Exact mathematical programming techniques such as branch-andbound or dynamic programming and stochastic local search techniques have traditionally been seen as being two general but distinct approaches for the effective solution of combinatorial optimization problems, each having particular advantages and disadvantages. In several research efforts true hybrid algorithms, which exploit ideas fro...

We introduce the dynamic SAT problem, a generalisation of the satisfiability problem in propositional logic which allows changes of a problem over time. DynSAT can be seen as a particular form of a dynamic CSP, but considering results and recent success in solving conventional SAT problems, we believe that the conceptual simplicity of SAT will allow us to more easily devise and investigate high...

In continuous black-box optimization, various stochastic local search techniques are often employed, with various remedies for fighting the premature convergence. This paper surveys recent developments in the field (the most important from the author’s perspective), analyzes the differences and similarities and proposes a taxonomy of these methods. Based on this taxonomy, a variety of novel, pr...

Recent times have seen the development of planners that exploit advances in SAT(isfiability) solving technology to achieve good performance. In that spirit we develop the approximate contingent planner PSLSPLAN. Our approach is based on local search for solving stochastic SAT (SSAT) representations of probabilistic planning problems. PSLSPLAN first constructs an SSAT representation of the ntime...

Facility layout is one of the most important Operations Management problems due to its direct impact on the financial performance of both private and public firms. Facility layout problem (FLP) with stochastic parameters, unequal area facilities, and grid system modeling is named GSUA-STFLP. This problem has not been worked in the literature so that to solve GSUA-STFLP is our main contribution....

augmented downhill simplex method (adsm) is introduced here, that is a heuristic combination of downhill simplex method (dsm) with random search algorithm. in fact, dsm is an interpretable nonlinear local optimization method. however, it is a local exploitation algorithm; so, it can be trapped in a local minimum. in contrast, random search is a global exploration, but less efficient. here, rand...

In this paper we present Stochastic Tree-based Local Search or STLS, a local search algorithm combining the notion of cycle-cutsets with the well-known Belief Propagation to approximate the optimum of sums of unary and binary potentials. This is done by the previously unexplored concept of traversal from one cutset to another and updating the induced forest, thus creating a local search algorit...