optimization through simulation is considered anefficient tool in dealing with optimization problems. it’s a tool that iscapable to cover real world problems to a much more complete way than the otheroptimization tools. the problem of this tool is the necessity of heavyprocessing computations. this problem is caused not only since the simulationprocess is time consuming, but the objective funct...

Krentel [J. Comput. System. Sci., 36, pp.490–509] presented a framework for an NP optimization problem that searches an optimal value among exponentially-many outcomes of polynomialtime computations. This paper expands his framework to a quantum optimization problem using polynomial-time quantum computations and introduces the notion of an “universal” quantum optimization problem similar to a c...

We consider optimization problems that are formulated and solved in the framework of tropical mathematics. The problems consist in minimizing or maximizing functionals defined on vectors of finite-dimensional semimodules over idempotent semifields, and may involve constraints in the form of linear equations and inequalities. The objective function can be either a linear function or nonlinear fu...

We present some properties of explicitly quasiconvex functions, which play an important role in both multicriteria and scalar optimization. Some of these results were recently obtained together with Ovidiu Bagdasar during a research visit at the University of Derby (UK), within the project

This paper is devoted to the analysis of multiphase shape optimization problems, which can formally be written as min { g ( (F1(Ω1), . . . , Fh(Ωh) ) +m ∣∣ h ⋃ i=1 Ωi ∣∣ : Ωi ⊂ D, Ωi ∩ Ωj = ∅}, where D ⊆ R is a given bounded open set, |Ωi| is the Lebesgue measure of Ωi and m is a positive constant. For a large class of such functionals, we analyse qualitative properties of the cells and the int...

the cuckoo search algorithm is a recently developedmeta-heuristic optimization algorithm, which is suitable forsolving optimization problems. to enhance the accuracy andconvergence rate of this algorithm, an improved cuckoo searchalgorithm is proposed in this paper. normally, the parametersof the cuckoo search are kept constant. this may lead todecreasing the efficiency of the algorithm. to cop...

Population-based meta-heuristics are algorithms that can obtain very good results for complex continuous optimization problems in a reduced amount of time. These search algorithms use a population of solutions to maintain an acceptable diversity level during the process, thus their correct distribution is crucial for the search. This paper introduces a new population meta-heuristic called ‘‘var...

Bilevel programming problems are hierarchical optimization problems in which the feasible set is determined by the set of optimal solutions of a second, parametric optimization problem. In this paper we consider problems where this second problem is a discrete one. We start with addressing the problem of the existence of optimal solutions. Then, an algorithm is proposed solving these problems. ...

We show how to use graphics hardware for tackling optimization problems arising in the field of computational geometry. We exemplarily discuss three problems, where combinatorial algorithms are inefficient or hard to implement. Given a set S of n point in the plane, the first two problems are to determine the smallest homothetic scaling of a star shaped polygon P enclosing S and to find the lar...

We consider two optimization problems with geometric structures The rst one con cerns the following minimization problem termed as the rectilinear polygon cover problem Cover certain features of a given rectilinear polygon possibly with rectilinear holes with the minimum number of rectangles included in the polygon Depending upon whether one wants to cover the interior boundary or corners of th...