Max-Min Ant System Algorithm Research Articles

Swpmmas: an optimized parallel max-min ant system algorithm based on the SW26010-pro processor

Swpmmas: an optimized parallel max-min ant system algorithm based on the SW26010-pro processor

An aerial framework for Multi-View grape bunch detection and route Optimization using ACO

Typically, commercial orchards and vineyards consist of large fields that encounter similar development phases at once. Thus, it becomes necessary to efficiently fly over all fields to detect fruit and identify their status in a very limited timeframe. For this purpose, Unmanned Aerial Vehicles (UAVs) path planning plays a pivotal role in agriculture as it enables optimal coverage of agricultural fields, leading to enhanced data acquisition and improved precision agriculture practices, for instance, disease assessment and pesticide application. In addition, deep learning techniques offer precise image analysis. On the one hand, object detection has been applied to agricultural fields to carry out a wide range of operations, such as detecting apples and predicting yield in vineyards, with higher detection accuracy when the fruits are fully visible. On the other hand, when crops present leaf-occlusion, the algorithms face difficulties and are unable to adapt to the specific characteristics of the field. Therefore, this study seeks to address this issue by developing a novel framework to enhance UAV path planning for data collection in vineyards, considering the current biophysical environment. To this end, the proposed framework requires two flights: i) a first flight (survey) to acquire insights on the crop structure and environment, and ii) a second flight using the Ant Colony Optimization Max-Min Ant System (ACO-MMAS) algorithm to enhance image acquisition by considering multiple angles to overcome partial leaf-occlusion. Further, the optimisation algorithm can potentially boost the acquisition of datasets for fruit detection by considering single and multiple UAVs flying synchronously while ensuring a safe distance between platforms and efficient coverage. The method was tested in two vineyards with different environmental characteristics, increasing levels of difficulty and acquired during two different growing seasons. It improved the length of the computed paths by up to 24%, compared to a base algorithm that considers only the closest point without any optimisation, improving the decision-making processes and resource allocation in crop management.

Finding Robotic Wide Free Space Path in Dynamic Environment by Improving MAX–MIN Ant System Algorithm

— This paper proposes a new obstacle avoidance method for finding an optimal path passing through wide free space based on the optimized MAX-MIN Ant System (MMAS) algorithm in dynamic environments. The proposed improvement of the MMAS algorithm occurs in two stages. The first stage analyses the environment with two modifications by proposing a new relation of pheromone trail updating to construct the consequence modified (deposited) pheromone trail update in each iteration based on adding a new parameter (clean) to calculate the possible wide empty locations. Another alteration at this level involves checking and identifying the four horizontal and vertical states. Suppose the two nodes on either side of the four diagonal nodes are barriers in the next neighbor locations. In that case, the Tight Tunnel state is then attained, and it is managed by sealing this location to prevent passing through it before the robot movement stage. Another alteration is made to the robot-moving stage by recommending a new relation of tour building probabilities to determine the best option for moving the robot from the start node through a dynamic environment that incorporates dynamic obstacles moving through free space by locating and exhibiting the ideal path via broad free space. The main outcome of the experiment simulation of the new method involves that the robot avoids going through narrow tunnels and chooses the best course through open areas to reach its destination without running into any impediments in various dynamic environments conditions. In addition, the comparison of average time occupied to find the paths by the new method reduces to 54.32% as compared with that required with the method of hybrid the basic D* algorithm with the Lbest PSO algorithm, and to 11.95% in comparison to the time occupied recorded when applying the method of the improved MAX–MIN ACO algorithm.

Influence of subproblem solutions on the quality of traveling thief problem solutions

The traveling thief problem (TTP) is a typical combinatorial optimization problem that integrates the computational complexity of the traveling salesman problem (TSP) and the knapsack problem (KP). The interdependent and mutually restrictive relationship between these two sub-problems brings new challenges to the heuristic optimization algorithm for solving the TTP problem. This paper first analyzes the performance of three sub-component combined iterative algorithms: Memetic Algorithm with the Two-stage Local Search (MATLS), S5, and CS2SA algorithms, which all employ the Chained Lin-ighan (CLK) algorithm to generate the circumnavigation path. To investigate the influence of different traveling routes on the performance of TTP solving algorithms, we propose a combinatorial iterative TTP solving algorithm based on the Ant Colony Optimization (ACO) and MAX-MIN Ant System (MMAS). Finally, the experimental investigations suggest that the traveling route generation method dramatically impacts the performance of TTP solving algorithms. The sub-component combined iterative algorithms based on the MMAS algorithm to generate the circumnavigation path has the best practical effect.

A Bilevel Ant Colony Optimization Algorithm for Capacitated Electric Vehicle Routing Problem.

The development of electric vehicle (EV) techniques has led to a new vehicle routing problem (VRP) called the capacitated EV routing problem (CEVRP). Because of the limited number of charging stations and the limited cruising range of EVs, not only the service order of customers but also the recharging schedules of EVs should be considered. However, solving these two aspects of the problem together is very difficult. To address the above issue, we treat CEVRP as a bilevel optimization problem and propose a novel bilevel ant colony optimization algorithm in this article, which divides CEVRP into two levels of subproblem: 1) capacitated VRP and 2) fixed route vehicle charging problem. For the upper level subproblem, the electricity constraint is ignored and an order-first split-second max-min ant system algorithm is designed to generate routes that fulfill the demands of customers. For the lower level subproblem, a new effective heuristic is designed to decide the charging schedule in the generated routes to satisfy the electricity constraint. The objective values of the resultant solutions are used to update the pheromone information for the ant system algorithm in the upper level. Through good orchestration of the two components, the proposed algorithm can significantly outperform state-of-the-art algorithms on a wide range of benchmark instances.

Ant Colony Optimization Algorithms for Selection and Sequencing Problems

This paper is a simple tutorial for researchers interested in ant colony optimization (ACO) in general and max-min ant system (MMAS) in particular. The paper compares the differences in implementing these algorithms to solve sequencing and selection problems. For selection problems, we use the famous knapsack problem (KP) to demonstrate how MMAS can be used, wherease, for the sequencing problem, we use the famous travelling salesman problem (TSP). Results from the literature shows how a MMAS algorithm can outperform other meta-heuristics in solving these two types of problems.

Smooth Path Planning of Mobile Robot Based on Improved Ant Colony Algorithm

Aiming at the problems of slow convergence, easy to fall into local optimum, and poor smoothness of traditional ant colony algorithm in mobile robot path planning, an improved ant colony algorithm based on path smoothing factor was proposed. Firstly, the environment map was constructed based on the grid method, and each grid was marked to make the ant colony move from the initial grid to the target grid for path search. Then, the heuristic information is improved by referring to the direction information of the starting point and the end point and combining with the turning angle. By improving the heuristic information, the direction of the search is increased and the turning angle of the robot is reduced. Finally, the pheromone updating rules were improved, the smoothness of the two-dimensional path was considered, the turning times of the robot were reduced, and a new path evaluation function was introduced to enhance the pheromone differentiation of the effective path. At the same time, the Max-Min Ant System (MMAS) algorithm was used to limit the pheromone concentration to avoid being trapped in the local optimum path. The simulation results show that the improved ant colony algorithm can search the optimal path length and plan a smoother and safer path with fast convergence speed, which effectively solves the global path planning problem of mobile robot.

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.

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.

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.

A Research on Vehicle and UAV Routing Problem during Distribution Based on IAMMAS

According to the characteristic of vehicle and unmanned aerial vehicle (UAV) in distribution service, a vehicle and UAV distribution mode were put forward in this paper to further expand the three-dimensional space of distribution, improve the efficiency of distribution and relieve the pressure of distribution personnel. Based on the consideration of real-time road condition, real-time driving speed, maximum driving distance and mixing time windows in distribution practice, a two-stage nested VRPTW model under vehicle and UAV distribution mode was constructed in this paper. A hybrid algorithm of genetic algorithm and adaptive max-min ant system algorithm (GA-AMMAS) was designed to solve the above problems. In addition, the actual road traffic conditions and vehicle actual travel time were added to the heuristic information in this algorithm. Data experiments shows that the vehicle and UAV routing problem can be effectively solved by GA-AMMAS. In addition, compared with GA and ACA, GA-AMMAS shows obvious advantages in quality solution and speed of convergence.

Interactive machine learning: experimental evidence for the human in the algorithmic loop

Recent advances in automatic machine learning (aML) allow solving problems without any human intervention. However, sometimes a human-in-the-loop can be beneficial in solving computationally hard problems. In this paper we provide new experimental insights on how we can improve computational intelligence by complementing it with human intelligence in an interactive machine learning approach (iML). For this purpose, we used the Ant Colony Optimization (ACO) framework, because this fosters multi-agent approaches with human agents in the loop. We propose unification between the human intelligence and interaction skills and the computational power of an artificial system. The ACO framework is used on a case study solving the Traveling Salesman Problem, because of its many practical implications, e.g. in the medical domain. We used ACO due to the fact that it is one of the best algorithms used in many applied intelligence problems. For the evaluation we used gamification, i.e. we implemented a snake-like game called Traveling Snakesman with the MAX–MIN Ant System (MMAS) in the background. We extended the MMAS–Algorithm in a way, that the human can directly interact and influence the ants. This is done by “traveling” with the snake across the graph. Each time the human travels over an ant, the current pheromone value of the edge is multiplied by 5. This manipulation has an impact on the ant’s behavior (the probability that this edge is taken by the ant increases). The results show that the humans performing one tour through the graphs have a significant impact on the shortest path found by the MMAS. Consequently, our experiment demonstrates that in our case human intelligence can positively influence machine intelligence. To the best of our knowledge this is the first study of this kind.

Multi-type ant system algorithm for the time dependent vehicle routing problem with time windows

The time dependent vehicle routing problem with time windows (TDVRPTW) is considered. A multi-type ant system (MTAS) algorithm hybridized with the ant colony system (ACS) and the max-min ant system (MMAS) algorithms is proposed. This combination absorbs the merits of the two algorithms in solutions construction and optimization separately. In order to improve the efficiency of the insertion procedure, a nearest neighbor selection (NNS) mechanism, an insertion local search procedure and a local optimization procedure are specified in detail. And in order to find a balance between good scouting performance and fast convergence rate, an adaptive pheromone updating strategy is proposed in the MTAS. Computational results confirm the MTAS algorithm's good performance with all these strategies on classic vehicle routing problem with time windows (VRPTW) benchmark instances and the TDVRPTW instances, and some better results especially for the number of vehicles and travel times of the best solutions are obtained in comparison with the previous research.

Scheduling Batch Processing Machine Using Max–Min Ant System Algorithm Improved by a Local Search Method

The problem of minimizing the makespan on single batch processing machine is studied in this paper. Both job sizes and processing time are nonidentical and the processing time of each batch is determined by the job with the longest processing time in the batch. Max–Min Ant System (MMAS) algorithm is developed to solve the problem. A local search method MJE (Multiple Jobs Exchange) is proposed to improve the performance of the algorithm by adjusting jobs between batches. Preliminary experiment is conducted to determine the parameters of MMAS. The performance of the proposed MMAS algorithm is compared with CPLEX as well as several other algorithms including ant cycle (AC) algorithm, genetic algorithm (GA), and two heuristics, First Fit Longest Processing Time (FFLPT) and Best Fit Longest Processing Time (BFLPT), through numerical experiment. The experiment results show that MMAS outperformed others especially for large population size.

MMAS Algorithm for Features Selection Using 1D-DWT for Video-Based Face Recognition in the Online Video Contextual Advertisement User-Oriented System

Face recognition is an importance step which can affect the performance of the system. In this paper, the authors propose a novel Max-Min Ant System algorithm to optimal feature selection based on Discrete Wavelet Transform feature for Video-based face recognition. The length of the culled feature vector is adopted as heuristic information for ant's pheromone in their algorithm. They selected the optimal feature subset in terms of shortest feature length and the best performance of classifier used k-nearest neighbor classifier. The experiments were analyzed on face recognition show that the authors' algorithm can be easily implemented and without any priori information of features. The evaluated performance of their algorithm is better than previous approaches for feature selection.

A max-min ant system for the finance-based scheduling problem

Construction contractors depend on bank overdrafts to finance their expenses; however, these overdrafts cannot exceed an imposed Credit Line (CL). The Finance-Based Scheduling Problem (FBSP) is about scheduling activities without exceeding the CL. In this paper, we provide a more eloquent formulation of the FBSP and list its different variants. Three Max-Min Ant System (MMAS) algorithms, which use different heuristic information when generating solutions, are then developed to solve the FBSP. To test the MMAS algorithms, we generate 60 instances that are used to tune the MMAS algorithms and then use these algorithms to solve the generated instances. The found solutions are compared with the best bounds found using a Branch and Bound (B&B) algorithm. A 0.6% improvement is achieved by the B&B algorithm when compared to the best results found by the MMAS algorithms; moreover, the comparison shows that using the number of successors as heuristic outperformed other heuristics. Furthermore, the MMAS algorithm outperformed other meta-heuristics that use repair operators or penalize infeasible solutions in terms of computation time while having comparable solution values.

Drift analysis of ant colony optimization of stochastic linear pseudo-boolean functions

In this paper we study the behavior of a variant of the Max–Min Ant System algorithm when applied to a stochastic Linear Pseudo-Boolean Optimization problem. Previous related work is on a partial analysis of its performance on a different problem. Here, we carry out its complete performance analysis giving a bound on its average runtime using drift analysis. For the purpose, we give a new drift theorem and use it to analyze the algorithm when applied to our problem.

A new hybrid matheuristic optimization algorithm for solving design and network engineering problems

A hybrid matheuristic algorithm is formulated by combining Grotschel–Holland and Max–Min Ant System algorithms to solve design and network engineering problems. In the majority of our examples, the hybrid algorithm seems promising. The matheuristic has the ability to increase exploration and it maximizes exploitation of various solutions and the certainty of exact methods to find a solution. Further, a recommended study direction is to improve the algorithm to solve higher-order polynomial type problems efficiently.

Container Shipping Network Optimisation Based on Steering Vector Search Pattern

Container shipping is one of the most important transport modes in international trade. For a large-scale container shipping network, an optimisation model is proposed to minimise the total shipping cost of container flow. Considering the directional property of the container shipping process, a steering vector search pattern is incorporated in a Max-Min Ant System (MMAS) solving algorithm, and an orthogonal array method is adopted in parameter settings. In numerical experiments, a traditional ant colony algorithm and an MMAS algorithm based on a steering vector search pattern are used respectively to optimise the network of five node scales. The results show that a steering vector search pattern can speed up the computation process and improve the optimisation effect.

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.