose computations at high speed, has sparked considerable research efforts into the development of GPU-based ACO implementations. In this paper, we discuss a range of novel ideas for improving the GPU-based parallel MMAS implementation, allowing it to better utilize the computing power offered by two subsequent Nvidia GPU architectures. Specifically, based on the weighted reservoir sampling algorithm we propose a novel parallel implementation of the node selection procedure, which is at the heart of the MMAS and other ACO algorithms. We also present a memory-efficient implementation of another key-component – the tabu list structure – which is used in the ACO’s solution construction stage. The proposed implementations, combined with the existing approaches, lead to a total of six MMAS variants, which are evaluated on a set of Traveling Salesman Problem (TSP) instances ranging from 198 to 3795 cities. The results show that our MMAS implementation is competitive with state-of-the-art GPU-based and multi-core CPU-based parallel ACO implementations: in fact, the times obtained for the Nvidia V100 Volta GPU were up to 7.18x and 21.79x smaller, respectively. The fastest of the proposed MMAS variants is able to generate over 1 million candidate solutions per second when solving a 1002-city instance. Moreover, we show that, combined with the 2-opt local search heuristic, the proposed parallel MMAS finds high-quality solutions for the TSP instances with up to 18,512 nodes.

P System Framework for Ant Colony Algorithm in IoT Data Routing

The Internet of Things (IoT) is a critical part of current information technology. When designing IoT data routes, device limited resources such as the computation speed, available amount of memory, remaining battery power or channel bandwidth, to name a few, must be considered. Since the Ant Colony System (ACS) is successfully applied to solving different routing problems, the implementation of ACS for routing in the IoT has also been considered. A P system inspired by the nature of membrane processes not only simplifies a complex system behavior annotation but also delivers a good balance between performance, flexibility and scalability. For this reason, the P system framework for ACS in IoT data routing has been investigated. From the research conducted, MMAPS, which is a combination of the P system and the Max-Min Ant System, is seen to perform better than the ACS.

Optimizing Bidders Selection of Multi-Round Procurement Problem in Software Project Management Using Parallel Max-Min Ant System Algorithm

This paper presents a Game-theoretic optimization via Parallel MinMax Ant System (PMMAS) algorithm is used in practice to determine the Nash equilibrium value to resolve the confusion in choosing appropriate bidders of multi-round procurement problem in software project management. To this end, we introduce an approach that proposes: (i) A Game-theoretic model of multiround procurement problem (ii) A Nash equilibrium strategy corresponds to multi-round strategy bid (iii) An application of PSO for the determination of global Nash equilibrium. The balance point in Nash Equilibrium can help to maintain a sustainable structure not only in terms of project management but also in terms of future cooperation. As an alternative of procuring entities subjectively, a methodology to support decision making has been studied using Nash equilibrium to create a balance point on benefit in procurement where buyers and suppliers need multiple rounds of bidding. Our goal focus on the balance point in Nash Equilibrium to optimizing bidder selection in multi-round procurement which is the most beneficial for both investors and selected tenderers. Our PMMAS algorithm is implemented based on MPI (message passing interface) to find the approximate optimal solution for the question of how to choose bidders and ensure a path for a win-win relationship of all participants in the procurement process. We also evaluate the speedup ratio and parallel efficiency between our algorithm and other proposed algorithms. As the experiment results, the high feasibility and effectiveness of the PMMAS algorithm are verified.