Random search algorithms are useful for many ill-structured global optimization problems with continuous and/or discrete variables. Typically random search algorithms sacrifice a guarantee of optimality for finding a good solution quickly with convergence results in probability. Random search algorithms include simulated annealing, tabu search, genetic algorithms, evolutionary programming, part...

Ant colony optimization (ACO) is a stochastic search method for solving NP-hard problems. The exploration versus exploitation dilemma rises in ACO search. Reactive max-min ant system algorithm is a recent proposition to automate the exploration and exploitation. It memorizes the search regions in terms of reactive heuristics to be harnessed after restart, which is to avoid the arbitrary explora...

Airspace sectorisation provides a partition of a given airspace into sectors, subject to geometric constraints and workload constraints, so that some cost metric is minimised. We make a study of the constraints that arise in airspace sectorisation. For each constraint, we give an analysis of what algorithms and properties are required under systematic search and stochastic local search.

In this short note, I argue against two commonlyheld biases. The first is that stochastic search is applicable only to improvement search over complete solutions. On the contrary, many problems have effective greedy heuristics for constructing solutions, making a tree-structured search space more appropriate. The second is that stochastic tree search algorithms should explore the same space of ...

Stochastic search algorithms are among the most sucessful approaches for solving hard combinatorial problems. A large class of stochastic search approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As the run-time behavior of LVAs is characterized by random variables, the detailed knowledge of run-time distributions provides important information for the analysis of these a...

This paper describes JAMES, a modern object-oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search...

Hybridization of genetic algorithms with local search approaches can enhance their performance in global optimization. Genetic algorithms, as most population based algorithms, require a considerable number of function evaluations. This may be an important drawback when the functions involved in the problem are computationally expensive as it occurs in most real world problems. Thus, in order to...

In this paper, I analyze the constant factors for implementations of line segment intersection algorithms. I explore how these algorithms could be applied to stochastic local search algorithms to determine the rectilinear crossing number of complete graphs.

Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behav...