Stochastic local search (SLS) algorithms are among the most powerful techniques for tackling computationally hard problems in various areas of computing science, operations research, business administration, and engineering. SLS range from simple constructive iterative improvement to general-purpose methods, also known as metaheuristics. In recent years, it has become evident that development i...

In this paper, we address the problem of local search for the falsification of hybrid automata with affine dynamics. Namely, if we are given a sequence of locations and a maximum simulation time, we return the trajectory that comes the closest to the unsafe set. In order to solve this problem, we formulate it as a differentiable optimization problem which we solve using Sequential Quadratic Pro...

The 0-1 Multidimensional Knapsack Problem (MKP) is a widely-studied problem in combinatorial optimization domaine which has been proven as NP-hard. Various approximate heuristics have been developed and applied effectively to this problem, such as local search and evolutionary methods. This paper proposes the Stochastic Local Search-Simulated Annealing (SLSA) approach that combines the stochast...

Incremental satisfiability problem (ISAT) is considered as a generalisation of the Boolean satisfiability problem (SAT). It involves checking whether satisfiability is maintained when new clauses are added to an initial satisfiable set of clauses. Since stochastic local search algorithms have been proved highly efficient for SAT, it is valuable to investigate their application to solve ISAT. Ex...

Two new stochastic search methods are introduced as prototypic examples showing how collective intelligence may emerge in a system of locally interacting units. They share the property of being theoretically understandable and computationally tractable, a quite “rare” feature. The first search method, based on multiresolution search algorithms, can be typically implemented under the form of sea...

It is well known that the performance of a stochastic local search procedure depends upon the setting of its noise parameter, and that the optimal setting varies with the problem distribution. It is therefore desirable to develop general priniciples for tuning the procedures. We present two statistical measures of the local search process that allow one to quickly find the optimal noise setting...

Pareto local search (PLS) methods are local search algorithms for multiobjective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and ne...

Stochastic Pareto local search (SPLS) methods are local search algorithms for multi-objective combinatorial optimization problems that restart local search from points generated using a stochastic process. Examples of such stochastic processes are Brownian motion (or random processes), and the ones resulting from the use of mutation and recombination operators. We propose a path-guided mutation...