The problem of finding a minimum vertex cover (MinVC) in a graph is a well known NP-hard problem with important applications. There has been much interest in developing heuristic algorithms for finding a “good” vertex cover in graphs. In practice, most heuristic algorithms for MinVC are based on the local search method. Previously, local search algorithms for MinVC have focused on solving acade...

This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of traditional distance-estimation heuristics for tasks of this type and present alternative heuristics that are more appropriate for multiple-goal search. In particular, we introduce the marginal-utility heuristic, which ...

We propose a new switching criterion, namely the evenness or unevenness of the distribution of variable weights, and use this criterion to combine intensification and diversification in local search for SAT. We refer to the ways in which state-of-the-art local search algorithms adaptG2WSATP and VW select a variable to flip, as heuristic adaptG 2WSATP and heuristic VW , respectively. To evaluate...

Search is one of the most frequently used problem solving methods in artificial intelligence (AI) [1], and search methods are gaining interest with the increase in activities related to modeling complex systems [2, 3]. Since most practical applications involve objective functions which cannot be expressed in explicit mathematical forms and their derivatives cannot be easily computed, a better c...

A very undesirable behavior of any heuristic algorithm is to be stuck in some specific parts of the search space, in particular in the basins of attraction of the local optima. While there are many wellstudied methods to help the search process escape a basin of attraction, it seems more difficult to prevent it from looping between a limited number of basins of attraction. We introduce a Positi...

In this paper,  a heuristic algorithm is proposed in order to solve a nonlinear lexicography goal programming (NLGP) by using an efficient initial point. Some numerical experiments showed that the search quality by the proposed heuristic in a multiple objectives problem depends on the initial point features, so in the proposed approach the initial point is retrieved by Data Envelopment Analysis...

In combinatorial solution spaces Iterated Local Search turned out to be exceptionally successful. The question arises: is the Iterated Local Search heuristic also able to improve the optimization process in real-valued solution spaces? This paper introduces a hybrid meta-heuristic based on Iterated Local Search and Powell’s optimization method combined with elements from stochastic search in re...

Iterated local search (ILS) is a powerful meta-heuristic algorithm applied to a large variety of combinatorial optimization problems. Contrary to evolutionary algorithms (EAs) ILS focuses only on a single solution during its search. EAs have shown however that there can be a substantial gain in search quality when exploiting the information present in a population of solutions. In this paper we...

We present a model for the surgery admission planning problem, and a meta-heuristic algorithm for solving it. The problem involves assigning operating rooms and dates to a set of elective surgeries, as well as scheduling the surgeries of each day and room. Simultaneously, a schedule is created for each surgeon to avoid double bookings. The presented algorithm uses simple Relocate and Two-Exchan...