The concept of backbone variables in the satisfiability problem has been recently introduced as a problem structure property and shown to influence its complexity. This suggests that the performance of stochastic local search algorithms for satisfiability problems can be improved by using backbone information. The Partial MAX-SAT Problem (PMSAT) is a variant of MAX-SAT which consists of two CNF...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Many real-world problems involving constraints can be regarded as instances of the Max-SAT problem, which is the optimization variant of the classic satisfiability problem. In this paper, we propose a novel probabilistic approach for Max-SAT called ProMS. Our algorithm relies on a stochastic local search strategy using a novel probability distribution function with two strategies for picking va...

Stochastic local search (SLS) algorithms have been successfully applied to hard combinatorial problems from diierent domains. One important feature of SLS algorithms is the fact that their run-time behavior is characterized by a random variable. Consequently, the detailed knowledge of the run-time distribution provides important information for the analysis of SLS algorithms. In this paper we i...

Temperature is the control parameter of Simulated Annealing, one of the best-known local search optimisation algorithms. Scheduling the temperature evolution during optimisation is a crucial component of simulated annealing. We propose to elect acceptance probability as a new control parameter of simulated annealing. The concept of imposing a schedule to acceptance probability throughout optimi...

In this paper the linear ordering problem with cumulative costs is approached. The best known algorithm solution for the problem is the tabu search proposed by Duarte. In this work an experimental study was performed to evaluate the intensification and diversification balance between these phases. The results show that the heuristic construction phase has a major impact on the tabu search algor...

The minimum conductance problem is an NP-hard graph partitioning problem. Apart from the search for bottlenecks in complex networks, the problem is very closely related to the popular area of network community detection. In this paper, we tackle the minimum conductance problem as a pseudo-Boolean optimisation problem and propose a memetic algorithm to solve it. An efficient local search strateg...

A local search algorithm operating on an instance of a Boolean constraint satisfaction problem (in particular, k-SAT) can be viewed as a stochastic process traversing successive adjacent states in an “energy landscape” defined by the problem instance on the n-dimensional Boolean hypercube. We investigate analytically the worst-case topography of such landscapes in the context of satisfiable k-S...

Response Surface Methodology (RSM) searches for the input combination that optimizes the simulation output. RSM treats the simulation model as a black box. Moreover, this paper assumes that simulation requires much computer time. In the first stages of its search, RSM locally fits first-order polynomials. Next, classic RSM uses steepest descent (SD); unfortunately, SD is scale dependent. Theref...

Many methods have been developed for structural size and configuration optimization in which cross-sectional areas are usually assumed to be continuous. In most practical structural engineering design problems, however, the design variables are discrete. This paper proposes two efficient structural optimization methods based on the harmony search (HS) heuristic algorithm that treat both discret...