Variable neighborhood search (VNS) is a recent metaheuristic for solving combinatorial and global optimization problems whose basic idea is systematic change of neighborhood within a local search. In this survey paper we present basic rules of VNS and some of its extensions. Moreover, applications are briefly summarized. They comprise heuristic solution of a variety of optimization problems, wa...

Many discrete optimization problems of practical interest cannot be solved to optimality in the available time. A practical approach to these problems is to use heuristic algorithms, which do not guarantee the optimality of the solution but which, for many problems, can find nearly optimal solutions within a reasonable amount of computational time. The literature on heuristic algorithms often d...

A combinatorial auction is an auction where the bidders have the choice to bid on bundles of items. The Winner Determination Problem (WDP) in combinatorial auctions is the problem of finding winning bids that maximize the auctioneer’s revenue under constraint, where each item can be allocated to at most one bidder. WDP is known as an NP-hard problem with practical applications like electronic c...

Very large-scale neighborhood search (VLSNS) is a technique intensively used in single-objective optimization. However, there is almost no study of VLSNS for multiobjective optimization. We show in this paper that this technique is very efficient for the resolution of multiobjective combinatorial optimization problems. Two problems are considered: the multiobjective multidimensional knapsack pr...

The scheduling problem under study may be viewed as an extension of the standard Multi-mode ResourceConstrained Project Scheduling Problem (MRCPSP) including Multi-Skilled Labor and will be called as MRCPSP-MS. This problem requires an integration of resource limitation, labor skills, and multiple possible execution modes for each task, and the objective is to minimize the overall project durat...

In this paper, we address the approximate nearest neighbor (ANN) search problem over large scale visual descriptors. We investigate a simple but very effective approach, neighborhood graph (NG) search, which conducts the local search by expanding neighborhoods with a best-first manner. Expanding neighborhood makes it efficient to locate the descriptors with high probability being true NNs. Howe...

Local search algorithms, such as simulated annealing, tabu search, and local hill climbers attempt to optimize a solution to a problem by making locally improving modifications to a candidate solution. They rely on a neighborhood operator to restrict the search to a typically small set of possible successor states. The genetic algorithm mutation operator, likewise, enables the exploration of th...

Many optimization problems of practical interest are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic (approximation) algorithms that can find nearly optimal solutions within a reasonable amount of computation time. An improvement algorithm generally starts with a feasible solution and iteratively tries to obtain a better solution. Ne...

Many optimization problems including the network design problem of finding the bounded diameter minimum spanning tree are computationally intractable. Therefore, a practical approach for solving such problems is to employ heuristic algorithms that can find solution close to the optimal one within a reasonable amount of time. Neighborhood search algorithms are a wide class of algorithms where at...