The ordered clustered travelling salesman problem is a variation of the usual travelling salesman problem in which a set of vertices (except the starting vertex) of the network is divided into some prespecified clusters. The objective is to find the least cost Hamiltonian tour in which vertices of any cluster are visited contiguously and the clusters are visited in the prespecified order. The p...

The traveling salesman problem (TSP) is one of the most important problems in combinatorial optimization: Given a complete graph with edge weights, the goal is to find a Hamiltonian cycle (also called a tour) of minimum weight. 2-opt is probably the most widely used local search heuristic for the TSP. It incrementally improves an initial tour by exchanging two edges of the tour with two other e...

The Travelling Salesman Problem (TSP) is one of the most studied combinatorial optimization problem which is significant in many practical applications in transportation field. The TSP is an NP-hard problem and requires large computational power to be optimally solved by exact algorithms. In the past few years, fast development of generalpurpose Graphics Processing Units (GPUs) has brought huge...

The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...

In air transportation networks the robustness of a network regarding node and link failures is a key factor for its design. At the same time, travelling passengers usually prefer the itinerary with fewer legs. In this paper the authors propose three design methods, which can maximize the air transportation network robustness and make sure every itinerary within this network have fewer legs than...

The dynamic traveling salesman problem (DTSP) is significant in logistics distribution real-world applications smart cities, but it uncertain and difficult to solve. This paper proposes a scheme library-based ant colony optimization (ACO) with two-optimization (2-opt) strategy solve the DTSP efficiently. work novel contributes three aspects: model, framework, algorithm design. Firstly, traditio...

Optimal results for the Traveling Salesrep Problem have been reported on problems with up to 3038 cities using a GA with Edge Assembly Crossover (EAX). This paper rst attempts to independently replicate these results on Padberg's 532 city problem. We then evaluate the performance contribution of the various algorithm components. The incorporation of 2-opt into the EAX GA is also explored. Final...

The 2-Opt heuristic is a simple, easy-to-implement local search heuristic for the traveling salesman problem. While it usually provides good approximations to the optimal tour in experiments, its worst-case performance is poor. In an attempt to explain the approximation performance of 2-Opt, we prove an upper bound of exp(O( √ log(1/σ))) for the smoothed approximation ratio of 2-Opt. As a lower...

Although tabu search is one of the most effective meta-heuristics for solving the job-shop scheduling problem (JSP), very little is known about why this approach works so well and under what conditions it excels. Our goal is to develop models of tabu search algorithms for the JSP that answer these and other related research questions. We previously demonstrated that the mean distance between ra...

Abstract. The development of autonomous Unmanned Aerial Vehicles (UAVs) is a priority to many civilian and military organizations. An essential aspect UAV autonomy the ability for automatic trajectory planning. In this paper, we use parallel Flower Pollination Algorithm (FPA) deal with problem's complexity compute feasible quasi-optimal trajectories fixed-wing UAVs in complex 3D environments, t...