A large number of combinatorial optimization problems can presently only be solved to proven optimality if they are of very moderate size. This fact motivates the interest in approximation algorithms and heuristics. The former class of algorithms are able to guarantee the quality of the produced (suboptimal) solutions within certain bounds, while heuristics usually only have empirical evidence ...

The DPLL algorithm, developed by Davis and Putnam [DP60] and by Davis, Logemann, and Loveland [DLL62], is the most popular complete algorithm for the problem of satisfiability of a set of propositional clauses (SAT). While it is outperformed by local search algorithms on satisfiable formulas [SLM92, SKC94], it is still used because local search algorithms can give a definite answer only if the ...

HyFlex (Hyper-heuristic Flexible framework) [15] is a software framework enabling the development of domain independent search heuristics (hyper-heuristics), and testing across multiple problem domains. This framework was used as a base for the first Cross-domain Heuristic Search Challenge, a research competition that attracted significant international attention. In this paper, we present one ...

Many algorithms for scheduling DAGs on multiprocessors have been proposed, but there has been little work done to determine their eeectiveness. Since multi-processor scheduling is an NP-hard problem, no exact tractible algorithm exists, and no baseline is available from which to compare the resulting schedules. Furthermore, performance guarantees have been found for only a few simple DAGs. This...

heuristics have been proposed for scheduling pipelined code, all assume, however, that pipeline constraints can occur after any instruction, and that operators. proaches are generally slower but more flexible than their heuristic couninstruction scheduling, since their dynamic reordering algorithms have limited scope. Optimierungsprobleme, Instruction Scheduling und Offset Assignment, when usin...

Recent domain-determinization techniques have been very successful in many probabilistic planning problems. We claim that traditional heuristic MDP algorithms have been unsuccessful due mostly to the lack of efficient heuristics in structured domains. Previous attempts like mGPT used classical planning heuristics to an all-outcome determinization of MDPs without discount factor ; yet, discounte...

This study offers an adaptive dynamic sine cosine fitness grey wolf optimizer (ADSCFGWO) for optimizing the parameters of two types antennas. The antennas are metamaterial and double T-shape monopoles. ADSCFGWO algorithm is based on technique recently developed powerful optimization techniques: a modified (GWO) value (SCA). suggested approach utilizes capabilities both algorithms to balance bet...

PRODIGY’s planning algorithm uses domain-independent search heuristics. In this paper, we support our that there is no ~ search heuristic that performs more efficiently than others for all problems or in all domains. The paper presents three diIFerent domaln-independent search heuristics of increasing complexity. We run PRODIGY with these heuristics in a series of artificial domms (introduced i...

This paper presents a column generation-based heuristics for the Vehicle Routing and scheduling Problem with Soft Time Windows (VRPSTW). The subproblem has been solved using a modified stochastic push forward insertion heuristics that incorporates the early and late arrival penalties. The useful dual information (shadow prices) from the column generation master problem guides the heuristic subp...

This paper introduces some advanced genetic algorithms for a complex hybrid flexible flow line problem with a makespan objective that was recently formulated. General precedence constraints among jobs are taken into account, as are machine release dates, time lags and sequence dependent set-up times; both anticipatory and non-anticipatory. This combination of constraints captures many real worl...