MAX-SAT, the optimisation variant of the satisfiability problem in propositional logic, is an important and widely studied combinatorial optimisation problem with applications in AI and other areas of computing science. In this paper, we present a new stochastic local search (SLS) algorithm for MAXSAT that combines Iterated Local Search and Tabu Search, two well-known SLS methods that have been...

It is widely recognized that long-term memory mechanisms such as reintensification and path relinking can significantly improve the performance of ”core” metaheuristics such as tabu search or iterated local search. Case studies abound, e.g., see [2]. These algorithms are commonly designed around a set or pool of elite, high-quality solutions from which intensification and diversification scheme...

Starting from an algorithm recently proposed by Pullan and Hoos, we formulate and analyze iterated local search algorithms for the maximum clique problem. The basic components of such algorithms are a fast neighbourhood search (not based on node evaluation but on completely random selection) and simple, yet very effective, diversification techniques and restart rules. A detailed computational s...

The broadcast scheduling problem (BSP) in packet radio networks is a well-known NP-complete combinatorial optimization problem. The broadcast scheduling avoids packet collisions by allowing only one node transmission in each collision domain of a time division multiple access (TDMA) network. It also improves the transmission utilization by assigning one transmission time slot to one or more nod...

We discuss the use of online learning of the local search neighborhood. Specifically, we consider the Linkage Tree Genetic Algorithm (LTGA), a population-based, stochastic local search algorithm that learns the neighborhood by identifying the problem variables that have a high mutual information in a population of good solutions. The LTGA builds each generation a linkage tree using a hierarchic...

Abstract. The Clustered Vehicle Routing Problem (CluVRP) is a variant of the Capacitated Vehicle Routing Problem in which customers are grouped into clusters. Each cluster has to be visited once, and a vehicle entering a cluster cannot leave it until all customers have been visited. This article presents two alternative hybrid metaheuristic algorithms for the CluVRP. The first algorithm is base...

The Linear Ordering Problem (LOP) is an NP-hard combinatorial optimization problem that arises in a variety of applications and several algorithmic approaches to its solution have been proposed. However, few details are known about the search space characteristics of LOP instances. In this article we develop a detailed study of the LOP search space. The results indicate that, in general, LOP in...

Many real-world applications of AI require both probability and first-order logic to deal with uncertainty and structural complexity. Logical AI has focused mainly on handling complexity, and statistical AI on handling uncertainty. Markov Logic Networks (MLNs) are a powerful representation that combine Markov Networks (MNs) and first-order logic by attaching weights to first-order formulas and ...

This study investigates the coordination of production scheduling and maintenance planning in theflow shop scheduling environment. The problem is considered in a bi-objective form, minimizing themakespan as the production scheduling criterion and minimizing the system unavailability as themaintenance planning criterion. The time interval between consecutive maintenance activities as well as the...

Local search algorithms and iterated local search algorithms are a basic technique. Local search can be a stand along search methods, but it can also be hybridized with evolutionary algorithms. Recently, it has been shown that it is possible to identify improving moves in Hamming neighborhoods for k-bounded pseudo-Boolean optimization problems in constant time. This means that local search does...