Hybridised ant colony optimisation for convoy movement problem

This paper compares the performance of the classic genetic algorithm with the more recently proposed ant colony optimisation for solving real-world pickup and delivery problems with hard time windows. A real-world problem that is present worldwide - waste collection - is used to evaluate the algorithms. As in most real-world waste collection problems, many of the waste bins have time windows. The time windows stem from such things as safety regulations and customer agreements, and must be strictly adhered to. The optimisation showed that the genetic algorithm is better than the ant colony optimisation when utilising standard implementations of both algorithms. However, when the algorithms are enhanced with a local search procedure, the ant colony optimisation immediately becomes superior and achieves improved results. Local search seems to be a drawback for the genetic algorithm when hard time windows are involved. Various implementations of the local search procedure are evaluated in this paper using five different test sets. Recommendations for future implementations are given as well as additional enhancements which could improve the performance of the ant colony optimisation.

The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.

Ant colony optimization (ACO) is a new heuristic algorithm developed by simulating ant foraging on the basis of group cooperative learning. TSP and other combinatorial optimization problems have been successfully solved. Like other heuristic search algorithms, ant colony algorithm has the disadvantage of being easily limited to local optimum. Aiming at the vehicle routing problem based on time window, the upper and lower limits of pheromone trajectory intensity are determined by analyzing the ant colony algorithm, and the transmission probability and pheromone updating method are improved to improve the convergence speed and global search ability of the algorithm. Aiming at the vehicle routing problem with time windows in logistics distribution, an improved maximum and minimum ant colony algorithm is proposed to improve the optimization performance. The algorithm can be extended to such related path optimization problems and applied.

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved. It helps to avoid local optimums and to find faster the global one. The theory of intuitionistic fuzzy logic, which is the basis of InterCriteria analysis (ICrA), is used to study the proposed hybrid algorithms for ant colony optimization (ACO). Different algorithms for ICrA implementation are applied on the results obtained by hybrid ACO algorithms for Multiple Knapsack Problem. The hybrid algorithms behavior is compared to the traditional ACO algorithm. Based on the obtained numerical results from the algorithms performance and from the ICrA the efficiency and effectiveness of the proposed hybrid ACO algorithms, combined with appropriate local search procedure, are confirmed.KeywordsLocal searchAnt colony optimizationHybrid algorithmIntercriteria analysis algorithmsKnapsack problemIndex matrixIntuitionistic fuzzy sets

In the last years, there has been a large growth in the research of computational techniques inspired in nature. This area, named Bioinspired Computing, has provided biologically motivated solutions for several real world problems. Among Bioinspired Computing techniques, one can mention Artificial Neural Networks (ANN), Evolutionary Algorithms (EA), Artificial Immune Systems (AIS) and Ant Colony Optimization (ACO). ACO is a meta-heuristic based on the structure and behavior of ant colonies. It has been successfully applied to several optimization problems. Several real world optimization problems may change the configuration of its search space with time. These problems are known as dynamic optimization problems. This chapter presents the main concepts of ACO and show how it can be applied to solve real optimization problems on dynamic environments. As a case study, it will be illustrated how ACO can be applied to process scheduling problems. The chapter starts describing how real ants forage for food, environmental modifications related to this activity (for example, the blockage of a path) and the effects of these modifications in the colony. After, a brief review of previous works on Ant Colony Optimization is presented. For computer simulations, the activities of an ant colony may be modeled as a graph. After modeling the problem of foraging for food (as a graph), the goal (finding the shortest path at a given moment) is defined and the mechanism to solve the problem is presented. The need to model an on-line problem as a graph, the goal of finding the shortest path and the mechanisms adopted for solving the problem (an adapted version of the AntSystem) are detailed. Before ACO is applied to the process scheduling problem, the problem is analyzed and modeled using a graph representation. Next, simulation results obtained in a set of experiments are presented, which are validated by results obtained in a real implementation. Important issues related with the use of ACO for process scheduling, like parameter adjustments, are discussed. In the conclusion of this chapter, we point out a few future directions for ACO researches. The use of computational techniques inspired in nature has become very frequent in the last years. This area, named Bioinspired Computing, provides efficient biologically motivated

We present two hybrid Metaheuristics, a hybrid Iterated Local Search and a hybrid Simulated Annealing, for solving real-world extensions of the Vehicle Routing Problem with Time Windows. Both hybrid Metaheuristics are based on the same neighborhood generating operators and local search procedures. The initial solutions are obtained by the Coefficient Weighted Distance Time Heuristics with automated parameter tuning. The strategies are compared in an empirical study on four real-world problems. A performance measure is used that also considers multiple restarts of the algorithms.

Inverse transient radiation analysis in one-dimensional participating slab using improved Ant Colony Optimization algorithms

The aim of this study to demonstrate that the previously developed ANGEL algorithm can be efficiently used for multiple constrained sizing-shaping truss optimization problems. The applied hybrid method ANGEL, which was originally developed for simple truss optimization problems combines ant colony optimization (ACO), genetic algorithm (GA), and local search strategy (LS). ACO and GA search alternately and cooperatively in the solution space. In ANGEL, the traditional stochastic mutation operator is replaced by the local search procedure as a deterministic counterpart of the stochastic mutation. The feasibility is measured by the maximal load intensity factor computed by a third order path-following method. The powerful LS algorithm, which is based on the local linearization of the set of the constraints and the objective function, is applied to yield a better feasible or less unfeasible solution when ACO or GA obtains a solution. In order to demonstrate the efficiency of ANGEL in the given application area, a well-known example is presented under multiple constraints.

Scheduling for a job shop production system is an integral aspect of production management. Scheduling operations must minimize stock, waste, and idle time and ensure on-time delivery of goods in a time window problem. In this study, due date is considered as an interval instead of a time point. This study addresses scheduling with a time window of job shop scheduling problem (JSP) and yields a solution that is close to the time window. The total penalty due to earliness and tardiness is minimized. As the problem is NP-hard, a mathematical model of the JSP with a time window is initially constructed, and data are then simulated. Solutions are obtained by ant colony optimization (ACO) programs written in C-language and are compared with the best solution obtained using LINGO 7.0 to determine the efficiency and robustness. Test results indicate that ACO is extremely efficient. Solution time using ACO is less than that using LINGO. Hence, ACO is both effective and efficient, which are two qualities stressed in business management.

Ant Colony Optimization (ACO) algorithms are a new branch of swarm intelligence. They have been applied to solve different combinatorial optimization problems successfully. Their performance is very promising when they solve small problem instances. However, the algorithms' time complexity increase and solution quality decrease for large problem instances. So, it is crucial to reduce the time requirement and at the same time to increase the solution quality for solving large combinatorial optimization problems by the ACO algorithms. This paper introduces a Local Search based ACO algorithm (LSACO), a new algorithm to solve large combinatorial optimization problems. The basis of LSACO is to apply an adaptive local search method to improve the solution quality. This local search automatically determines the number of edges to exchange during the execution of the algorithm. LSACO also applies pheromone updating rule and constructs solutions in a new way so as to decrease the convergence time. The performance of LSACO has been evaluated on a number of benchmark combinatorial optimization problems and results are compared with several existing ACO algorithms. Experimental results show that LSACO is able to produce good quality solutions with a higher rate of convergence for most of the problems.

Network Optimization of RPTB Express based on Ant Colony Algorithm

Convoy movement problem (CMP) involves the routing and scheduling of military convoys during war time, peacekeeping, or related operations. Although this study focuses on wartime CMP, the underlying methodologies have potential relevance for peacetime logistics and civilian emergency mobility planning. Wartime convoy routing is subject to stringent strategic constraints such as no road crossing, no halts enroute, and maintaining minimum headway distances. Existing literature faces challenges related to scalability, computational complexity, and limited use of search heuristics, highlighting the need for faster and reliable solution approaches. This study proposes 2 methodologies: (1) a heuristic based on the breadth first search [BFS] principal principle and (2) a Simulated Annealing [SA] meta-heuristic algorithm. Two categories of instances [CAT1 and CAT2] are generated using realistic parameters derived from minimum and maximum arc densities. The methodologies are tested on 70 hypothetical cases across both categories, and results are analyzed. Both approaches solve the problem within reasonable computation times. On average, the BFS heuristics yields solutions 6.20% from optimal for CAT1 and 19.43% for CAT2. SA What forms comparatively better, with deviations of 3.86% for CAT1 and 5.78% for CAT2. Overall, the findings indicate that BFS and SA each provide strong performance for specific classes of CMP instances when compared with optimal solutions obtained through conventional global optimization techniques.

A complete and efficient algorithm is proposed for searching form-closure grasps of n hard fingers on the surface of a three-dimensional object represented by discrete points. Both frictional and frictionless cases are considered. This algorithm starts to search a form-closure grasp from a randomly selected grasp using an efficient local search procedure until encountering a local minimum. The local search procedure employs the powerful ray-shooting technique to search in the direction of reducing the distance between the convex hull corresponding to the grasp and the origin of the wrench space. When the distance reaches a local minimum in the local search procedure, the algorithm decomposes the problem into a few subproblems in subsets of the points according to the existence conditions of form-closure grasps. A search tree whose root represents the original problem is employed to perform the searching process. The subproblems are represented as children of the root node and the same procedure is recursively applied to the children. It is proved that the search tree generates O(KlnK/n) nodes in case a from-closure grasp exists, where K is the number of the local minimum points of the distance in the grasp space and n is the number of fingers. Compared to the exhaustive search, this algorithm is more efficient, and, compared to other heuristic algorithms, the proposed algorithm is complete in the discrete domain. The efficiency of this algorithm is demonstrated by numerical examples.

In order to solve the optimization of new energy bus line network, a new energy bus line network based on ant colony algorithm and low-carbon concept is proposed. Firstly, the model of public transport network is established combined with OD matrix, and the improved ant colony algorithm is used to iteratively optimize the initial suboptimal route set, optimize the objective function, and finally obtain the optimal public transport route set. Secondly, the improved ant colony algorithm based on simulated annealing solves the design of public transport network, and the optimization scheme greatly reduces the number of passenger transfers and the total travel time of passengers. Finally, simulated annealing algorithm, basic ant colony algorithm, and simulated annealing improved ant colony algorithm are used to optimize the objective function. It is proved that the improved ant colony algorithm of simulated annealing can find the optimal solution of the three algorithms when solving the problem of public transport network design, which is better than the solution of basic ant colony and simulated annealing algorithm. The solution efficiency is 10.6 times that of simulated annealing algorithm and 3.5 times that of basic ant colony algorithm.

Based on the mobile robot path planning problem, on the basis of the improved grid method, this paper proposes an improved ant colony algorithm, the particle swarm optimization algorithm can be incorporated into the ant colony algorithm. Firstly, using the particle swarm optimization algorithm to search for global path roughly. At the same time of search for dynamic pheromone intelligent distribution, to get ant colony algorithm's initial pheromone distribution and according to the pheromone content to eliminate some poor path. Secondly, using ant colony algorithm for the optimal path, it was gotten by particle swarm optimization for the secondary search. Finally, get the optimal solution of path search. The simulation results show that, the fusion algorithm does better than the ant colony algorithm in terms of path optimization.