S search is a population-based method that has recently been shown to yield promising outcomes for solving combinatorial and nonlinear optimization problems. Based on formulations originally proposed in the 1960s for combining decision rules and problem constraints, such as the surrogate constraint method, scatter search uses strategies for combining solution vectors that have proved effective ...

In this paper we develop a heuristic algorithm, based on Scatter Search, for project scheduling problems under partially renewable resources. This new type of resource can be viewed as a generalization of renewable and non-renewable resources, and is very helpful in modelling conditions that do no fit into classical models, but which appear in real timetabling and labor scheduling problems. The...

Scatter search is an evolutionary method that, unlike genetic algorithms, operates on a small set of solutions and makes only limited use of randomization as a proxy for diversification when searching for a globally optimal solution. The scatter search framework is flexible, allowing the development of alternative implementations with varying degrees of sophistication. In this paper, we test th...

In this paper we discuss the application of a metaheuristic approach based on the Scatter Search to deal with robust optimization of the planning problem in the deploying of the Dense Wavelength Division Multiplexing (DWDM) technology on an existing optical fiber network taking into account, in addition to the forecasted demands, the uncertainty in the survivability requirements. Keyword: Netwo...

The problem of finding a global optimum of an unconstrained multimodal function has been the subject of intensive study in recent years, giving rise to valuable advances in solution methods. We examine this problem within the framework of adaptive memory programming (AMP), focusing particularly on AMP strategies that derive from an integration of Scatter Search and Tabu Search. Computational co...

This paper introduces the variable objective search framework for combinatorial optimization. The method utilizes different objective functions used in alternative mathematical programming formulations of the same combinatorial optimization problem in an attempt to improve the solutions obtained using each of these formulations individually. The proposed technique is illustrated using alternati...

In the paper, two evolutionary approaches to the general DNA sequencing problem, assuming both negative and positive errors in the spectrum, are compared. The older of them is based on the idea of genetic approach and is enhanced by a greedy algorithm. The newly proposed algorithm combines the tabu search and the scatter search methods. After conducting experiments with random and coding DNA se...

I this paper, we develop a general-purpose heuristic for permutations problems. The procedure is based on the scatter-search and tabu-search methodologies and treats the objective-function evaluation as a black box, making the search algorithm context-independent. Therefore, our main contribution consists of the development and testing of a procedure that uses no knowledge from the problem cont...

Multi-dimensional Visual Tracking (MVT) problems include visual tracking tasks where the system state is defined by a high number of variables corresponding to multiple model components and/or multiple targets. A MVT problem can be modeled as a dynamic optimization problem. In this context, we propose an algorithm which hybridizes Particle Filters (PF) and the Scatter Search (SS) metaheuristic,...