Elitist Ant System Research Articles

A review on Elitist Ant System Algorithm and applications in Flexible Job Scheduling Problem

A review on Elitist Ant System Algorithm and applications in Flexible Job Scheduling Problem

An Effective Combination of Tabu Search, Elite Ant System and Local Search Algorithms for Solving the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

An Effective Combination of Tabu Search, Elite Ant System and Local Search Algorithms for Solving the Heterogeneous Fixed Fleet Open Vehicle Routing Problem with Time Windows

Minimizing total completion time on non-identical parallel batch machines with arbitrary release times using ant colony optimization

This paper considers the problem of scheduling a group of jobs with arbitrary release times, non-identical sizes, and different processing times on non-identical parallel batch processing machines to minimize the total completion time. A mixed-integer programming (MIP) model is firstly constructed in this paper to solve this problem. Then since the studied problem is strongly NP-hard, a modified elite ant system algorithm with the local search (MEASL) is also proposed to solve it, which is compared with several meta-heuristic algorithms and the commercial optimization solver (Gurobi) through extensive simulation experiments. Finally, the experimental results verify the effectiveness of the proposed algorithm.

Determining the Number of Ants in Ant Colony Optimization

The goal of this contribution article is to investigate the effect of the numbers of ants on the Ant Colony Optimization (ACO) metaheuristic's obtained solution while addressing the Traveling Salesman Problem. Within a restricted number of iterations, the purpose was to see how the duration of the calculated tours varied for various numbers of ants. Three well-known ACO algorithms: Elitist Ant System (EAS), Ranked Ant System (RAS), and Min-Max Ant System (MMAS), were developed and tested in this paper. The findings revealed comparable patterns across several test instances. EAS and RAS, both of which use specialized ants, demonstrated that the number of specialists had a significant impact on the duration of solutions. Normal ants, on the other hand, had no effect on the solutions. The response differed somewhat between EAS and MMAS, with a smaller number of ants being more preferred. When working with five specialists and ants, which are the same to the smart cities, however, RAS outperformed by a considerable margin.

The Information Technology for Determining Vehicle Route Based on Ant Colony Algorithms

The article proposes on the information technology for determining vehicle route based on ant colony algorithms. The analysis of specialized software and information technologies for determining vehicle route was carried out. Existing information technologies for determining the route of vehicles are not able to find an accurate solution or require an unreasonably long time to find effective solutions. The relevance of the article is due to the great complexity and dimension of routing problems, as well as the emergence of its new verifications. In this regard, there is a need to develop new information technologies for solving problems of this class. The proposed information technology is based on ant colony algorithms. This information technology allows to reduce the time of searching for solutions in problems of large dimensions, and at the same time improve the quality of the solutions obtained. Information technology for determining the route of the vehicle will be considered in the form of a set of functions. This set of functions are related to each other in a certain way. This set of functions implement techniques, methods and methods that ensure the receipt, storage, processing, transmission and use of information regarding the route of the vehicle. In the paper, we will use the IDEF0. This is required for visualization and further formal representation of the structure and composition of the information technology for determining the route of the vehicle. We will present the Determining Route Information Technology in its entirety: the tuple and the sets. The top-level context diagram and. the upper child diagram top-level context diagram are presented. When determining the route of the vehicle, we will use ant algorithms. The main modifications of the classic ant colony algorithm are presented: Max-Min Ant System, Elitist Ant System, Q-ant system, Rank ant system, Ant Colony System, Fast Ant System, Antabu

3D PathPlanning of RobotFishBased on ImprovedAntColonyAlgorithm

For the following problems:three-dimensional obstacle avoidance path selection when using robot fish to find targets in a complex coral reef geographical environment, an improved ant colony algorithm that can be applied in an environment with multiple complexities is proposed. Combined with the underwater terrain, several maps with different obstacle complexities are established with the three-dimensional environment modeling scheme. Aiming at easy stagnation, slow search speed, and easiness to produce local optimization in the conventional ant colony algorithm, this paper improved the function design and helped the robot fish carry out the early search based on three pieces of heuristic information (the global distance, traffic ability cost, and path consumption cost). Then the path is sorted according to the path length to adjust the pheromone update mode. At the same time, an elite ant system is constructed to adjust the pheromone value of elite ants separately. The simulation results show that convergence speed and searchabilityof the algorithm have been improved to some extent, which proves feasibility of the algorithm.

A Firefly Algorithm and Elite Ant System-Trained Elman Neural Network for MPPT Algorithm of PV Array

This article proposes a novel MPPT algorithm based on the firefly algorithm and elite ant system-trained Elman neural network (FA-EAS-ElmanNN). First, the position of fireflies is randomly initialized by the firefly algorithm (FA), meanwhile the firefly individuals with higher attractiveness degree value are selected as the optimal solution. Second, the extra pheromones are artificially released to boost the positive feedback effect and convergence rate of the elite ant system (EAS). Third, the weight and threshold of the Elman neural network (ElmanNN) are updated by the FA and EAS. Also, the trained ElamnNN is employed to acquire the maximum voltage of the photovoltaic (PV) array. At last, the PID controller and PWM technology are adapted to regulate the switch time of the boost converter. Furthermore, MATLAB/Simulink is adopted to acquire the datasets of irradiance, temperature, and maximum voltage and validate the reliability and superiority of the proposed algorithm under complex atmospheric conditions. The tracking characteristic, response speed, and efficiency of the proposed MPPT algorithm are contrasted with the particle swarm optimization (PSO), ant colony optimization (ACO), ACO-artificial neural network (ACO-ANN), and PSO-RBF neural network (PSO-RBNFNN) algorithm via simulation. The efficiency of the FA-EAS-ElmanNN algorithm is 99.73%, compared with the ACO-ANN, PSO-RBFNN, PSO, and ACO algorithm, which is increased by 0.49%, 0.58%, 1.2% %, and 1.5%, respectively. Additionally, the experimental setup is built to demonstrate the tracking characteristic of the proposed MPPT algorithm.

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.

Adaptive elitist-ant system for solving combinatorial optimization problems

This work investigates the effects achieved by using an adaptive elite pool over a basic population-based approach, such as, the ant system approach, to different combinatorial optimization problems (COPs). Optimization methods are generally considered a good methodology for solving COPs, while population-based approaches are considered better for exploring the search space and local-based approaches are considered better for exploiting the search space. However, some of these approaches are also hybridized in order to balance between exploiting and exploring the search space. Using an adaptive elite pool offers further balance while also adaptively tuning the importance parameter. Taken together, these three processes may improve and enhance the efficiency of any given search. In this work, three COPs (i.e. CVRP, STSP and 0-1 MKP) are used as test domains. In order to evaluate the effectiveness of using an adaptive elite pool, a comparison is made among the original ant system algorithm, the hybrid Elitist-Ant system algorithm, and other approaches drawn from the literature. Experimental results indicate that using an adaptive elite pool enhances the algorithm and is often able to produce better-quality solutions than other approaches. Where the adaptive elitist-ant system approach obtained 10 out of 14 best known results for CVRP, 20 out of 29 optimal solution results for STSP, 9 out of 11 best known results for 0-1 MKP, and generally better than other individuals approaches in term of better, worse, and similar results.

Colourful fused filament fabrication method based on transitioning waste infilling technology with a colour surface model

PurposeFused filament fabrication (FFF) is a common additive manufacturing method that is widely used owing to its low cost, environmental friendliness and safety. Colour models are needed because of their ability to express more information, but high printing quality and efficiency are difficult to achieve with the existing FFF colour printing methods because of the “inertia” of printing. Inertia refers to the feature of the former colour material remaining in the molten cavity when switching colours in colour FFF printing. The purpose of this paper is to propose a new FFF colour printing method to reduce printing material usage and printing time.Design/methodology/approachA new FFF colour printing method that uses transitioning waste to construct the part is proposed. Based on the freedom of the colour surface model’s interior space, the internal fill and support of the print model are generated using the transitioning waste to reduce printing material usage and printing time and to achieve environmentally friendly colour printing. The modified elite ant system (EAS) algorithm is used to construct and optimize this method based on the colour surface model.FindingsA colour printing experiment is performed using a colour-mixing FFF printer platform with a special waste extrusion module. The experimental results show that this method can significantly reduce material and time consumption compared to the commonly used method. The printed part produced by this method also has high surface quality.Originality/valueA new FFF colour printing method that uses transitioning waste to construct the part is proposed. Based on the freedom of the colour surface model’s interior space, the internal fill and support of the print model are generated using the transitioning waste to reduce printing material usage and printing time and to achieve environmentally friendly colour printing. The modified EAS algorithm is used to construct and optimize this method based on the colour surface model. A special waste extrusion module is developed.

Ant Colony Optimisation for Performing Computational Task in Cellular Automata

A method is presented for the design of cellular automata rules by means of ant algorithms.
 In particular, Elitist Ant System and a~modified MAX-MIN Ant System are applied to search for transition functions of 1D cellular automata that are able to calculate squares of given input values.
 It will be shown that the proposed MAX-MIN Ant System can perform significantly better than the standard variant of Elitist Ant System.
 In particular, in the most advanced case study, the ant algorithm showed an ability to design a~complete set of elementary cellular automata rules that fulfil the required square calculations.
 Some selected results will be presented and their features discussed.

ANT INTELLIGENT APPLIED TO SEWER NETWORK DESIGN OPTIMIZATION PROBLEM: USING FOUR DIFFERENT ALGORITHMS

In this paper, four different kinds of the Ant Colony Optimization Algorithm (ACOA) are used to find optimal solution for sewer network design optimization problem proposing two different formulations for each of them. In both proposed formulations, the decision variables of the problem are cover depths of sewer network nodes. In the second formulation, the constrained version of ACOA is used to find optimal cover depths of the sewer network nodes. The constrained version of ACOA is used here to satisfy slope constraints explicitly leading to reduction of search space of the problem. The Ant System, Elitist Ant System, Elitist-Rank Ant System and Max-Min Ant System are used here to solve two benchmark test examples and the results are presented and compared with other available results. The results show the superiority of the Max-Min Ant System over than other ACOAs in which the trade-off between the two contradictory search characteristic of exploration and exploitation is managed better using this algorithm. Furthermore, best results are obtained with second proposed formulation in comparison with other available results in which they are shown the capability of the proposed formulation to find optimal solution for sewer network design optimization. In other words, the second formulation of Max-Min Ant System has been able to produce results 0.3% and 0.15% cheaper than those obtained by first formulation of Max-Min Ant System for the first and second benchmark test examples, respectively. Furthermore, the average solution cost value of second formulation of Max-Min Ant System is reduced 10.6% (8.1%), 0.43% (0.6%) and 0.34% (0.02%) in comparison with second formulation of Ant System, Elitist Ant System and Elitist-Rank Ant System for first (second) benchmark test examples, respectively.

A Combined Metaheuristic Algorithm for the Vehicle Routing Problem and its Open Version

Abstract: The Open Vehicle Routing Problem (OVRP) is one of the most important extensions of the vehicle routing problem (VRP) that has many applications in industrial and service. In the VRP, a set of customers with a specified demand of goods are given and a depot where a fleet of identical capacitated vehicles is located. We are also given the ‘‘traveling costs’’ between the depot and all the customers, and between each pair of customers. In the OVRP against to VRP, vehicles are not required to return to the depot after completing service. Because VRP and OVRP belong to NP-hard Problems, an efficient hybrid elite ant system called EACO is proposed for solving them in the paper. In this algorithm, a modified tabu search (TS), a new state transition rule and a modified pheromone updating rule are used for more improving solutions. These modifications lead that the proposed algorithm does not trapped at the local optimum and discovers different parts of the solution space. Computational results on fourteen standard benchmark instances for VRP and OVRP show that EACO finds the best known solutions for most of the instances and is comparable in terms of solutions quality to the best performing published metaheuristics in the literature.

Adaptive elitist-ant system for medical clustering problem

In general, population based algorithms are superior to local search based algorithms in term of exploration the search space. In any case, the primary downside in different population based algorithms is in exploiting the search space. Recently, a Hybrid Elitist ant system approach is considered as a decent population based algorithm over various optimization problems. Along these lines, the objective of this work is to evaluate the hybrid elitist ant system approach performance to exploit the search space in clustering problem. Six benchmark datasets for medical clustering problem are utilized as a test domain (UCI Machine Learning Repository). Keeping in mind the end goal is to explore the performance of hybrid elitist ant system approach, a comparison performed between the hybrid elitist-ant system and different methodologies in the literature. The result of elitist ant system approach contrasted with different methodologies outcomes exhibits its viability. In any case, the impediment of hybrid elitist ant system approach is in tuning the importance of constraints parameter, where in each dataset needs to tune its own importance for every problem. Subsequently, its props motivate to enhance the hybrid elitist ant system approach by adaptively tuning the importance parameter. Computational outcomes demonstrate that the proposed adaptive elitist ant system approach is competent in delivering higher quality solutions (outcomes) than hybrid elitist ant system approach and different methodologies (outcomes) in the literature in all datasets.

Hybrid Elitist-Ant System for Nurse-Rostering Problem

The diversity and quality of high-quality and diverse-solution external memory of the hybrid Elitist-Ant System is examined in this study. The Elitist-Ant System incorporates an external memory for preserving search diversity while exploiting the solution space. Using this procedure, the effectiveness and efficiency of the search may be guaranteed which could consequently improve the performance of the algorithm and it could be well generalized across diverse problems of combinatorial optimization. The generality of this algorithm through its consistency and efficiency is tested using a Nurse-Rostering Problem. The outcomes demonstrate the competitiveness of the hybrid Elitist-Ant System’s performance within numerous datasets as opposed to those by other systems. The effectiveness of the external memory usage in search diversification is evidenced in this work. Subsequently, such usage improves the performance of the hybrid Elitist-Ant System over diverse datasets and problems.

Path planning of modular robots on various terrains using Q-learning versus optimization algorithms

Self-reconfigurable modular robots (SRMRs) have recently attracted considerable attention because of their numerous potential applications in the real world. In this paper, we draw a comprehensive comparison among five different algorithms in path planning of a novel SRMR system called ACMoD through an environment comprised of various terrains in a static condition. The contribution of this work is that the reconfiguration ability of ACMoD has been taken into account. This consideration, though raises new algorithmic challenges, equips the robot with new capability to pass difficult terrains rather than bypassing them, and consequently the robot can achieve better performance in terms of traversal time and energy consumption. In this work, four different optimization algorithms, including Adaptive Genetic Algorithm, Elitist Ant System, Dijkstra and Dynamic Weighting A*, along with a well-known reinforcement learning algorithm called Q-Learning, are proposed to solve this path planning problem. The outputs of these algorithms are the optimal path through the environment and the associated configuration on each segment of the path. The challenges involved in mapping the path planning problem to each algorithm are discussed in full details. Eventually, all algorithms are compared in terms of the quality of their solutions and convergence rate.

Hybrid elitist-ant system for a symmetric traveling salesman problem: case of Jordan

This work investigates the performance of a hybrid population-based meta-heuristic with an external memory structure of a hybrid elitist-ant system (elitist-AS). This memory is known as an elite pool, which contains high quality and diverse solutions to maintain a balance between diversity and quality of the search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithm across different instances. A very well known and intensively studied NP-hard optimization problem has been selected to test the performance of the hybrid elitist-AS via its consistency, effectiveness and efficiency. This famous problem is the symmetric traveling salesman problem. The elitist-AS is a class of ant colony optimization techniques which are known to be outstanding for the traveling salesman problem where they have the ability to find the shortest tours guided by the heuristic and the pheromone trail information. An iterated local search is combined with elitist-AS to intensify the search around elite solution and maintains the solution’s exploitation mechanism. Experimental results showed that the performance, compared to the best known results, is optimal for many instances. This finding indicates the effectiveness, efficiency and consistency in diversifying the search while intensifying high-quality solutions. This outstanding performance is due to the utilization of an elite pool along with diversification and intensification mechanisms. In addition, this work proposes two instances that consist of 26 Jordanian cities and 1094 Jordanian locations which have been generated based on coordinates and distances similar to the format of the selected symmetric traveling salesman problem. This step is meant to contribute to finding a solution for a real-world problem and further test the performance of the hybrid elitist-AS.

The effect of elite pool in hybrid population-based meta-heuristics for solving combinatorial optimization problems

This work investigates the effect of elite pool that has high-quality and diverse solutions in three hybrid population-based meta-heuristics with an elite pool of a hybrid Elitist-Ant System, a hybrid Big Bang-Big Crunch optimization, and a hybrid scatter search. The purpose of incorporating an elite pool in population-based meta-heuristics is to maintain the diversity of the search while exploiting the solution space as in the reference set of the scatter search. This may guarantee the effectiveness and efficiency of the search, which could enhance the performance of the algorithms and generalized well across different datasets. To test the generality of these meta-heuristics via their consistency and efficiency, we use three classes of well-known combinatorial optimization problems as follows: symmetric traveling salesman problem, 0–1 multidimensional knapsack problem, and capacitated vehicle routing problem. Experimental results showed that the performance of our hybrid population-based meta-heuristics, compared to the best known results, is competitive in many instances. This finding indicates the effectiveness of utilizing an elite pool in our hybrid meta-heuristics in diversifying the search and subsequently enhances their performance over different instances and problems.

Survey on Project Management System using Event based Scheduler and Ant Colony Optimization

Resource allocation and tasks assignment to software development teams are very crucial and arduous activities that can affect a project's cost and completion time. Solution for such problem is NP-Hard and requires software managers to be supported with efficient tools that can perform such allocation and can resolve the software development project scheduling problem (SDPSP) more efficiently. Ant colony optimization (ACO) is a rapidly evolving meta-heuristic technique based on the real life behavior of ants and can be used to solve NP-Hard (SDPSP) problem. Different versions of ACO meta-heuristic have already been applied to the software project scheduling problem in the past that took various resources into account. We have applied elitist strategy of ACO (elitist ant system) for solving SDPSP in a parameter-constrained environment taking project's cost and duration into consideration. The objective of the ACOSDPSP methodology allows software project managers and schedulers to assign most effective set of employees that can contribute in minimizing cost and duration of the software project. Experimental results show that the proposed ACOSDPSP methodology is promising in achieving the desired results. General Terms Simulation,Data dictionary/directory ,Data warehouse and repository,Logging recovery,Security,integrity and protection

A Modified Column Generation to Solve the Heterogeneous Fixed Fleet Open Vehicle Routing Problem

In the heterogeneous fixed fleet open vehicle routing problem (HFFOVRP), several different types of vehicles can be used to service the customers. The types of vehicles are different in terms of capacity, fixed cost, and variable cost. In this problem, the vehicles are not required to return to the depot after completing a service and the number of vehicles of each type is fixed and limited. Since this problem belongs to NP-hard problems, in this paper a compound heuristic algorithm called SISEC which includes sweep algorithm, insert, swap, and 2-opt moves, modified elite ant system (EAS), and column generation (CG) is applied to solve the HFFOVRP. We report computational results on 22 problems and solve each problem by using our SISEC. The results which were compared to the results of exact algorithms and the classic CG confirm that the proposed algorithm produces high quality solutions within an acceptable computation time.