This paper presents several ways to compute lower and upper bounds for MaxSAT based on calling a complete SAT solver. Preliminary results indicate that (i) the bounds are of high quality, (ii) the bounds can boost the search of MaxSAT solvers on some benchmarks, and (iii) the upper bounds computed by a Stochastic Local Search procedure (SLS) can be substantially improved when its search is init...

Differential Evolution is a stochastic and meta-heuristic technique that has been proved powerful for solving real-valued optimization problems in high-dimensional spaces. However, Differential Evolution does not guarantee to converge to the global optimum and it is easily to become trapped in a local optimum. In this paper, we aim to enhance Differential Evolution with Random Local Search to i...

MAX-SAT is a well-known optimisation problem that can be seen as a generalisation of the propositional satisfiability problem. In this study, we investigate how the performance of stochastic local search (SLS) algorithms — a large and prominent class of algorithms that includes, for example, Tabu Search, Evolutionary Algorithms and Simulated Annealing — depends on features of the underlying sea...

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New adaptive search methods based on multiresolution analysis and wavelet theory are introduced and discussed within the framework of Markov theory. These stochastic search methods are suited to problems for which good solutions tend to cluster within the search space. Multiresolution search methods are extended to searches with memory. The introduction of a memory allows an easy inclusion of l...

In this paper, two smoothing effects are firstly pointed out by analysis and by experiment on Traveling Salesman Problem(TSP) instances. We design a novel algorithm which runs stochastic local search under the SSS framework. The function determining the accepting probability of uphill moves is designed so that the algorithm can take advantage of the local smoothing effect ignored in original SS...

A major problem in evaluating stochastic local search algorithms for NP-complete problems is the need for a systematic generation of hard test instances having previously known properties of the optimal solutions. On the basis of statistical mechanics results, we propose random generators of hard and satisfiable instances for the 3-satisfiability problem. The design of the hardest problem insta...

Global optimization is an essential component of econometric modeling. Optimization in econometrics is often difficult due to irregular cost functions characterized by multiple local optima. The goal of this paper is to apply a relatively new stochastic global technique, particle swarm optimization, to the well-known but difficult disequilibrium problem. Because of its co-operative nature and b...