Browsing by Subject "Local search"

Now showing 1 - 5 of 5

Open Access
Integrating social features into mobile local search
(Elsevier Inc., 2016) Kahveci, B.; Altıngövde, İ. S.; Ulusoy, Özgür
As availability of Internet access on mobile devices develops year after year, users have been able to make use of search services while on the go. Location information on these devices has enabled mobile users to use local search services to access various types of location-related information easily. Mobile local search is inherently different from general web search. Namely, it focuses on local businesses and points of interest instead of general web pages, and finds relevant search results by evaluating different ranking features. It also strongly depends on several contextual factors, such as time, weather, location etc. In previous studies, rankings and mobile user context have been investigated with a small set of features. We developed a mobile local search application, Gezinio, and collected a data set of local search queries with novice social features. We also built ranking models to re-rank search results. We reveal that social features can improve performance of the machine-learned ranking models with respect to a baseline that solely ranks the results based on their distance to user. Furthermore, we find out that a feature that is important for ranking results of a certain query category may not be so useful for other categories.
Open Access
A local search heuristic with self-tuning parameter for permutation flow-shop scheduling problem
(IEEE, 2009) Dengiz, B.; Alabaş-Uslu, Ç.; Sabuncuoğlu, İhsan
In this paper, a new local search metaheuristic is proposed for the permutation flow-shop scheduling problem. In general, metaheuristics are widely used to solve this problem due to its NP-completeness. Although these heuristics are quite effective to solve the problem, they suffer from the need to optimize parameters. The proposed heuristic, named STLS, has a single self-tuning parameter which is calculated and updated dynamically based on both the response surface information of the problem field and the performance measure of the method throughout the search process. Especially, application simplicity of the algorithm is attractive for the users. Results of the experimental study show that STLS generates high quality solutions and outperforms the basic tabu search, simulated annealing, and record-to-record travel algorithms which are well-known local search based metaheuristics.
Open Access
A problem space genetic algorithm in multiobjective optimization
(Springer New York LLC, 2003) Türkcan, A.; Aktürk, M. S.
In this study, a problem space genetic algorithm (PSGA) is used to solve bicriteria tool management and scheduling problems simultaneously in flexible manufacturing systems. The PSGA is used to generate approximately efficient solutions minimizing both the manufacturing cost and total weighted tardiness. This is the first implementation of PSGA to solve a multiobjective optimization problem (MOP). In multiobjective search, the key issues are guiding the search towards the global Pareto-optimal set and maintaining diversity. A new fitness assignment method, which is used in PSGA, is proposed to find a well-diversified, uniformly distributed set of solutions that are close to the global Pareto set. The proposed fitness assignment method is a combination of a nondominated sorting based method which is most commonly used in multiobjective optimization literature and aggregation of objectives method which is popular in the operations research literature. The quality of the Pareto-optimal set is evaluated by using the performance measures developed for multiobjective optimization problems.
Open Access
Robust gateway placement in wireless mesh networks
(Elsevier, 2018) Gökbayrak, Kağan
Wireless mesh networks (WMNs) are communication networks that provide wireless Internet access over areas with limited infrastructure. Each node in a WMN serves several clients in its coverage area and transfers their traffic over wireless media to a few gateway nodes that have wired connections to the Internet. In this paper, we consider the Internet gateway placement (IGP) problem along with operational problems such as routing and wireless transmission capacity allocation. To eliminate wireless interference, we adopt the spatial reuse time division multiple access (TDMA) method, in which wireless transmissions are scheduled to occur at different time slots. We also employ destination-based single path routing for its ease of implementation. We present two mixed integer linear programming (MILP) formulations, both of which jointly determine the minimum number of gateway nodes needed to support forecasted demand, the locations of these gateway nodes, the routing trees, and the time slot allocations to wireless links. These formulations differ in the flow constraints. We also present a set of valid inequalities for the formulation with the multi-commodity flow constraints. In most cases, the solution to the IGP problem is not unique. Therefore, we also introduce a local search algorithm to select the most robust solution against any demand forecast errors. On example networks, we compare the proposed formulations with and without the valid inequalities in terms of the exact solution performances and the linear programming (LP) relaxations. We also demonstrate our local search algorithm to improve robustness against forecast errors on these example networks.
Open Access
Star p-hub median problem with modular arc capacities
(Elsevier, 2008) Yaman, H.
We consider the hub location problem, where p hubs are chosen from a given set of nodes, each nonhub node is connected to exactly one hub and each hub is connected to a central hub. Links are installed on the arcs of the resulting network to route the traffic. The aim is to find the hub locations and the connections to minimize the link installation cost. We propose two formulations and a heuristic algorithm to solve this problem. The heuristic is based on Lagrangian relaxation and local search. We present computational results where formulations are compared and the quality of the heuristic solutions are tested.