A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Copyright @ Springer-Verlag 2010.Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02

Development of Novel Task-Based Configuration Optimization Methodologies for Modular and Reconfigurable Robots Using Multi-Solution Inverse Kinematic Algorithms

Modular and Reconfigurable Robots (MRRs) are those designed to address the increasing demand for flexible and versatile manipulators in manufacturing facilities. The term, modularity, indicates that they are constructed by using a limited number of interchangeable standardized modules which can be assembled in different kinematic configurations. Thereby, a wide variety of specialized robots can be built from a set of standard components. The term, reconfigurability, implies that the robots can be disassembled and rearranged to accommodate different products or tasks rather than being replaced. A set of MRR modules may consist of joints, links, and end-effectors. Different kinematic configurations are achieved by using different joint, link, and end-effector modules and by changing their relative orientation. The number of distinct kinematic configurations, attainable by a set of modules, varies with respect to the size of the module set from several tens to several thousands. Although determining the most suitable configuration for a specific task from a predefined set of modules is a highly nonlinear optimization problem in a hybrid continuous and discrete search space, a solution to this problem is crucial to effectively utilize MRRs in manufacturing facilities. The objective of this thesis is to develop novel optimization methods that can effectively search the Kinematic Configuration (KC) space to identify the most suitable manipulator for any given task. In specific terms, the goal is to develop and synthesize fast and efficient algorithms for a Task-Based Configuration Optimization (TBCO) from a given set of constraints and optimization criteria. To achieve such efficiency, a TBCO solver, based on Memetic Algorithms (MA), is proposed. MAs are hybrids of Genetic Algorithms (GAs) and local search algorithms. MAs benefit from the exploration abilities of GAs and the exploitation abilities of local search methods simultaneously. Consequently, MAs can significantly enhance the search efficiency of a wide range of optimization problems, including the TBCO. To achieve more optimal solutions, the proposed TBCO utilizes all the solutions of the Inverse Kinematics(IK) problem. Another objective is to develop a method for incorporating the multiple solutions of the IK problem in a trajectory optimization framework. The output of the proposed trajectory optimization method consists of a sequence of desired tasks and a single IK solution to reach each task point. Moreover, the total cost of the optimized trajectory is utilized in the TBCO as a performance measure, providing a means to identify kinematic configurations with more efficient optimized trajectories. The final objective is to develop novel IK solvers which are both general and complete. Generality means that the solvers are applicable to all the kinematic configurations which can be assembled from the available module inventory. Completeness entails the algorithm can obtain all the possible IK solutions

Combinatorial optimization and metaheuristics

Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (“efficient”) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find “quickly” (reasonable run-times), with “high” probability, provable “good” solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

An Effective Ensemble Framework for Multi-Objective Optimization

This work was supported by the National Natural Science Foundation of China under Grants 61876110, 61876163, and 61836005, a grant from ANR/RGC Joint Research Scheme sponsored by the Research Grants Council of the Hong Kong Special Administrative Region, China and France National Research Agency (Project No. A-CityU101/16), the Joint Funds of the National Natural Science Foundation of China under Key Program Grant U1713212, and CONACyT grant no. 221551.Peer reviewedPostprin