A Modified transmission Algorithm for Resolving Vehicle Routing Problem by Intelligent Water drop Algorithm

A comparison between two technologies ‘Swarm Intelligence’ and ‘Intelligent Water drops’ inorder to overcome the disadvantages of various technologies is an integral concern of this paper . It is nature inspired. IWD algorithm is used to calculate the solutions of the n-queen puzzle with a simple local heuristic. Water of the ocean river easily finds best way from the number of various ways available to reach from its starting to end point. The water drops that flows in rivers has optimal paths that have been obtained by the actions and reactions. With the help of modified IWD algorithm the traveling of salesman problem has also solved. So it is considered as NP-hard Polynomial.NP-hard (Non-deterministic Polynomial-time hard) is a class of problems that are informally,” at least as hard as the hardest problems in NP”. IWD is a fastest algorithm. It provides the minimum distance among the all options. Due to the collaboration of SI and IWD, this algorithm is more efficient. It includes the properties of both SI and IWD. This paper proposes IWD techniques to solve VRP

A Multi-Objective Genetic Algorithm for the Vehicle Routing with Time Windows and Loading Problem

This work presents the Vehicle Routing with Time Windows and Loading Problem (VRTWLP) as a multi-objective optimization problem, implemented within a Genetic Algorithm. Specifically, the three dimensions of the problem to be optimized – the number of vehicles, the total travel distance and volume utilization – are considered to be separated dimensions of a multi-objective space. The quality of the solution obtained using this approach is evaluated and compared with results of other heuristic approaches previously developed by the author. The most significant contribution of this work is our interpretation of VRTWLP as a Multi-objective Optimization Problem

Bus route design with limited travel time

Routing problems are especially important for public transport and especially for urban transport. This research deals with designing a bus route to satisfy the demands of most passengers within a limited total bus travel time. A Simulated Annealing (SA) algorithm is proposed for optimizing the routing design. Before applying the proposed SA algorithm, the Taguchi method is adopted for optimizing the setting of parameters. The experimental results show that the proposed SA algorithm with the optimal parameters setting results in better routes than those designed by other research methods

A Development Framework for Rapid Metaheuristics Hybridization

While meta-heuristics are effective for solving large-scale combinatorial optimization problems, they result from time-consuming trial-and-error algorithm design tailored to specific problems. For this reason, a software tool for rapid prototyping of algorithms would save considerable resources. This paper presents a generic software framework that reduces development time through abstract classes and software reuse, and more importantly, aids design with support of user-defined strategies and hybridization of meta-heuristics. Most interestingly, we propose a novel way of redefining hybridization with the use of the “request and response ” metaphor, which form an abstract concept for hybridization. Different hybridization schemes can now be formed with minimal coding, which gives our proposed Metaheuristics Development Framework its uniqueness. To illustrate the concept, we restrict to two popular metaheuristics Ants Colony Optimization and Tabu Search, and demonstrate MDF through the implementation of various hybridized models to solve the Traveling Salesman Problem. 1

Large neighborhood search for the double traveling salesman problem with multiple stacks

This paper considers a complex real-life short-haul/long haul pickup and delivery application. The problem can be modeled as double traveling salesman problem (TSP) in which the pickups and the deliveries happen in the first and second TSPs respectively. Moreover, the application features multiple stacks in which the items must be stored and the pickups and deliveries must take place in reserve (LIFO) order for each stack. The goal is to minimize the total travel time satisfying these constraints. This paper presents a large neighborhood search (LNS) algorithm which improves the best-known results on 65% of the available instances and is always within 2% of the best-known solutions