We contribute to the theoretical understanding of evolutionary algorithms and carry out a parameterized analysis of evolutionary algorithms for the Euclidean traveling salesperson problem (Euclidean TSP). We exploit structural properties related to the optimization process of evolutionary algorithms for this problem and use them to bound the runtime of evolutionary algorithms. Our analysis stud...

In this paper, we propose an effective local search algorithm based on variable depth search (VDS) for the MCP. The VDS has been first successfully applied by Lin and Kernighan to the traveling salesman problem [5] and the graph partitioning problem [4]. Their algorithms are often called k-opt local search. The basic concept of the k-opt local search based on VDS is to search a portion of large...

We generalize the standard vehicle routing problem with time windows by allowing both traveling times and traveling costs to be time-dependent functions. In our algorithm, we use a local search to determine routes of the vehicles. When we evaluate a neighborhood solution, we must compute an optimal time schedule of each route. We show that this subproblem can be efficiently solved by dynamic pr...

The Traveling Salesman Problem (TSP) is a famous NP-hard problem typically solved using various heuristics. One of popular heuristics class is k-opt local search. Though these heuristics are quite simple, combined with other techniques in an iterated local search (ILS) framework they show promising results. In this paper, we propose to combine the 2-opt, 3-opt and 4-opt local search algorithms ...

For the problem of indeterminate direction of local search, lacking of efficient regulation mechanism between local search and global search and regenerating new antibodies randomly in the original optimization version of artificial immune network (opt-aiNet), this paper puts forward a novel predication based immune network (PiNet) to solve multimodal function optimization more efficiently, acc...

the multiple traveling salesman problem (mtsp) involves scheduling m > 1 salesmen to visit a set of n > m nodes so that each node is visited exactly once. the objective is to minimize the total distance traveled by all the salesmen. the mtsp is an example of combinatorial optimization problems, and has a multiplicity of applications, mostly in the areas of routing and scheduling. in this paper,...

The travelling salesman problem (TSP) is perhaps the most researched in field of Computer Science and Operations. It a known NP-hard has significant practical applications variety areas, such as logistics, planning, scheduling. Route optimisation not only improves overall profitability logistic centre but also reduces greenhouse gas emissions by minimising distance travelled. In this article, w...

The solution space of the travelling salesman problem under 2-opt moves has been characterized as having a big-valley structure, in which the evaluation of a tour is positively correlated to the distance of the tour from the global optimum. We examine the big-valley hypothesis more closely and show that while the big-valley structure does appear in much of the solution space, it breaks down aro...

This application solves the quadratic assignment problem (QAP) [1]. In QAP, we are given l locations and l facilities and the task is to assign the facilities to the locations to minimize the cost. We chose QAP for the following reasons: First, problem sizes of QAPs in real life problems are relatively small compared with other problems in permutation domains such as the traveling salesman prob...

This paper employs the Memetic algorithm (MA) to optimize the urban transit network. Aiming at the optimal route configuration and service frequency for the urban transit network, the objective function of the proposed mathematical model is to minimize the passenger (user) cost and to reduce the unsatisfied passenger demand at most. MA is one of the recent growing evolutionary computation algor...